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To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R2) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the Similar Days Method (SDM) and Artificial Neural Network (ANN), introduces several novelties: (1) selection of similar days based on hidden representations of day data using Autoencoder (AE); (2) enhancement of model generalization by utilizing a broader set of training examples; (3) incorporating the relative importance of training examples derived from the similarity measure during training; and (4) mitigation of the influence of outliers by applying an ensemble of ANN models trained with different data splits. The presented AE configuration and procedure for selecting similar days generated a higher-quality training dataset, which led to more robust predictions by the ANN model for days with unexpected deviations. Experiments were conducted on actual load data from a Serbian electrical power system, and the results were compared to predictions obtained by the field-proven STLF tool. The experiments demonstrated an improved performance of the presented solution on test days when the existing STLF tool had poor predictions over the past year. [ABSTRACT FROM AUTHOR]

This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China's "dual-carbon" policy. The integration of renewable energy under this policy has introduced complexities such as nonlinearity and instability. To enhance forecasting accuracy, the VMD-SE-BiSATCN prediction model is proposed. This model improves computational efficiency and reduces prediction errors by analyzing and reconstructing sequence component complexity using sample entropy (SE) following variational mode decomposition (VMD). Additionally, a self-attention mechanism is integrated into the temporal convolutional network (TCN) to overcome the traditional TCN's limitations in capturing long-term dependencies. The model was evaluated using data from the China Ninth Electrical Attribute Modeling Competition and validated with real-world data from a specific county in Shijiazhuang City, Hebei Province, China. Results indicate that the VMD-SE-BiSATCN model outperforms other models, achieving a mean absolute error (MAE) of 92.87, a root mean square error (RMSE) of 126.906, and a mean absolute percentage error (MAPE) of 0.81%. [ABSTRACT FROM AUTHOR]

The current power load exhibits strong nonlinear and stochastic characteristics, increasing the difficulty of short-term prediction. To more accurately capture data features and enhance prediction accuracy and generalization ability, in this paper, we propose an efficient approach for short-term electric load forecasting that is grounded in a synergistic strategy of feature optimization and hyperparameter tuning. Firstly, a dynamic adjustment strategy based on the rate of the change of historical optimal values is introduced to enhance the PID-based Search Algorithm (PSA), enabling the real-time adjustment and optimization of the search process. Subsequently, the proposed Improved Population-based Search Algorithm (IPSA) is employed to achieve the optimal adaptive variational mode decomposition of the load sequence, thereby reducing data volatility. Next, for each load component, a Bi-directional Gated Recurrent Unit network with an attention mechanism (BiGRU-Attention) is established. By leveraging the interdependence between feature selection and hyperparameter optimization, we propose a synergistic optimization strategy based on the Improved Population-based Search Algorithm (IPSA). This approach ensures that the input features and hyperparameters for each component's predictive model achieve an optimal combination, thereby enhancing prediction performance. Finally, the optimal parameter prediction model is used for multi-step rolling forecasting, with the final prediction values obtained through superposition and reconstruction. The case study results indicate that this method can achieve an adaptive optimization of hybrid prediction model parameters, providing superior prediction accuracy compared to the commonly used methods. Additionally, the method demonstrates robust adaptability to load forecasting across various day types and seasons. Consequently, this approach enhances the accuracy of short-term load forecasting, thereby supporting more efficient power scheduling and resource allocation. [ABSTRACT FROM AUTHOR]

To accommodate the rapid development of the distribution network of China, it is essential to research load forecasting methods with higher accuracy and stronger generalization capabilities in order to optimize distribution system control strategies, ensure the efficient and reliable operation of the power system, and provide a stable power supply to users. In this paper, a short-term load forecasting method is proposed for low-voltage distribution substations based on the bidirectional long short-term memory (BiLSTM) model. First, principal component analysis (PCA) and the fuzzy C-means method based on a genetic algorithm (GA-FCM) are used to extract the main influencing factors and classify different types of user electricity consumption behaviors. Then, the BiLSTM forecasting model utilizing the stochastic weight averaging (SWA) algorithm to enhance generalization capability is constructed. Finally, the load data from a low-voltage distribution substation in China over recent years are selected as a case study. Compared with conventional LSTM and BiLSTM prediction models, the annual electricity load curves for various user types forecasted by the PCA-BiLSTM model are more closely aligned with actual data curves. The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage. [ABSTRACT FROM AUTHOR]

