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The single-channel jet control strategy, with a suction slot on the upper surface and an injection slot near the trailing edge on the lower surface, has the dual benefit of both obviating the additional air requirement and effectively suppressing the reversed flow. Nevertheless, most studies focused on the control device application to airfoils leading to the three-dimensional rotation effect being ignored. In light of this, the objective of this study is to investigate the impacts of the single-channel jet control strategy with suction-injection combination on the rotational blade flow field. Numerical simulations using the Reynolds-Averaged Navier-Stokes (RANS) method accompanied by the k-ω shear stress transport turbulence (SST) model and the multi-reference frame (MRF) method are performed. It is confirmed that the control device can improve the net power output across a wide range of inflow wind speeds, particularly at low tip speed ratios. For instance, when the control strategy is applied to the span from 0.25 R to the blade tip, the net output power can be increased by 45.68 % at a wind speed of 10 m/s for the NREL Phase VI rotor, and simultaneously the axial thrust is only increased by 23.98 %. • Suction-injection combination reshapes the boundary layer of the rotation blade. • Torque of the blade across a wide range of inflow wind speeds is improved. • Tip segment control has greater positive effects than the root and mid-segment. • Net output power can be improved by 45.68 % at 10 m/s for the NREL Phase VI rotor. [ABSTRACT FROM AUTHOR]

Wind power, a promising renewable energy technology, not only offers an alternative to coal-fired power plants but also brings significant economic and environmental benefits. This study employs the hybrid analysis assessment and multi-objective optimization to propose various wind power schemes for Chinese provinces/municipalities under economic and decarbonization priority scenarios. The findings reveal that the plain wind power scheme in Northeast China presents the largest net economic benefit as well as the net emission reduction potential, ranging from 8.33 to 9.10 million CNY/MW and from 28.91 to 30.03 ktCO 2 /MW, respectively. Inner Mongolia is projected to be the leading wind power installer between 2021 and 2030, potentially yielding the net economic benefit of 4.76 billion CNY and the net emission reduction potential of 12.50 million tons per year. For provinces with 0-10 km2 of unexploitable land, such as Shanghai, Beijing, and Henan, the average economic benefit is almost less than 100 million CNY, and the average emission reduction is less than 500,000 tons. This study proposes the geographical multi-objective decision-making of different wind power schemes, which attempts to provide practical implications for the rational deployment of wind power installation within China toward low-carbon transition. • Multi-objective optimization of onshore wind power installation within China is conducted. • Net economic benefits and emission reduction potentials of wind power installation are assessed. • Land use areas, topography, population density, and wind power speed are taken into consideration. • Different wind power schemes by province are proposed through the multi-objective optimization. [ABSTRACT FROM AUTHOR]

This study explores the impact of climate variability on the operation of sustainable ventilation systems in urban infrastructure, with a focus on smoke movement in metro stations under varied external climatic conditions. Smoke characteristics and ventilation strategies in an atrium-type metro station are investigated using numerical simulations. The simulations reveal the influence of external climate on smoke movement through the atrium and roof windows, with particular attention to outdoor temperature and wind velocity. The study designs different ventilation strategies for smoke control that incorporate meteorological data, evaluating smoke visibility, CO distribution, and smoke extraction efficiency. The findings indicate that under severe external climates of low temperatures and high wind velocities, smoke extraction from roof window outlets is challenging. The adverse effects of external climate decrease the natural smoke extraction efficiency, increase the CO concentration, and reduce the safe visibility area. However, improved ventilation measures, such as the platform make-up air system and roof mechanical extraction system, can significantly enhance smoke control performance and facilitate safe evacuation. The study also employs the NSGA-II multi-objective optimisation method to obtain optimal ventilation strategies for different climates, balancing three optimisation objectives of operating energy consumption, safe visibility area, and smoke extraction efficiency. The outcomes show that the recommended values of V p are 20%–30 % (0 °C ≤ T o ≤40 °C) and 40 % (−40 °C ≤ T o <0 °C), v r are 0.3 (10 °C ≤ T o ≤40 °C) and 0.4 (−40 °C ≤ T o < −10 °C), and V c are all lower than 4 %. As wind velocity increases, the recommended values need to be enhanced. This research contributes to sustainable urban development by offering a framework for managing public safety and energy efficiency in the context of climate variability. • Numerical simulations are employed to realise different climatic environments. • The impact of varied climates on smoke movement in metro stations is revealed. • Different ventilation strategies are designed and evaluated for their effectiveness. • The NSGA-II multi-objective optimisation method is used to find optimal solution. • Contributes to sustainable urban development and resilient ventilation systems. [ABSTRACT FROM AUTHOR]

To counter the challenges posed by the unpredictability of wind velocities on wind energy production, a wind speed prediction model combining chaotic mapping-based particle swarm optimization with variational modal decomposition (CPSO-VMD), sample entropy (SE), improved completely integrated empirical modal decomposition with adaptive noise (ICEEMDAN) two-layer decomposition, and attention mechanism-based sequence to sequence (Att-S2S) method is proposed. Firstly, the Lasso method is employed to filter the features that significantly contribute to the wind speed data, fully considering hidden relevant information and eliminating redundant data to improve the prediction accuracy; Secondly, CPSO optimized by introducing chaotic mapping determines the parameter pairs for VMD. This facilitates an adaptive VMD breakdown of the wind speed sequence, aiding in noise removal and selection of the intrinsic mode function (IMF) with the highest sample entropy for further decomposition using ICEEMDAN. In light of this, a sequence-to-sequence method based on the attention mechanism is suggested, which may highlight the influence features' effects on IMF; Finally, the proposed model is implemented at both the Paso Robles wind farm and the Oasis wind farm for practical assessment and benchmarked against other models. Overall, the proposed model outperforms the other models in these trials. • CPSO method is used to tune and select the best VMD parameters to optimize the decomposition of wind speed series. • ICEEMDAN quadratic decomposition of the IMF with the highest sample entropy. • The features with high contribution are filtered as input using the Lasso method. • The Att-S2S method is used to predict wind speed, enhancing the accuracy of the prediction. [ABSTRACT FROM AUTHOR]

When a fuel leakage/energy release (i.e. , combustion, fire) inside a compartment, the external facade flame characteristics through opening are of great significance for protecting human life and property, as well as assessment of its risk and impact to urban environment. This work investigates facade flame depth coming out from fire compartment opening with fuel combustion/energy release inside the compartment for under-ventilated condition under sideward wind, which is a fundamental parameter in describing facade morphological characteristics in particular building fire, meanwhile still no work can be found in previous studies. Results show that, as sideward wind speed increases, the flame depth first increases a bit then decreases significantly. A CFD simulation is carried out to understand flow field controlling facade flame depth under windless and sideward wind situations. A physical analysis is presented on account of mechanisms of complex interaction among sideward wind inertial, outflow momentum through opening, and flame buoyancy flux are proposed based on controlling mechanisms, which well represents the facade flame depth by a non-dimensional function under sideward wind condition. This study makes an important new contribution comparing with previous knowledge, and has potential practical value for fuel combustion inside confined space and the corresponding numerical model verification. [Display omitted] • Experiments on fuel combustion/energy release inside compartment with an opening. • Investigated the flame coming out from the fire compartment opening under the sideward wind. • Quantified facade flame depth for various external sideward wind speeds and energy release rate. • Proposed three characteristic length scales to represent the facade flame depth. • Proposed a physical model of facade flame depth with fuel combustion under sideward wind conditions. [ABSTRACT FROM AUTHOR]