For the air conditioning with secondary pump variable flow system, considering the regional cooling situation, the multi-objective regression method was used to solve the load forecasting problem for improving the accuracy of load forecasting. For the central air conditioning, two multi-objective regression load forecasting models of multi-objective support vector regression(SVR) and multi-objective long short-term memory(LSTM)neural network were proposed. The two models were used to train and predict on the data of the secondary pump variable flow system of the hospital in Shanghai, and the results were compared with those of the single objective regression prediction model. The results show that the prediction accuracies of the two multi-objective prediction models are higher than that of the single objective regression prediction model, and the multi-objective SVR load forecasting model has higher prediction accuracy than the multi-objective LSTM load forecasting model. [ABSTRACT FROM AUTHOR]

Electricity is the most important and useful energy form for our modern society. This calls for smart management to have optimal usage. Artificial neural networks (ANNs) have emerged as a powerful tool in forecasting power demand, especially when multiple variables are involved. In this paper, taking a city in Morocco as an illustrating example, a long short-term memory (LSTM) neural network is developed to predict power consumption via five weather factors. Our numerical results corroborate the merits of LSTM in forecasting the load demand with which temperature exhibits the strongest correlation. [ABSTRACT FROM AUTHOR]

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, thebidirectional LSTMlayermodels the data of thehistorical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant information from different time steps to reduce the prediction error of the model. Simultaneously, the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism, so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input. Finally, the chaotic sparrow search algorithm (CSSA) is used to optimize the hyperparameter selection of the model. The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238, respectively, which is lower than other forecastingmethods, demonstrating the accuracy and stability of our model. [ABSTRACT FROM AUTHOR]

There are many influencing factors present in different situations of power load. There is also a strong imbalance in the number of load samples. In addition to examining the problem of low training efficiency of existing algorithms, this paper proposes a short-term power load prediction method based on feature selection and error compensation under imbalanced samples. After clustering the load data, we expand some sample data to balance the sample categories and input the load data and filtered feature sequences into the improved GRU for prediction. At the same time, the errors generated during the training process are used as training data. An error correction model is constructed and trained, and the results are used for error compensation to further improve prediction accuracy. The experimental results show that the overall prediction accuracy of the model has increased by 80.24%. After expanding a few samples, the prediction accuracy of the region where the samples are located increased by 59.41%. Meanwhile, due to the improvement of the algorithms, the running time was reduced by approximately 14.92%. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting is crucial for the steady operation of the power system and power market schedule planning. The extraction of features and training of prediction models are challenging as the load series is extremely volatile and nonlinear. To address the above issues, we propose a deep bidirectional long short-term memory (DBiLSTM) network based on variational mode decomposition (VMD) and an attention mechanism, in which the model hyperparameters are optimized using the improved particle swarm optimization (IPSO) technique. In this study, the mode number k of the VMD is determined by the ratio of residual energy following decomposition. Subsequently, the DBiLSTM is stacked using multiple layers of BiLSTM for a more precise representation of time-series data and the capturing of information at different scales, thereby enabling nonlinear load sequence forecasting and enhancing the accuracy. Finally, the IPSO uses nonlinear decreasing inertia weights to overcome the drawbacks of premature convergence and local optima. The effectiveness and progress of the proposed method are evaluated using the power load dataset from the ninth electrical attribute modeling competition test questions. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method. [ABSTRACT FROM AUTHOR]