Efficient low wind speed energy harvesting is pivotal in expanding the reach of sustainable wind power to regions with limited wind resources. This paper introduces an innovative approach to enhance wind energy harvesting efficiency in low wind speed environments through the development of a biomimetically inspired design featuring V-shaped attachments on a square bluff body. Drawing inspiration from the swallowtail, these attachments are engineered to manipulate airflow, creating an asymmetrical pressure field that significantly improves the vibration amplitude of the harvesting device. The proposed design is tested in wind tunnel experiments, demonstrating a 32 % reduction in cut-in wind speed and a significant increase in power output at the lowest tested wind speed of 1.7 m/s, compared to traditional designs. Computational fluid dynamics simulations further elucidate the mechanism behind the enhanced energy harvesting, highlighting the role of V-shaped attachments in generating stronger and more stable vortex shedding, crucial for efficient energy harvesting. The proposed attachments not only substantially reduce the cut-in wind speed of galloping but also significantly increase the device's power output, demonstrating the potential of this design for expanding the viability of wind energy in regions with limited wind resources. • Introducing two bionic swallowtail V-shaped attachments boosts output performance. • Compared to traditional designs, it significantly reduces the cut-in wind speed. • The V-shaped attachment's symmetry enhances energy harvesting efficiency. • The harvester exhibits efficient performance in low wind speed environments. [ABSTRACT FROM AUTHOR]

Spatio-temporal Forecasting has been implemented in diverse fields, such as energy, traffic, and weather. As one typical paradigm of intelligent power systems, spatio-temporal wind speed forecasting has attracted increasing attention. Recently, some graph-based methods have revealed the great potential in the spatio-temporal wind speed forecasting task. Those methods are usually built on a static graph which is incapable of discovering the dynamic characteristics and the uncertainty of temporal wind patterns. To this end, we propose a novel D ynamic S tochastic G raph N etwork (DSGN) for spatio-temporal wind speed forecasting. Specifically, we characterize each wind turbine of regional power grids with a dynamic stochastic distribution to incorporate the uncertainty into networks. Then Wasserstein distance is adopted to build the stochastic graph, which can effectively represent the spatial dependencies between wind turbines. Additionally, we devise a reconstruction task to preserve the geographical distribution information and regularize the learning of the stochastic graph. To further exploit global spatial dependencies and enhance graph modeling, we augment the stochastic graph view by diffusion operations and integrate a self-supervised learning framework, serving as an auxiliary task, to maximize mutual information between the wind latent states from different graph views. Empirical experiments on four real-world wind speed datasets demonstrate the superiority of DSGN over some state-of-the-art methods. Some ablation studies further validate the effectiveness of the stochastic graph modeling and the self-supervised framework. • A novel dynamic stochastic graph network for spatio-temporal wind speed forecasting. • Characterizing the dependencies between wind turbines in the Wasserstein space. • Exploiting the potential of self-supervised learning for the forecasting task. [ABSTRACT FROM AUTHOR]

To establish Weibull distribution patterns for outdoor wind speed (WS) over an extended time period and determine wind conditions for building energy-efficient design, this study collected daily WS data from 381 cities spanning all thermal design zones in China. Weibull distribution shape and scale parameters were estimated using three methods: Maximum Likelihood Estimation (MLE), Graphical Method (GM), and Method of Moments (MM). A comprehensive goodness-of-fit assessment of these methods revealed that GM and MLM exhibited superior performance, making them suitable for determining Weibull parameters in the summer, winter, and the entire year. Through a rigorous examination and evaluation of the shape and scale parameters within each subzone across the summer, winter, and the entire year periods, the study identified the Rayleigh distribution as the typical pattern for low WS in building energy-efficient design. The determined Weibull distribution patterns can serve as fundamental information for wind inlet in assessing climate potential, WS in Outdoor Climate Design Conditions, and wind information in Typical Meteorological Year. • A comparative analysis was conducted using daily wind speed data from 381 stations using three estimation methods. • Weibull parameter ranges for the summer, winter, and annual were determined and analysed across all subzones. • A consistent Rayleigh distribution pattern was found in all seasons. • The distribution patterns can be used to determine wind conditions in building energy-efficient design. [ABSTRACT FROM AUTHOR]

Improving wind speed prediction is essential for increasing the use of wind energy and promoting sustainable utilization of resources. Most previous studies relied on single-valued data with a common frequency, resulting in limited wind energy utilization. To fill this research gap and strengthen the precision of wind speed prediction, this study proposes a new multi-objective ensemble forecasting system based on a mixed-frequency interval-valued modeling paradigm for power system decision-making. The system comprises three modules: interval-valued data preprocessing module, mixed-frequency interval-valued forecasting module, and multi-objective ensemble module. Firstly, to reduce the data complexity by preprocessing the mixed-frequency interval-valued wind speed data, an interval-valued data preprocessing module is designed. Then, different types of prediction models, including a mixed data sampling model, two artificial intelligence models, and a traditional statistical model, are used to predict each subsequence in the mixed-frequency interval-valued forecasting module. Finally, a multi-objective ensemble module is designed to ensemble the interval-valued prediction results of each sub-model, and the final interval-valued wind speed forecast results are obtained. The proposed system obtained IMAE values of 0.4634 and 0.3452 at sites from Inner Mongolia and Penglai, China, respectively; this provides a significant tool for wind speed prediction. • Mixed-frequency interval-valued wind speed modeling paradigm is proposed. • A new multi-objective ensemble wind speed forecasting system is developed. • Mixed-frequency interval-valued forecasting module is designed using four models. • A new ensemble strategy is designed for interval-valued wind speed forecasting. [ABSTRACT FROM AUTHOR]

The large-scale variable renewable energy (VRE) forces hydropower to smooth out more frequent and violent load/supply fluctuations in cascade hydropower-VRE hybrid systems (CHVHS), threatening the hydropower units safety. This paper proposes a short-term optimization operation model, considering the vibration in both the stable operation and regulation process, which minimizes the whole-process vibration for a CHVHS. Particularly, the traversing depth (TD) is defined for the first time and combined with the traversing time to quantify the cost of traversing vibration zone (VZ), and a complete vibration avoidance strategy applicable to multiple VZs of multiple units is proposed. Finally, the cascade hydropower stations in Hongshui River Basin are selected as a case study. The validity and superiority of the model are verified by comparing with two conventional operation models from the hydropower station and unit perspectives under different load curves, hydrology, wind speed and solar intensity scenarios. The proposed model enables the hydropower unit not to operate in the VZ, and its TD is reduced by 54.28 % and 55.28 % compared to the two conventional models respectively. The approach provides a safe and stable operation pattern that considers whole-process vibration for a CHVHS, mitigating exacerbated vibration from hydropower units compensating for VRE uncertainty. • Traversing depth is defined for the first time to quantify traversing vibration zone cost. • A generic vibration avoidance strategy is proposed considering the vibration cost. • A short-term optimization operation model is constructed for hybrid systems. • The proposed model enables the hydropower unit not to operate in the vibration zone. • Its traversing depth is reduced by 54.28 % and 55.28 % compared to the two previous models. [ABSTRACT FROM AUTHOR]

Obtaining wind speed and direction predictions with high accuracy is of vital significance for the utilization of wind energy. This research focuses on three problems: how to realize multiple station prediction, multi-step ahead prediction and wind vector prediction. Firstly, a 4-D time series variable scene is proposed for multi-step ahead forecasting of multiple station wind vector. Then, a wind vector prediction model (CM-G) based on Convolutional Minimum Gate Memory Neural Network and Graph Convolution Neural Network is constructed to improve the prediction accuracy. Further, three multi-step ahead forecasting modes are compared and their advantages, disadvantages and application scenarios are analyzed. Finally, the model and method proposed in this study are applied to two datasets in Tibet, China. The experimental results and conclusions are as follows: (1) The prediction accuracy of CM-G proposed in this study is higher than that of the existing state-of-the-art model in 90 % of the metrics. (2) From single-step to multi-step ahead forecasting, the prediction accuracy of wind vector gradually decreases. (3) In wind vector prediction, the prediction accuracy of wind speed is higher than that of wind direction. (4) The direction of accuracy improvement for multiple stations is relatively consistent with the average wind direction. • A 4-D time series variable scene is proposed for multi-step ahead forecasting of multiple station wind vector. • A wind vector prediction model based on deep learning is constructed to improve the prediction accuracy. • Three multi-step ahead forecasting modes are compared. [ABSTRACT FROM AUTHOR]

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method. • Propose a new input mode for NWP wind speed correction algorithm. • Propose a method for decomposition and reconstruction of wind speed error sequence. • Propose a new mode of searching high frequency components. • Propose a new clustering index of fluctuation components. [ABSTRACT FROM AUTHOR]