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Accurate short-term load forecasting is the key to ensuring smooth and efficient power system operation and power market dispatch planning. However, the nonlinear, non-stationary, and time series nature of load sequences makes load forecasting difficult. To address these problems, this paper proposes a short-term load forecasting method (EPT-VMD-TCN-TPA) based on the hybrid decomposition of load sequences, which combines ensemble patch transform (EPT), variational modal decomposition (VMD), a temporal convolutional network (TCN), and a temporal pattern attention mechanism (TPA). In which, the trend component (Tr(t)) and the residual fluctuation component (Re(t)) of the load series are extracted using EPT, and then the Re(t) component is decomposed into intrinsic modal function components (IMFs) of different frequencies using VMD. The Tr(t) and IMFs components of the fused meteorological data are predicted separately by the TCN-TPA prediction model, and finally, the prediction results of each component are reconstructed and superimposed to obtain the final predicted value of the load. In addition, experiments after reconstructing each IMF component according to the fuzzy entropy (FE) values are discussed in this paper. To evaluate the performance of the proposed method in this paper, we used datasets from two Areas of the 9th Mathematical Modeling Contest in China. The experimental results show that the predictive precision of the EPT-VMD-TCN-TPA model outperforms other comparative models. More specifically, the experimental results of the EPT-VMD-TCN-TPA method had a MAPE of 1.25% and 1.58% on Area 1 and Area 2 test sets, respectively. [ABSTRACT FROM AUTHOR]

Wind power generation is the major approach to wind energy utilization. However, due to the volatility, intermittent, and controllability of wind power, it is difficult to control and scheduling of wind power, which brings challenges to the grid-connected operation and dispatch of wind power. Therefore, accurate power prediction of the wind power generation system is worthy of in-depth study. And this paper proposes a wind power prediction model based on logistic chaos atom search optimization (LCASO) optimized back-propagation (BP) neural network, aiming to achieve accurate and efficient power prediction. Moreover, this work utilizes data preprocessing to obtain more precise prediction results and related prediction evaluation indexes to quantificationally compare the effect of the proposed one with other prediction models based on GA-BP neural network and PSO-BP neural network. In contrast with the BP neural network, GA-BP neural network, and PSO-BP neural network, the simulation tests verify the comprehensive prediction performance and wider applicability of LCASO-BP neural network-based power prediction model. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation fluctuates, photovoltaic output is easily affected by various meteorological characteristics, and traditional TCN networks tend to over-enhance spatial characteristics and weaken individual characteristics. In view of these problem, a short-term photovoltaic power generation prediction model based on VMD and improved TCN is proposed in this study. The original photovoltaic power generation time series is decomposed into several modal components of different frequencies through VMD, and each modal component and the corresponding meteorological data are input to the improved TCN network for modeling and learning. The center frequency method is used to determine the optimal decomposition modal number of VMD. On the basis of the traditional TCN prediction model, DropBlock regularization is used to replace Dropout regularization to achieve the effect of suppressing information synergy in the convolutional layer, and the attention mechanism is introduced to autonomously mine and highlight the impact of key meteorological input characteristics and quantify the impact of various meteorological factors on photovoltaic power generation to improve forecasting precision. Based on the real data of a photovoltaic power station in Jiangsu, the simulation experiments show that the RMSE of the proposed prediction method is 0.62 MW and the MAPE is 2.03%. [ABSTRACT FROM AUTHOR]

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models' robustness can't be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks. [ABSTRACT FROM AUTHOR]

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model. [ABSTRACT FROM AUTHOR]

With the rapid development of IoT, big data and artificial intelligence, the research and application of data-driven hydrological models are increasing. However, when conducting time series analysis, many prediction models are often directly based on the following assumptions: hydrologic time series are normal, homogeneous, smooth and non-trending, which are not always all true. To address the related issues, a solution for short-term hydrological forecasting is proposed. Firstly, a feature test is conducted to verify whether the hydrological time series are normal, homogeneous, smooth and non-trending; secondly, a sequence-to-sequence (seq2seq)-based short-term water level prediction model (LSTM-seq2seq) is proposed to improve the accuracy of hydrological prediction. The model uses a long short-term memory neural network (LSTM) as an encoding layer to encode the historical flow sequence into a context vector, and another LSTM as a decoding layer to decode the context vector in order to predict the target runoff, by superimposing on the attention mechanism, aiming at improving the prediction accuracy. Using the experimental data regarding the water level of the Chu River, the model is compared to other models based on the analysis of normality, smoothness, homogeneity and trending of different water level data. The results show that the prediction accuracy of the proposed model is greater than that of the data set without these characteristics for the data set with normality, smoothness, homogeneity and trend. Flow data at Runcheng, Wuzhi, Baima Temple, Longmen Town, Dongwan, Lu's and Tongguan are used as input data sets to train and evaluate the model. Metrics RMSE and NSE are used to evaluate the prediction accuracy and convergence speed of the model. The results show that the prediction accuracy of LSTM-seq2seq and LSTM-BP models is higher than other models. Furthermore, the convergence process of the LSTM-seq2seq model is the fastest among the compared models. [ABSTRACT FROM AUTHOR]