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the N RMSE by 3.82 % and N MAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system. • Propose a method to extract the most relevant trend component form wind speed. • Propose a novel method for wind speed correction and power calibration. • Propose a sample condition generation method based on GAF and CVA. • Propose an improved clustering distance into the wind speed correction process. [ABSTRACT FROM AUTHOR]

Offshore wind energy is booming and floating offshore wind has been proved to be a promising clean energy. Wake effect is one of the biggest challenges in large-scale floating wind farm which results in power loss and increased fatigue load. In this paper, actuator line model (ALM) coupled with unsteady Reynolds-averaged Navier-Stokes (URANS) is adopted to investigate the wake characteristics and evolution of floating wind turbine under motion. Besides, modal analysis is adopted to extract typical features of turbine wake. Velocity fluctuation is the prominent characteristic of floating turbine wake. The increase of surge frequency results in the decrease of velocity fluctuating region and large surge amplitude leads to bigger velocity deficit fluctuations. In addition, the surge motion brings a faster recovery rate than fixed wind turbine. The induced velocity field generated by vortex ring structure is the key to understanding complex evolution of turbine wake and the vortex ring structure is analyzed in this paper. With the deep understanding of FOWT wake, an innovative vortex ring structure model is proposed in this paper. This work is the basic work of developing the engineering wake model of floating wind turbine, to cope with the wake effect challenge in the large-scale floating wind farm. • Wake characteristics and vortex structure is analyzed using actuator line method. • Wake evolution mechanism is clearly revealed through modal analysis. • Instability theory of helical vortices is introduced into the study of turbine wake. • A semi-empirical vortex structure model is proposed based on theoretical analysis. [ABSTRACT FROM AUTHOR]

Accurate wind forecasts for one day ahead or longer periods have significant impacts on the safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) serves as the essential tool, such as ensemble NWP integrating multiple single simulations. Typically, ensembles include all single members with fixed weights; however, the relative accuracy of each member may change over time. This study introduces an attractive idea: improving ensemble performance by dynamically recognizing and avoiding low-performing members. It proposes a dynamic ensemble strategy based on NWP, reinforcement learning and error sequence correction. The process begins with Weather Research and Forecasting ensemble simulations. A dynamic framework is then constructed by mapping the multi-step ensemble problem into a Markov decision process, which is further solved using deep deterministic policy gradient. Subsequently, a hybrid deep learning model, comprising temporal convolutional network and bidirectional long short-term memory, is constructed for error sequence estimation of dynamic ensemble, using the high-frequency information of NWP as input. Conducting experiments at two wind farms, and focusing on the 24-h wind speed forecast with a 15-min time resolution, the proposed system demonstrates a reliable and stable ensemble throughout the entire forecasting horizon, significantly reducing the probability of large forecasting errors. • Cross-study approach integrating deep learning to reduce NWP uncertainty errors. • New exploration of reinforcement learning into multi-step forecast of NWP ensemble. • Effective correction of multi-step error sequence through hybrid deep learning. • Adaptive ensemble dynamically avoids bad-performing members by feature variations. • Reliable forecasting tool notably reduces the probability of large error cases. [ABSTRACT FROM AUTHOR]

Efficient estimation of the uncertainty associated with wind speed forecast is crucial for evaluating wind farms' power quality and operation. Typically, the performance of prediction interval (PI) generating models; is restricted in terms of computational efficiency inheritably from the sequential data processing; whereas restrictions in forecast quality are derived from the PI generation techniques. This paper presents an efficient Gated Multi-Scale Convolutional Sequence Model (GMSCSM) to forecast wind speed PIs. GMSCSM while conserving the 'recurrence' of LSTMs also offers 'parallel input' advantage of CNNs for better computational efficiency which is highly desirable for short-term forecasting models. In addition, capturing features at various scales in the sequence, GMSCSM learns both local details and global context which is vital for multi-horizon forecasts. The model generates quality PIs utilizing an improved quality-driven loss which we proposed by invoking calibration assessment in its existing definition. Forecasts generated for eight different datasets obtained from two different wind farms show an improvement of 30 % and 6 % in the average coverage width criterion index while reducing the model training time to nearly half and one third of the respective values obtained from traditional and LSTM based models which showcases the proposed model's excellent prediction capability and efficiency. • An efficient wind speed interval prediction tool is developed which could prove helpful in tackling the wind power development problems. • Utilizing the parallel input advantage of CNNs and recurrence properties of RNNs, an efficient deep learning framework for wind speed interval prediction is proposed. • Combining different window sizes-based convolutions the study develops a smart and efficient multi-scale feature extraction mechanism to capture both local and long-term dependencies in the data. • Adding a calibration function, the study proposed an improved calibrated quality driven loss function (QD imp) which mitigates the threshold coverage problem specially under multi-horizon forecasting. [ABSTRACT FROM AUTHOR]

The efficiency of a single piezoelectric energy harvester (PEH) is inadequate to meet the energy requirements of equipment, requiring the simultaneous operation of multiple PEHs. Consequently, this work conducts research on multi-piezoelectric energy harvesters (MPEH) array based on wind-induced vibration. Analyze the output characteristics of array configurations using simulation and experimental methods, including dual parallel array (PEH-2p), dual series array (PEH-2s), four-square array (PEH-4), and nine-square array (PEH-9) energy harvesters. The results indicate that the spacing ratio (L/D) is the primary factor influencing the output performance of the PEH array. Both of the starting and peak wind speeds of PEH-2p were below the reference value, but the power peak has increased. The starting wind speed of PEH-2s has been reduced, with a more significant improvement in the output performance of the downstream energy harvester compared to the upstream energy harvester. The output power of the downstream energy harvester has increased by 323.35 % in comparison to the reference value. Additionally, the PEH-4 demonstrates a boosting effect on the output power of both the upstream and downstream energy harvesters. The energy harvester in the center of the PEH-9 has the highest power peak, being 349.44 % higher than the reference value. • Multi-piezoelectric energy harvesters array were studied. • Comparative analysis through simulation and experiment. • The spacing ratio has a great influence on the of the energy harvester array. • The power peak of nine-square array is 349.44 % higher than the reference value. [ABSTRACT FROM AUTHOR]

As the global demand for sustainable energy continues to grow, accurate wind speed prediction becomes crucial for optimizing the allocation of wind energy resources and enhancing the efficiency of wind power generation. This paper presents a composite prediction model that integrates Optimized Decomposition, Multi-Objective Feature Selection (MOFS), Patch-Based Recurrent Neural Networks (RNNs), and Cross-Attention Mechanisms. Initially, the wind speed time series is decomposed using RIME-optimized Variational Mode Decomposition (VMD), and sample entropy of each component is calculated to reconstruct high, medium, and low-frequency components. Subsequently, these components are patched to serve as inputs to the RNN layers. The third step involves the incorporation of exogenous variables, where MOFS is employed for feature selection, followed by a decomposition and reconstruction through RIME-optimized VMD. This process applies Variate-wise Cross-Attention Mechanism on the high, medium, and low-frequency components of both endogenous and exogenous variables. Finally, the outputs from all processing steps are integrated through a normalization layer and a feed-forward layer to produce the final prediction results. Experimental results demonstrate that our proposed model, through meticulous preprocessing and advanced architectural design, significantly improves the accuracy of wind speed predictions. • Novel DMPR model integrates optimized decomposition, multi-objective feature selection, and patch-based RNN for wind speed forecasting. • Innovative multi-objective feature selection enhances exogenous variable integration. • Patch-based RNN efficiently models temporal dependencies. • DMPR outperforms state-of-the-art models in multi-metric wind speed prediction evaluations. [ABSTRACT FROM AUTHOR]