Excavation load prediction is of great importance for the prior design and latter performance evaluation of tunnel boring machines (TBMs). In this paper, an improved load prediction model is developed based on classical Colorado school of mines model for TBMs equipped with constant cross-sectional disc cutters. The typical structure and principle are introduced to predict the single cutter force, and the total cutter group load is calculated by defining the equivalent diameter and cutter spacing. Subsequently, the improved model of a more brief and acceptable type is established via summation. Some novel performance indexes, including the reformed field penetration index, torque/thrust penetration index, and specific energy are, respectively, derived in formulaic form. By field data verification in the borehole zones of two cases, the proposed model is proven to be more accurate in the total load prediction. The single-factor regression results show that the reformed field penetration index reveals the nonlinear relationship between TBM load and penetration rate, and the torque/thrust penetration index is a new TBM inherent index to evaluate the working conditions. Specific energy, used to evaluate the excavation efficiency, is positive with rock strength and proved negative with penetration rate via a normalization analysis. Finally, suggestions on the cutter group configuration against abominable stratum are discussed. [ABSTRACT FROM AUTHOR]

In the existing short-term forecasting methods of time series, two challenges are faced: capture the associations of data and avoid cumulative errors. For tackling these challenges, the fuzzy information granule based model catches our attention. The rule used in this model is fuzzy association rule (FAR), in which the FAR is constructed from a premise granule to a consequent granule at consecutive time periods, and then it describes the short-association in data. However, in real time series, another association, the association between a premise granule and a consequent granule at non-consecutive time periods, frequently exists, especially in periodical and seasonal time series. While the existing FAR can't express such association. To describe it, the fuzzy long-association rule (FLAR) is proposed in this study. This kind of rule reflects the influence of an antecedent trend on a consequent trend, where these trends are described by fuzzy information granules at non-consecutive time periods. Thus, the FLAR can describe the long-association in data. Correspondingly, the existing FAR is called as fuzzy short-association rule (FSAR). Combining the existing FSAR with FLAR, a novel short-term forecasting model is presented. This model makes forecasting at granular level, and then it reduces the cumulative errors in short-term prediction. Note that the prediction results of this model are calculated from the available FARs selected by the k-medoids clustering based rule selection algorithm, therefore they are logical and accurate. The better forecasting performance of this model has been verified by comparing it with existing models in experiments. [ABSTRACT FROM AUTHOR]

With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified. [ABSTRACT FROM AUTHOR]

The central air conditioning system provides city dwellers with an efficient and comfortable environment. Meanwhile, coinciding with their use, the building electricity load is increased, as central air conditioners consume a lot of electricity. It has become necessary to control central air conditioners for storage and to analyze the energy saving optimization of central air conditioner operation. This study investigates the energy consumption background of central air conditioning systems, and proposes an intelligent load prediction method. With a back propagation (BP) neural network, we use the data collected in the actual project to build the cooling load prediction model for central air conditioning. The network model is also trained using the Levenberg–Marquardt (LM) algorithm, and the established model is trained, tested, and predicted by importing a portion of the sample data, which is filtered by preprocessing. The experimental results show that most of the data errors for training, testing, and prediction are within 10%, indicating that the accuracy achievable by the model can meet the practical requirements, and can be used in real engineering projects. [ABSTRACT FROM AUTHOR]

Due to the sensitivity of spaceborne atomic clock to many factors, the variation law of satellite clock bias (SCB) can be regarded as a grey system. The GM (1,1) model is a most classical and basic model of grey system, which has been successfully applied in SCB prediction. Moreover, many improved models have been proposed and widely used in various forecasts since GM (1,1) was generated. However, the prediction performance of these models is not obviously improved compared with the classical models in clock bias prediction. In view of this, a new GM (1,1) model has been come up with in this paper by optimising fitting model and initial condition. The new fitting model is obtained by differentiating time response function of winterisation, and the new initial condition is generated through one or more components of the original clock bias sequence. The authors employ GPS rapid and precise SCB provided by the International GNSS Service (IGS) for prediction experiments. The results show that the new GM (1,1) model is effective and feasible, and its prediction accuracy and stability are enormously better than that of the classical GM (1,1) model, especially for ultra‐short‐term prediction. [ABSTRACT FROM AUTHOR]

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in a passive manner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. For non-persistent desktop pool, based on the aggregation session count of pool-sharing users, the CAFE approach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. For persistent desktop pool, based on the session connection status of individual users within the same pool, the SOUP approach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks. [ABSTRACT FROM AUTHOR]

Power system is the primary basis for national development, and electricity load prediction is an essential part of the power system. Accurate short-term load forecasting can ensure the stable operation of the power system. To improve the accuracy of short-term load forecasting, a hybrid prediction model with an improved Carnivorous Plant Algorithm (ICPA) to Optimize the Group Method of Data Handling (GMDH) and Long Short-term Memory (LSTM) is proposed. This prediction model includes Improved Complete Ensemble Empirical Mode Decomposition Adaptive Noise (ICEEMDAN), Information Entropy (IE), LSTM, GMDH, and ICPA. Firstly, ICEEMDAN is applied to the original load data to obtain the Intrinsic Mode Functions (IMF) with different characteristic information. The obtained IMFs are reconstructed according to the IE. Then, the reconstructed IMFs are predicted using LSTM and GMDH forecasting models, respectively. Finally, the final forecasting results are weighted by the optimal weights obtained using ICAP. To verify the performance of the model, two power load datasets are selected as test datasets. And Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and inequality coefficient (TIC) are selected as indicators. The prediction model proposed in this paper outperforms other prediction models in all indicators. [ABSTRACT FROM AUTHOR]

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data. [ABSTRACT FROM AUTHOR]

In order to realize short-time load forecasting, an Adaboost-BP method with a weight update mechanism is proposed based on ensemble learning theory. Firstly, the original historical load power is decomposed into a set of sub-series with diverse characteristics via using ensemble empirical mode decomposition. Then, BP neural network is performed as a weak learner to predict the load power of test samples. At the same time, the prediction results are used to update the weight of the weak learner and test sample and then construct a strong learner to obtain the final prediction results. According to the analysis results of the characteristics of each sub-series, the load forecasting model is established. The result of analysing the calculation example shows that the proposed prediction model outperforms all other algorithms in accuracy, which has high engineering application value. [ABSTRACT FROM AUTHOR]

The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. Current researches have focused on the real-time prediction of mooring load using the machine learning method, but most of the studies are about the application and generalization analysis of different models. There are few studies on the influence of data distribution characteristics on prediction accuracy. In view of the above problems, this paper investigates the effect of data skewness on the prediction performance for the deep learning model. The long short-term memory (LSTM) neural network is applied to construct the mooring load prediction model. The numerical simulation datasets of the deep water semi-submersible platform are employed in model training and data analysis. The prediction performance of the model is preliminarily verified based on the simulation results. Meanwhile, the distribution characteristics of mooring load data under different sea states are analyzed and a skewness processing method based on the Box-Cox Transformation (BCT) is proposed. The effect of data skewness on prediction accuracy is further investigated. The comparison results indicate that reducing the mooring load data skewness can effectively improve the prediction accuracy of LSTM model. [ABSTRACT FROM AUTHOR]