Wind resource assessments are crucial for pre-construction planning of wind farms, especially offshore. This study proposes a novel hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Wavelet Transform (EWT) for enhanced wind speed forecasting. This secondary decomposition reduces forecasting complexity by processing high-frequency signals. A Bidirectional Long Short-Term Memory (BiLSTM) neural network optimized with the Grey Wolf Optimizer (GWO) is then employed for forecasting. The model's accuracy is evaluated using simulated wind speeds along the coast of Denmark, combined with lidar measurements through data fusion. This approach demonstrates significant improvements in prediction accuracy, highlighting its potential for offshore wind resource assessment. [Display omitted] • A novel algorithm is proposed to predict offshore wind speed using continuous simulation data. • A secondary signal decomposition algorithm is used to pre-process a time series and further reduce the complexity of the signal. • A Gaussian regression is constructed for the fusion of data with multiple fidelities where high-fidelity data is considered as a function of low fidelity data. • The data fusion method using gappy empirical measurement results in more accurate wind resource assessment with 40% improvement, significantly outperforming models that are trained only on a single source of data. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting (WSF) plays a pivotal role in anticipating the power output of wind farms. Nevertheless, the stochastic and variable nature of wind speed presents a significant challenge in achieving accurate WSF. Hence, this study proposes a novel 4D high-resolution system, Windformer, for wind speed vector forecasting (WSVF). Windformer combines the ability of convolutional neural networks (CNNs) for feature extraction and transformers based on attention mechanisms for information fusion. In the Windformer, the input and output layers are mainly composed of 3D CNNs. The input layer is employed for feature extraction and information compression, while the output layer is tasked with recovering the WSV field. The key components of the model consist of encoders and decoders built upon temporal shifted window multi-head self-attention. This architecture is capable of effectively integrating spatio-temporal information. Trained on 39 years of regional reanalysis data, Windformer obtains the most accurate forecast results compared to 5 WSF baseline models. Most importantly, within the next 6 hours, Windformer demonstrates higher accuracy compared to the High-Resolution Deterministic Forecast (HRES) from the European Centre for Medium-Range Weather Forecasts (ECMWF). • Windformer enables high-resolution regional-level wind speed vector predictions. • Windformer surpasses baseline models in forecasting wind speeds for the upcoming 1 to 48 h. • Training Windformer only requires two RTX4090 GPUs. • Windformer shows an MAE about 0.05–0.2 m/s lower than HRES within the next 6-hour wind speed forecast. • Windformer efficiently handles spatio-temporal information using temporal shifted window multi-head self-attention. [ABSTRACT FROM AUTHOR]

In aerodynamic studies, endplates or wingtip devices play a crucial role in enhancing the performance of airfoil blades and minimizing losses. This is particularly important for wind turbines, especially the drag-type Savonius turbine. This paper aims to investigate the effects of different endplate designs. Both experimental works and numerical simulations were conducted on a Savonius rotor with an aspect ratio of 1. A total of eight different endplate geometries and sizes were investigated. The mechanical torque and rotational speed were measured experimentally under various loads with an incoming wind speed of 5.89 m/s. The coefficient of torque (C T) and coefficient of power (C P) were calculated in both the experiments and simulations. The simulation was first validated using data from the literature, and flow characteristics were then scrutinized. The results indicate that the endplates greatly impact the turbine performance, with improvements of up to 299.7 % compared to a turbine without endplates from the experiment. The endplates help prevent flow leakage near the blade edges. In addition to tip losses reducing turbine performance, aerodynamic parasitic drag also contributed to a decrease in C P for the full endplate. The relationship between the aerodynamic parasitic drag with TSR is also reported. By implementing a well-designed endplate, these adverse effects can be mitigated. [Display omitted] • Comparison of eight different endplate designs. • Experiment and CFD simulation of Savonius rotor performance. • Tip losses and parasitic drag affect the performance significantly. • Flow characteristics and pressure distribution are scrutinized. • Full circular endplate outstands other endplate geometries. [ABSTRACT FROM AUTHOR]

The problem of ash deposition on the surface of photovoltaic (PV) arrays during actual operation seriously affects their power generation efficiency. Because traditional research does not consider the PV module spacing, it cannot reflect the actual PV array ash deposition problem. This study explored the influence of PV module spacing, combined with different tilt angles and inlet wind speeds, on the characteristics of ash deposition and power output of the PV array through indoor experiments. The study designed a set of experimental equipment for simulating the formation process of ash deposition on the surface of PV arrays and set up an experimental platform to measure the influence of ash deposition on the maximum output power (P max), open-circuit voltage (U oc), and short-circuit current (I sc) of the PV modules. The findings indicate that as the tilt angle increases, the PV array experiences reduced ash deposition and increased output power. Conversely, higher inlet wind speeds lead to greater ash deposition and decreased output power. Moreover, widening the spacing between PV modules results in increased ash deposition and reduced output power. Notably, within the PV array, the front PV module accumulates more ash than the rear module, with larger particle sizes. These research findings have significant implications for optimizing PV array design and enhancing power generation efficiency. • Investigated the impact of PV module spacing, tilt angles, and wind speeds on ash deposition and power output through indoor experiments. • Designed experimental equipment to simulate ash deposition on PV array surfaces. • Designed a platform to measure how ash deposition affects PV array output. [ABSTRACT FROM AUTHOR]

Accurate wind speed prediction plays an important role in scheduling optimization of wind power generation systems. In complex scenarios, wind speed features are difficult to be captured effectively, leading to unsatisfactory prediction results. To overcome this problem, a trend and residual network (TRNet) based on a novel hilly attention mechanism is proposed for wind speed forecasting, which consists of a trend component (HAttn-AR), a residual component (CHAttn) and an entangle component. Specifically, HAttn-AR composed of hilly attention layers and a recursive layer is constructed, which can separately extract parallel and inference features. In the CHAttn, a cross hilly attention layer is built to capture dynamic residual features. Considering the complexity of trend and residual features, an entangle component is constructed to integrate these features. To validate the performance of TRNet, two offshore wind speed data sets from farms in China and Norway have been used. The results show that TRNet is superior to other existing advanced models. In particular, in the China data, the MAE value of TRNet is improved to 5.26 %–77.32 %, the MSE value is reduced to 7.14 %–91.13 %, and the SMPAE value is optimized to 12.50 %–74.07 %. https://github.com/linsonM/TRNet. • TRNet is proposed to improve the performance for complex wind speed prediction. • A novel hilly attention is presented to improve the ability to capture features. • Entangle component is built to integrate the trend features and residual features. • TRNet offers outstanding performance compared to nine other advanced models. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines suffer severe aerodynamics under platform motions. Novel control methods are urgently needed to improve power and load performances. A mechanical cyclic pitch control strategy is introduced in this paper, and the load reduction effect on the NREL 5-MW wind turbine is investigated under dominant pitch motions. The simulation is performed by the standalone Aerodyn_Driver code. Results show that the relative airflow under pitch motions can be equated to a linear sheared flow and is only related to wind velocity and pitch angular velocity. Cyclic pitch control hardly affects power and thrust but significantly changes aerodynamic moments. The resultant moment and moment axis azimuth are defined, and they are controlled respectively by pitch amplitude and phase. The optimal pitch parameter equations to minimize pitching and yawing moments are derived from fitting and derivation, and the standard deviations of aerodynamic moments are reduced by over 70 % under the sinusoidal pitch motion and steady inflow below rated operation. This paper provides a new idea for the individual pitch control structure, and the mechanism study for aerodynamic control is important and instructive to fill the technological gap in mature pitch control strategies for floating offshore wind turbines. • Cyclic pitch control is used to reduce aerodynamic loads under pitch motions. • The linear sheared airflow is only related to wind and pitch angular velocity. • Power and thrust are almost unaffected in the limited pitch angle range. • Load magnitude and direction are adjusted respectively by pitch amplitude and phase. • Standard deviations of moments are reduced by over 70 % with the optimal control. [ABSTRACT FROM AUTHOR]

Wind-induced vibration piezoelectric energy harvesters have garnered significant interest in recent years as a means to power autonomous wireless sensor systems. A magnetic transfer piezoelectric wind energy harvester (MT-PWEH) is proposed and its durability, power generation performance and environmental adaptability are improved utilizing dual mode conversion. This MT-PWEH incorporated a downstream rectangular baffle, which facilitated the transition of the hollow cylinder from a traditional single vortex-induced vibration to a coupled vibration involving both vortex-induced vibration and galloping (i.e., the first vibration mode conversion). Besides, the vibration direction of the hollow cylinder was perpendicular to the vibration direction of the transducer, which achieved the purpose of limiting the amplitude (i.e., the second vibration mode conversion). The feasibility of MT-PWEH was confirmed through theoretical analysis, CFD simulation, fabrication and experiments. The experimental results demonstrated that the working characteristics of MT-PWEH were significantly affected by position and size of the baffle. Specifically, the ratio of the maximum cut-in wind speed of 12.5 m/s to the minimum cut-in wind speed of 1.2 m/s reached 10.4. Additionally, a maximum power output of 0.78 mW was recorded at 10 m/s with an optimal load resistance of 800 kΩ and 10 LEDs were drove successfully. • A magnetic transfer piezoelectric wind energy harvester (MT-PWEH) is presented. • The MT-PWEH can achieve galloping coupled with vortex-induced vibration. • The MT-PWEH can achieve vibration direction conversion. • The voltage and cut-in wind speed can be tuned by varying leeward rectangular baffle. • The energy harvester exhibits excellent durability and environmental adaptability. [ABSTRACT FROM AUTHOR]

Floating vertical axis wind turbines are bearing complex aerodynamic fatigue loads due to the movement of the platform and the reciprocating variations in the angle of attack and the blade relative wind speed. To explore the effect of the surge motion of the platform and verify the performance and acting mechanism of adaptive pitch control on reducing the fatigue loads and improving the power coefficient, a floating vertical axis wind turbine with three motion forms (non-surge, pure surge, and surge-adaptive pitch) is investigated in the condition of a low tip speed ratio with a high incoming wind speed. The results show that the surge motion enhances the fatigue loads, with the power coefficient less affected. The adaptive pitch control reduces the enhanced fatigue loads and improves the power coefficient by nearly twice. Vorticity and pressure distributions verify that the fluctuation ranges of the angle of attack and the blade relative wind speed are expanded by the surge motion, and the angle of attack is reduced by the adaptive pitch control especially in the upwind section, with the flow separation mitigated. However, the effects of the surge motion and the pitch variation are also influenced by the shedding vortex and the flow separation reattachment. For the upwind part of the blade aerodynamic forces, with the surge amplitude increase or the surge period decrease, the global amplitudes are increased gradually by the surge motion and then reduced by the pitch control, with the load reduction performance enhanced gradually. • A floating vertical axis wind turbine with a surge motion and pitch variation was investigated. • The performance and mechanism of the adaptive pitch control was analyzed. • The effects of the surge motion and the pitch variation associate with the shedding vortex and the flow separation. • With surge amplitude increase or surge period decrease, the performance of the pitch control was enhanced. [ABSTRACT FROM AUTHOR]

Various models for wind speed mapping have been developed, with increasing attention on models focusing on mapping wind speed distribution. This study extends these models to predict hourly wind speed time series at unsampled locations. A model based on the quantile mapping (QM) procedure was compared to a traditional and machine-learning model to interpolate wind speed spatially. These proposed models were also used with inputs from the ERA5 reanalysis dataset, enabling them to consider local variation in orography and large-scale wind fields. A widely used procedure for mean bias correction of reanalysis based on the Global Wind Atlas (GWA) was implemented and compared to the proposed models. It was found that the QM and machine learning model, both using input from ERA5, significantly outperformed GWA bias correction in terms of time series correlation and probability distribution. Despite being more computationally intensive than GWA bias correction, both models are recommended due to their significantly (in a statistical sense) superior performance. • Various methods for the prediction of hourly wind speed time series at unsampled locations were evaluated. • The complex approaches were more effective than the baseline model. • No single method excelled across all the evaluation criteria. • Two methods seemed promising for downscaling ERA5 wind speed data. [ABSTRACT FROM AUTHOR]

This study utilizes improved delayed detached-eddy simulation (IDDES) to investigate the impact of extreme wind conditions on a horizontal-axis wind turbine, as specified by the IEC-61400 standard. The employed IDDES has a hybrid formulation, where SST K- ω is used to model the turbulence in the boundary layers and large-eddy simulation (LES) is used to resolve turbulence in separated flow regions and the wake area. It also allows for switching to a wall-modeled LES approach, depending on the incoming turbulence content. The research focuses on extreme operating gust (EOG), extreme horizontal shear (EHS), and extreme vertical shear (EVS) conditions at two Tip Speed Ratios (TSR). The loadings and wake characteristics are compared to uniform inlet velocity results to highlight the effects of extreme winds. The novelty of this research lies in its comprehensive investigation of wake behavior under extreme wind conditions, providing valuable insights for improving wind farm layout design and reliability in challenging operational environments. The analysis shows that the impact of the TSR on wind turbine loadings during EOG, EVS, and EHS events is significant. During EOG events for a turbine operating at low TSR, the thrust force and bending moment reach 2.38 and 2.57 times their respective values in uniform inflow conditions, while at the optimal TSR, the thrust force and bending moment increase by only 25%. In all extreme wind cases, there is an escalation in turbulence within the wake, and wake meandering is observed in the EHS and EVS cases. • Oscillating bending moment during extreme vertical shear event. • Moment creation about the vertical direction during the extreme horizontal shear event. • Wake meandering during extreme vertical and horizontal shear events. • Turbulence persistence in the far wake during extreme events. • Effect of Tip Speed Ratio on the performance of the turbine rotor during the extreme wind condition. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting is of great significance for the utilization of Arctic wind energy resources. The traditional single model is difficult to fully depict the nonlinearity of wind speed and its wide range of variations. In this paper, a hybrid model is proposed for multi-step wind speed forecasting, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural network (CNN) and long-short term memory neural network (LSTM). The wind speed series is firstly decomposed into several intrinsic mode functions (IMF) by CEEMDAN to provide more stable data. Secondly, four-step-ahead forecasts are realized using well-tuned CNN-LSTM model for each IMF. Finally, the forecasted wind speed is obtained by reconstruction. The effectiveness and feasibility of the proposed method is validated based on thorough evaluation and step-by-step analysis. The RMSE of the proposed model is 0.4046 m/s, which are reduced by 58 % compared with 7 benchmark models. Furthermore, the average prediction interval of the proposed model is also reduced by 20 %, 16 % and 7 % compared to CEEMDAN-FCNN, CEEMDAN-CNN and CEEMDAN-LSTM respectively. The results prove that all three parts of the proposed model contribute to a better performance in wind speed forecasting. [Display omitted] • CEEMDAN can reduce the nonlinearity of wind speed data and unstable IMFs have a large impact on the wind speed forecasting. • Combining LSTM with CNN can enhance feature extraction and improve the accuracy of multi-step wind speed forecasting. • The proposed model achieves more accurate forecasting for catastrophe points which provides more critical information. [ABSTRACT FROM AUTHOR]

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities. • Optimization of power distribution network with hydrogen systems. • Analysis impacts of hydrogen system to mitigate natural disasters' impacts. • Optimal day-ahead scheduling under uncertainty in photovoltaics and disasters. • Risk evaluation using effective risk tool to reach trade-off between risk and cost. [ABSTRACT FROM AUTHOR]

In last decades because of the increase in water demand various technologies used to increase the performance of desalination systems and decreasing the cost of produced water. In the present research, the thermal and economic performance of a parabolic trough collector connected to a desalination system was analyzed. Moreover, in order to improve the thermal efficiency of the collector, the effect of wind speed (considering the position of the collector) on the deflection of the collector and thermal performance was evaluated. By increasing the wind speed the thermal efficiency decreases. The twist angle at the end of the reflecting shaft was considered for calculations. Ansys analysis was used to survey the structural behavior of the collector at various conditions. The results of the experimental test demonstrated that for all three positions, the twist angle of the shaft increases as well Moreover, due to the fluctuation of the solar radiation the fluid temperature flows in the collector is not homogenous. Using materials with higher latent heat decreases the thermal fluctuations. In the present work PCM in the spiral tube was installed inside of the receiver tube. The results implied that the maximum twist angle was obtained for the wind speed of 30 km/h at the right position. The deviation from the focal line increased the heat loss, thus optimization process was applied to the system considering the maximum wind speed of 124 km/h. The produced water for 15, 22 and 30 km/h is 722, 557 and 351 ml/m2 in 4 h, respectively. The economic analysis implied that using proposed system would cause a decrease in the payback time. • Effect of the wind force on the deflection of parabolic solar collector chassis was investigated. • The collector chassis simulated using 3D geometry and applying wind force. • The thermal performance and the amount of produced water was evaluated. • The data obtained by simulated model validated by laboratory data. [ABSTRACT FROM AUTHOR]