Electrical load forecasting is a core element reflecting the operating conditions of the electricity system and a key tool responding to the demand of the electricity market. Achieving accurate short-term load forecasts remains a challenge due to the dynamic and non-stationary characteristics of the load data. Previous studies have mostly analyzed electrical load transformations from a single perspective. This approach often overlooks the dynamic diversity across different frequencies and the comprehensive effects of multi-time scale and granularity information. Research in electrical load forecasting has frequently failed to fully integrate multi-granularity perspectives. In this study, we introduce a novel approach, multi-granularity time-augmented learning (MTAL), to enhance the precision of short-term electrical load forecasting. Since the degree of dynamic change of different granularity information is overly influenced by time features, we design a time-augmented block to learn time representation and apply it to all granularity information to represent multi-granularity electrical load more reasonably. Furthermore, we incorporate an attention mechanism into the model, which serves to mitigate information redundancy and bolster its generalization capabilities. We evaluated our method on a univariate electrical load dataset and a multivariate electrical load dataset, respectively, and compared its performance with existing forecasting models. Experiments demonstrate that the MTAL model performs well in capturing load variation information and achieves better performance in both univariate and multivariate short-term electric load forecasting tasks. Compared to existing methods, our proposed model improves the prediction accuracy by 10%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} and reduces the computation time by 18%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document}. [ABSTRACT FROM AUTHOR]

The volatility and intermittency of solar energy seriously restrict the development of the photovoltaic (PV) industry. Accurate forecast of short‐term PV power generation is essential for the optimal balance and dispatch of power plants in the smart grid. This article presents a machine learning approach for analyzing the volt‐ampere characteristics and influential factors on PV data. A correlation analysis is employed to discover some hidden characteristic variables. Then, an adaptive ensemble method with stochastic configuration networks as base models (AE‐SCN) is proposed to construct the PV prediction model, which integrates bagging and adaptive weighted data fusion algorithms. Compared with the original SCN, SCN ensemble (SCNE) and random vector functional‐link network (RVFLN), linear regression model, random forest model and autoregressive integrated moving average (ARMA) model, AE‐SCN performs favorably in the terms of the prediction accuracy. This article is categorized under:Technologies > Prediction [ABSTRACT FROM AUTHOR]

To reduce the impact of volatility on photovoltaic (PV) power generation forecasting and achieve improved forecasting accuracy, this article provides an in-depth analysis of the characteristics of PV power outputs under typical weather conditions. The trend of PV power generation and the similarity between simultaneous outputs are found, and a hybrid prediction model based on feature matching, singular spectrum analysis (SSA) and a long short-term memory (LSTM) network is proposed. In this paper, correlation analysis is used to verify the trend of PV power generation; the similarity between forecasting days and historical meteorological data is calculated through grey relation analysis; and similar generated PV power levels are searched for phase feature matching. The input time series is decomposed by singular spectrum analysis; the trend component, oscillation component and noise component are extracted; and principal component analysis and reconstruction are carried out on each component. Then, an LSTM network prediction model is established for the reconstructed subsequences, and the external feature input is controlled to compare the obtained prediction results. Finally, the model performance is evaluated through the data of a PV power plant in a certain area. The experimental results prove that the SSA-LSTM model has the best prediction performance. [ABSTRACT FROM AUTHOR]

With the continuous development of new power systems, the load demand on the user side is becoming more and more diverse and random, which also brings difficulties in the accurate prediction of power load. Although the introduction of deep learning algorithms has improved the prediction accuracy to a certain extent, it also faces problems such as large data requirements and low computing efficiency. An ultra-short-term load forecasting method based on the windowed XGBoost model is proposed, which not only reduces the complexity of the model, but also helps the model to capture the autocorrelation effect of the forecast object. At the same time, the real-time electricity price is introduced into the model to improve its forecast accuracy. By simulating the load data of Singapore's electricity market, it is proved that the proposed model has fewer errors than other deep learning algorithms, and the introduction of the real-time electricity price helps to improve the prediction accuracy of the model. Furthermore, the broad applicability of the proposed method is verified by a sensitivity analysis on data with different sample sizes. [ABSTRACT FROM AUTHOR]