This study investigated the burning rate and the flame geometry of gasoline pool fires under crosswinds in the context of bridge vehicle fires. A 1:4 scale bridge fire platform was established. A total of 45 fire experiments were conducted on 9 gasoline pool with 1–2.2 aspect ratios under 0–2 m/s cross-wind. The influence mechanism of aspect ratio n on the characteristic parameters of pool fires was investigated. The results showed that the burning rate of gasoline pool ranges from 0.028 kg‧m−2‧s−1 to 0.064 kg‧m−2‧s−1, which increases with wind speed and pool size. When the wind speed exceeds 1 m/s, the title angle of the flame on the windward side is greater than 45° and is less affected by pool size. The wall heat conduction at wind downstream increases with aspect ratio when placing the longer side facing to cross flow. The correction factor F r · n can be used to describe the competition between the air entrainment and the buoyancy in gasoline pool fire. A general model based on aspect ratio is proposed to quantify the burning rate and flame tilt characteristics of gasoline fires. The reliability and applicability of the model are compared with the existing experimental data. • The coupling effect of cross-wind and aspect ratio on gasoline combustion was investigated. • The aspect ratio was introduced to propose a general model of flame geometry. • A 1:4 scale bridge fire platform simulating vehicle fire was established. [ABSTRACT FROM AUTHOR]

Wind energy forecasting is significantly affected by the strong volatility, intermittency and variability of the wind speed sequence itself. Therefore, to improve forecast accuracy and stability, based on nonlinear auto-regressive model with exogenous inputs (NARX) and the hybrid chaos-cloud salp swarm algorithm (CC-SSA), a short-term wind speed prediction method is proposed. Firstly, to reduce the complexity of the original wind speed data and generate subcomponents with different patterns and low complexity, a mixed modal decomposition method is carried out by combining variational modal decomposition (VMD) based on Pearson correlation coefficient and generalized S-transform (GST) based on adaptive sample entropy. Therefore, the complementary advantages of different mode decompositions are obtained by combining the two different subcomponents into a mixed component. Secondly, by using cloud model and chaotic map, the improved CC-SSA algorithm is proposed to improve the convergence performance of salp swarm algorithm (SSA). Finally, by using CC-SSA algorithm to optimize the weights of NARX, the CC-SSA-NARX forecasting model is established to predict wind speed. The experimental results show that for the 1-Step, 2-Step, and 3-Step datasets of the actual wind speed in the studied region, the MAE, MAPE, RMSE, and R 2 of the CC-SSA-NARX model are 0.23, 6 %, 0.27, and 0.97 respectively. The proposed model present the highest prediction index among the other 7 comparative models, showing high accuracy and generalization ability in short-term wind speed prediction, it can provide a certain reference for enhancing the stability of wind power generation and promoting the sustainable development of the wind power industry to some extent. • A mixed decomposition method using VMD and generalized S-transform is proposed. • An improved SSA algorithm based on chaotic cloud (CC-SSA) is proposed. • The proposed CC-SSA is applied to optimize the parameter of NARX. • A short-term wind speed prediction model of CC-SSA-NARX is constructed. [ABSTRACT FROM AUTHOR]

Accurate and reliable wind forecasts for urban blocks play a pivotal role in the construction of zero-energy communities by guiding the selection and placement of wind turbines and the aerodynamic design optimization of ducted openings. While relatively accurate wind fields are available based on numerical methods, their heavy computational cost and discontinuity make it necessary to explore an interactive and end-to-end method. In this study, we develop a physics-inspired and data-driven two-stage deep learning approach that can reconstruct complex wind fields precisely. The proposed method integrates a physical feature extraction model of the flow field with a sparse measurement data-driven error correction approach. In particular, a well-designed and well-trained flow field feature extraction model (original model) can preserve salient features of CFD modelling, while data-driven error correction techniques may harvest the uncertainty features and fill the remaining gaps between the original model predictions and the measured data. The proposed method is verified by a measured dataset from a community in Beijing. Experimental validation illustrates that the proposed algorithm successfully accomplishes wind field reconstruction in complex terrains using sparse datasets. We show that the proposed two-stage strategy exhibits significantly improved prediction results over the purely original method, with an average accuracy improvement of 47.17% and a maximum accuracy improvement of 72.59%. Overall, the proposed method delivers the potential in accurate wind field construction and urban wind energy forecasting. [Display omitted] • A physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction is proposed. • A well-designed and well-trained flow field feature extraction model is developed. • An error correction method is proposed by considering the uncertainties present in real conditions. • Effectiveness of the proposed approach is verified via full-scale experiment. • The proposed algorithm exhibits good performance in simulating high wind speed field with significant fluctuations. [ABSTRACT FROM AUTHOR]

The actuator disc (AD) model combined with the Reynolds-averaged Navier-Stokes (RANS) model has been demonstrated as the most effective approach for simulating wind farm wakes due to its computational efficiency and acceptable accuracy. However, challenges remain in defining the reference inflow wind speed for the AD model and identifying a robust turbulence model for the RANS method. To solve these issues, two strategies for establishing the AD model (AD- up1D model and AD- local model), along with four turbulence models (standard k - ε , SST k - ω , linear pressure-strain RSM and modified RSM), are comprehensively validated here to predict multiple wakes within two representative wind farms. The temporal variability of multiple turbines' power output and the significant impact of wind direction on the power is particularly emphasized. Overall, this study indicates that the proposed AD- local /Mod RSM method facilitates a realistic wake recovery in the cumulated wake flow and agrees well with the field measurements. It consistently performs well, never ranking last in any test cases, with percentage deviations ranging from 1.8% to 9.0%. Most importantly, this study evaluates the strengths and weaknesses of each AD/RANS method and provides recommendations for wind farm design purposes. • Two typical wind farms featuring regular and irregular layouts are investigated. • A systematic evaluation of the AD/RANS method setting approaches are conducted. • The proposed AD- local /Mod RSM method facilitates a realistic cumulated wake effect. • Identify an effective AD/RANS model for predicting multiple wake interactions. • The quality of each turbulence model is highly related to the wind sector angle. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. • An outlier-robust edRVRFL prediction model is developed. • Use VMD to decompose the data and enhance the model prediction capability. • Fuzzy entropy is used as a criterion for data aggregation. • AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. [ABSTRACT FROM AUTHOR]

The global increase in energy demand and environmental concerns have made the development of wind energy increasingly important. Accurate wind speed prediction is crucial for maximizing the benefits of wind energy. In order to further improve the accuracy of multi-site wind speed prediction, this study adopts an evolutionary algorithm-based deep learning model that fully considers the spatiotemporal relationships among multiple station's wind speed data. Firstly, the mutual information (MI) method is used to select variables with stronger correlations to wind speed as auxiliary input factors. Then, an improved version of Attribute-Augmented Spatiotemporal Graph Convolutional Network (IASTGCN) is employed to process data from multiple stations, taking into account both temporal and spatial factors. Additionally, an MI-based wind speed data relationship matrix between multiple stations is calculated to replace the original distance relationship matrix in the model, enabling the model to better capture and utilize the relationships between stations. Next, the Tree-structured Parzen Estimator (TPE) is used to optimize the hyperparameters of the model. This ultimately achieves multi-site multi-step wind speed prediction. Experimental results demonstrate that the proposed model outperforms baseline models and models that only consider either temporal or spatial factors in various scenarios, exhibiting better predictive performance. • A new hybrid model has been proposed for multi-site wind speed prediction. • The IASTGCN model is used to achieve multi-site multi-step wind speed prediction. • Partial improvements have been made to the ASTGCN model. • The TPE algorithm is used to optimize the hyperparameters of the model. [ABSTRACT FROM AUTHOR]

The integration of large-scale offshore wind power into the power grid presents significant challenges for grid operation and dispatch due to the variability and intermittency of offshore wind energy. However, non-stationarities and noise in wind speed time series pose challenges. This study proposes a VMD-PE-FCGRU methodology to address this challenge of predicting offshore wind speeds. Firstly, the Variational Mode Decomposition (VMD) algorithm decomposes the original wind speed signal, considering the strong fluctuations and high noise inherent to offshore wind energy. A corresponding performance evaluation index is established to assess the effectiveness of this deconstruction process. To further enhance the model's (GRU) ability to capture time series information, a Position Encoding layer (PE) has been added, and the network is deepened through a Fully Connected Neural Network (FCNN). This allows multi-dimensional time series features to be input into the Gated Recurrent Unit (GRU) network for forecast. The accuracy of the proposed method is then verified using measured historical data from the wind tower of an offshore wind farm in Guangdong. Experimental results demonstrate that the proposed method can accurately forecast short-term wind speeds for offshore wind farms, with a Mean Absolute Error (MAE) of 0.199 and a Mean Absolute Percentage Error (MAPE) of only 2.45%. Moreover, the qualified rate (r) reaches 100%, providing an accurate reference for real-time power grid dispatching and can inform decision-making for the integration of offshore wind power into the grid. • VMD-PE-FCGRU improves offshore wind speed prediction for grid integration. • VMD decomposes wind speed signal, while PE and FCNN enhance time series information. • Method accurately predicts short-term wind speeds with MAE of 0.206 and MAPE of 1.11%. • VMD-PE-FCGRU outperforms 5 popular prediction models with 100% qualified rate. • Ablation experiments confirm VMD's noise reduction and model enhancement with PE and FCNN. [ABSTRACT FROM AUTHOR]

This study presents an analytical model for the radiative SkyCool radiators (RSCR) with various interior flowing channels by using Laplace transformations. Moreover, the convolution theory is applied to solve the problem of nonuniform temperature distribution on the RSCR. Then, an outdoor experiment is carried out to verify the proposed model. Subsequently, the influence of different channel geometries, flow rates, tilt angles and wind velocities on the cooling performances of RSCRs are investigated. The results indicate that increasing the RSCR's length, enlarging the pipe spacing, and decreasing the flow rate and thickness of RSCR can definitely intensify the cooling performances. The application suggestions for S-channel RSCR (SRSCR) and I-channel RSCR (IRSCR) are given. When the pipe spacing ratio is smaller than five, the IRSCR is recommended because of lower thermal interferences. By contrast, SRSCR is recommended once the dimensionless pipe spacing is greater than ten. For cases with smaller tilt angles but larger wind velocities, IRSCR is suggested, while SRSCR is prioritized for cases with opposite conditions. This research provides an effective analytical tool in the evaluation of cooling power generation by producing cold water, contributing valuable insights for the optimization of cooling efficiencies of RSCRs under varied configuration scenarios. • The analytical models of RSCR with different channel configurations are proposed. • The outdoor experiments are carried out for the analytical model validations. • The influence of design and operation parameters on cooling performances is investigated. • The application suggestions on selecting the different RSCRs are given. [ABSTRACT FROM AUTHOR]

Wind power is one of the most vital renewable energy resources in the world. Wind energy production is directly correlated with the quality and quantity of wind speed data. Interpolation techniques can be employed to fill in the gaps in the current wind speed observation data series. However, existing methods for obtaining blank data do not pre-regulate the regional spatial wind speed sequence and instead rely on direct interpolation, which leads to low accuracy in the interpolation. This is not a problem with the model itself, but rather with the wind speed flowing in space and exhibiting sequence misalignment on the timeline. To address this issue, this study proposes a Wind Speed Dynamic Time Warping (WSDTW) algorithm based on Dynamic Time Warping (DTW) to match similar wind speed reduction sequences in terms of time error. We used the shape context descriptor to encode wind speed and introduced wind rose descriptors to represent wind direction initially. The matching cost of DTW was then optimized. Finally, five common interpolation methods were selected to evaluate the method. The research results indicate that interpolation after WSDTW matching and warping can significantly improve the accuracy of wind speed interpolation and reduce the spatial dependence of wind speed. This method demonstrates good stability and generalization, performing exceptionally well in situations where there are gaps in regional wind speed data or missing data. • The time series bias caused by the wind speed transfer mechanism is explained. • A wind speed dynamic time warping (WSDTW) based on DTW is proposed. • WSDTW strengthens the spatial position connection between wind direction and interpolation points. • The beneficial effects of WSDTW were evaluated using five interpolation techniques. [ABSTRACT FROM AUTHOR]

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity. • Propose a GCN-based adaptive predictive generative adversarial network (GAPGAN). • Employ a novel evaluation framework to assess the generated wind speed scenarios. • Incorporate both temporal and spatial features in the wind speed scenario generation. • Balance both the accuracy and the diversity of the generated wind speed scenarios. • Validate the proposed GAPGAN using real wind farm data. [ABSTRACT FROM AUTHOR]

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance. • A dynamic adjacency matrix is used to assess the significance of spatial features. • The DSTGCN is proposed to boost the sensitivity of the model to critical features. • A residual estimation strategy is applied to eliminate the quantile crossings problem. • Conduct comprehensive comparisons with the-state-of-the-art methods. [ABSTRACT FROM AUTHOR]

It is well known that a shroud placed around a wind turbine can increase its power coefficient, but it brings complex mechanisms by which the shroud alters the flow passing through the rotor. Such mechanisms impose numerical challenges, as the shrouded turbines present nonlinear behavior in the wake. This paper deals with a comprehensive analysis of tip vortex trajectory in shrouded wind turbines using Reynolds Averaged Navier–Stokes numerical solutions. The analysis includes aerodynamic performance and vortex characteristics of the whole wind turbine. The Multiple Reference Frame is used on a high-order unstructured compressible solver to study both, isolated and shrouded rotor. The NREL Phase VI Unsteady Aerodynamic Experiment rotor is used as a test case. The accuracy of results for wind speeds between 7 and 25 m s − 1 is discussed. Overall, good agreement is achieved between the computed pressure distributions and the experimental reference values. At stalled blade, more efforts are needed to improve numerical solutions, especially for integrated load quantities. The vortex structure is examined, showing that shroud impacts tip vortex trajectory by the increase of the axial induced velocity at the rotor plane. This result, demonstrates that the classical Prandtl tip loss is not accurate for shrouded turbine analysis, and modern finite blade functions are needed. The influence of the flow conditions on the tip vortex trajectory, flow separation and shroud interaction are also discussed. • Tip vortex trajectory of the NREL Phase Vi. • Use of a simplified Multiple Reference Frame model with open-source high-order CFD solver, avoiding conformal interface complexities and enabling efficient shrouded turbine simulation. • Accurate and efficient computation of the integrated loads on the wind turbine. • Unprecedented perspective into profoundly altered tip vortex trajectory behavior within shrouded turbines using validated higher-order framework. • Study of the interaction between the tip vortex and shroud's boundary layer. [ABSTRACT FROM AUTHOR]

The issue of nonlinear aerodynamic stability of blades is a significant technological bottleneck in the development of large-scale wind turbines. In order to explore the influencing parameters and mechanisms of the nonlinear aerodynamic stability of large-scale wind turbine blades, the flexible blade was simplified into a two-dimensional rigid blade segment with elastic supports, which formed the pitch single-degree-of-freedom aeroelastic wind tunnel test system. Through wind tunnel vibration and pressure measurement synchronization tests, the torsional vibration characteristics and the flow field development mechanism of the airfoil under different wind speeds and attack angles were studied in detail. The wind tunnel tests revealed two distinct torsional aeroelastic behaviors: the static equilibrium state with negligible amplitude and high vibrational randomness, and the stall flutter state with larger amplitude, which was changed according to sinusoidal law substantially. The stall flutter state was confined within a specific wind speed range, and its stability boundary, which included the critical initiation wind speed and the critical cessation wind speed, all varied linearly with the installation attack angle. The growth rate of the critical cessation wind speed with the installation attack angle was significantly higher than that of the critical initiation wind speed, making the wind speed range for the occurrence of stall flutter also linearly larger. Additionally, the frequency of the stall flutter was significantly higher than the natural frequency of the model, and it exhibited a positive correlation with the critical initiation wind speed. The flow state around the airfoil during stall flutter was primarily distinguished from its static counterpart by the presence of leading-edge vortices. The formation and shedding of these leading-edge vortices would significantly influence the characteristics of stall flutter. • Used single-freedom aeroelastic test system to study airfoil torsional vibration. • Stall flutter of airfoil is confined in a specific wind speed range. • Vibration frequency has positive correlation with wind speed stability boundary. • Leading-edge vortices significantly affect characteristics of airfoil stall flutter. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming. • This article proposes an enhanced version of the dual-layer LSTM structure. • A self-adaption preprocessing to manage the impact of noise-induced interference. • Fuzzy interval forecasting can give the upper and lower of wind speed effectively. [ABSTRACT FROM AUTHOR]

To promote the transformation from traditional heating to clean heating, the electric heat storage technology is gradually gaining attention. This study prepared a new kind of porous phase change bricks with high heat storage density and thermal conductivity, and constructed a new type of phase change storage device with multi-airflow channels for space heating. Under different operating modes and conditions, a series of experiments were conducted to analyze the heat storage/release performance, energy conversion efficiency and internal temperature distribution. When the heating power increased from 1000 W to 1400 W, the heat storage efficiency increased by 7.3% and the exergy efficiency decreased by 2.6% in Case I (heat storage mode). The rise in outlet wind speed improved the heat output capacity, yet the exergy performance varied in different modes. When the outlet wind speed increased from 0.05 m/s to 0.2 m/s, the heat release efficiency increased by 27.3% in Case II (heat release mode) and 8.3% in Case III-b (simultaneous heat storage and release mode). The exergy efficiency increased by 6.3% in Case II and dropped by 4.6% in Case III-b. In addition, the internal temperature uniformity (S x) was mainly affected by the distribution of heat sources and airflow. • An end heating device based on new porous phase change brick is constructed. • Experimental tests are conducted under different operating modes and parameters. • High power leads to high heat storage efficiency yet low exergy stored amount. • The rise of wind speed increases the heat release efficiency by up to 27.3%. • In the heat storage/release mode, the heat utilization efficiency reaches 99.5%. [ABSTRACT FROM AUTHOR]

The accurate evaluation of wake turbulence intensity (TI) is of great significance for the layout optimization of wind farms and research on control strategies. However, the existing engineering wake models still have many shortcomings in evaluating the TI in the wake of horizontal-axis wind turbines (HAWTs), especially in the near wake region. In response to this, this article proposed an analytical model based on the double-Gaussian ellipse function that can describe the three-dimensional added TI distribution in the entire wake region. Firstly, the anisotropy of wake expansion is taken into account in both the horizontal and vertical directions, assuming that the added TI distribution downstream of the HAWT is a double-Gaussian elliptical shape. Secondly, taking into account the self-similarity characteristics of flow TI, the maximum added TI and its position in the entire wake region are evaluated. In addition, considering the influence of the ground effect, the vertical turbulence distribution was corrected. Finally, the proposed added turbulence model was validated and relative error analysis was conducted using large eddy simulation (LES) data, wind field measurement data, and wind tunnel experimental data. The results show that the model had good prediction accuracy in the entire wake region, and its prediction performance was significantly improved compared to traditional models, especially in the near wake region, with the lowest even relative error of 3.66%. This model has significant advantages such as low computational cost, wide applicability, and high accuracy. It can reduce energy losses in wind farms, improve wind energy utilization efficiency, and has great potential for widespread application in large-scale wind farms. • The ground effect and the anisotropy of wake expansion are studied. • The maximum added turbulence and its position in entire wake region are evaluated. • A 3D added turbulence intensity analytical model is proposed. • The 3D analytical model is verified using the LES, wind farm and wind tunnel data. • The 3D analytical model has good accuracy in entire wake region. [ABSTRACT FROM AUTHOR]

Wind speed plays a pivotal role in ensuring the stability of power grid operations. However, the inherent high volatility and non-stationarity of wind patterns pose significant challenges to achieving accurate predictions. To tackle this issue and enhance the interpretability of existing wind speed prediction models, this study proposes an innovative short-term wind speed prediction model that combines two-stage decomposition, meteorological data feature engineering, adaptive differential evolution with optional external archive (JADE) algorithm, and temporal fusion transformers (TFT). To begin, the wind speed data is subjected to decomposition using improved complete ensemble empirical mode decomposition with adaptive noise (IEEMD), yielding multiple eigenmode functions. Subsequently, the nonlinear decomposition subsequence undergoes further decomposition into multiple submodes via empirical wavelet transform (EWT), followed by the careful screening of nonlinear quadratic decomposition submodes. Additionally, meteorological features are ingeniously reconstructed into combined and statistical meteorological features, enhancing the effectiveness of input features. Next, the hyperparameters of the TFT are meticulously optimized using the powerful JADE algorithm. Empirical results unequivocally demonstrate that the IEEMD-EWT-JADE-TFT model achieves remarkably high prediction accuracy. Meanwhile, this interpretative experimental process and its results chart a novel path for decision-makers seeking reliable wind speed forecasting processes and outcomes. • A scientific feature engineering method for meteorological data is proposed. • An effective linear-nonlinear two-stage decomposition method is introduced. • An explainable wind speed forecasting methodology named ICEEMDAN-EWT-JADE-TFT is proposed. • Impacts of input variables are analyzed by TFT's interpretability capability. [ABSTRACT FROM AUTHOR]

Due to the nonlinearity, fluctuation, and intermittency of wind speed, its accurate prediction is essential for improving efficiency in wind power operation systems. In this regard, a hybrid model that combines the rime optimization algorithm (RIME), variational mode decomposition (VMD), multi-headed self-attention (MSA) mechanism and long short-term memory (LSTM) is proposed for wind speed prediction. First, the number of modes and VMD penalty parameter are optimized with RIME, the optimized parameters are brought into the VMD to decompose the raw wind speeds, and a Lagrange multiplier and quadratic penalty function are introduced to obtain the input series. Then, a LSTM short-term wind speed prediction model is constructed based on the MSA mechanism and solved for the hidden states and weights of each layer of attention in the model. Finally, a ReLU activation function is used to activate the hidden states of the LSTM model, and a weighted sum vector is used as the final sequence representation, which is inputted to the output layer for specific prediction to obtain the short-term wind speed prediction results. To verify the effectiveness of the proposed model, wind speed data from four wind farms in Ningxia, China, and two sets of wind speed data from an M2 tower in the USA are selected, and 19 models are built to compare the performance of the proposed model. The results show that the proposed model outperforms other models on all datasets in terms of all five performance metrics, with smaller errors and higher prediction accuracy. • Tuning the parameters of the VMD by the RIME optimization algorithm. • Proposing a new MSA-LSTM module to prevent long-range dependencies. • An MSA is introduced to the LSTM input and to enhance sequence correlation. • The stability and robustness of the method are verified in different experiments. • The model was validated using six sets of data with different characteristic wind speeds. [ABSTRACT FROM AUTHOR]

This study focuses on ensuring the stable operation of the power grid by accurately forecasting the theoretical power generation capacity of wind turbine units, especially in scenarios integrating significant amounts of renewable energy into the grid. The forecasting process involves two key steps: initially forecasting wind speeds and then estimating theoretical power generation using wind turbine power conversion curves. This article proposes a wind speed forecasting system based on deep learning, integrating multiple hybrid models and employing deep learning algorithms to select the optimal wind speed hybrid forecasting model, optimized by the multi-objective mayfly optimization algorithm. Additionally, a wind energy conversion simulation system for wind turbines has been developed, precisely simulating the physical process of converting wind energy into electrical energy. This system, in conjunction with wind speed forecasting, estimates the theoretical power generation of wind farms. The results of this research hold significant practical implications for enhancing the operational efficiency of wind power, strengthening the grid's supply-demand balance, and increasing the economic and environmental value of wind power projects. • The wind forecasting system incorporates clustering, classification, and a forecasting model. • A deep learning neural network serves as the model selection mechanism, choosing the optimal model for each time point. • A multi-objective optimization algorithm is employed to fine-tune the parameters of the chosen optimal model. • The wind speed forecast values are integrated with the power curve to forecast theoretical power generation. [ABSTRACT FROM AUTHOR]