The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power prediction. Here, the MVMD combined with Spearman extracts correlation features of the weather data. An equilibrium optimizer (EO) is integrated with MVMD to achieve optimal values of the long short-term memory (LSTM) parameters. Firstly, the correlation of input features is determined and selected by Spearman. The MVMD model is used to mine the high correlation features of solar radiation and conduct cross-correlation analysis to extract input feature components. Secondly, the similar weather days of the sample set are classified to ensure a good adaptability in different weather situations. Finally, the high correlation features are introduced into the photovoltaic power prediction model of EO optimized LSTM. Performance analysis using actual output power data from a PV plant shows that the proposed MVMD feature extraction method can effectively mine correlation features to achieve an optimized dataset under different seasons. Compared with the gray wolf and particle swarm optimization algorithms, the proposed model has a better optimization performance in a low discrimination of input feature decomposition components and low correlation with output power. [ABSTRACT FROM AUTHOR]

• Overall energy efficiency (OEE) is a crucial factor in tractor-implement systems. • Response surface methodology (RSM) identifies the influence of factorial changes. • Higher correlation value associated with the subsoiler's OEE prediction model. • RSM model accurately predicted the OEE in current study. • Vertical load has a negligible effect on the tested parameters and OEE. The aim of this paper was to predict and optimize the overall energy efficiency (OEE) of a tractor-implement system in semi-deep tillage via response surface methodology (RSM) approach. The OEE was affected by two tillage tines (i.e., subsoiler and paraplow), four forward speeds (i.e., 1.8, 2.3, 2.9 and 3.5 km/h), three operating depths (i.e., 30, 40 and 50 cm) and vertical load imposed on tractor rear wheels at two levels (i.e., 225 kg and no-weight). All tests were replicated four times, forming 192 data points. Field test results revealed all variables were influential on the OEE except vertical load. An increment of speed and depth increased OEE. The RSM approach displayed 3D views with higher accuracy for the OEE change due to changing tine, speed, depth and vertical load relative to regression prediction models. Another feature of the RSM approach was the output graphs with many small pixels. Accordingly, input variables changes and their influences on the OEE are more locational and visible. Moreover, the RSM model accurately predicted the OEE as 35.216% with the desirability of 0.843 at the optimum state as paraplow tine, 30 cm depth, 2.07 km/h forward speed, and 0.01 kg vertical load. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting is the key foundation and important premise of urban power system planning. Considering that urban power load demand is closely related to economic development and meteorological conditions, and with the application and promotion of various energy coupling equipment and the promotion of electric energy substitution policy, the relationship between gas load and power load demand is increasing. However, most of the existing load forecasting methods only consider a single economic factor or a single meteorological factor, and fail to combine the coupling and complementarity between loads. Based on this, this paper first analyzes the coupling characteristics between power load and urban economic, meteorological, and gas load with Copula theory; Secondly, the key parameters of the least squares support vector machine (LSSVM) are solved by using the salp swarm algorithm (SSA), and the urban monthly power load forecasting model based on SSA-LSSVM is established; Finally, based on the macroeconomic data, meteorological observation data and monthly electrical load of a city in North China, the prediction model is verified. Through comparison with different prediction scenarios, it is confirmed that the built model can effectively improve the prediction accuracy and has good application effects. [ABSTRACT FROM AUTHOR]

The economic growth in the new normal is no longer limited to the total amount and scale of economic growth in the traditional and neoclassical periods, but has changed to "quality" and "development" under the dual requirements of historical changes and tasks of the times. The quality of regional economic growth is an important part of the quality of China's economic development and an important part of the quality of China's economic development in the new era. Therefore, this paper proposes a BP neural network based on visual recognition in a regional economic prediction model and conducts application experiments. This regional economic forecasting model is relying on data technology for economic panel data mining, then graphical processing of panel data, followed by the selection of visual recognition technology for economic panel map analysis, to derive its various component coefficients, and finally then using the BP neural network to fit the prediction, at the same time, through long-term and short-term prediction, to predict the future development quality of each region's change trends and fluctuations, to predict the institution's role, so as to avoid major transitions and deteriorating alarms, and to provide support for the macroregulation of regional economic development quality. [ABSTRACT FROM AUTHOR]

Governments have implemented different interventions and response models to combat the spread of COVID-19. The necessary intensity and frequency of control measures require us to project the number of infected cases. Three short-term forecasting models were proposed to predict the total number of infected cases in Canada for a number of days ahead. The proposed models were evaluated on how their performance degrades with increased forecast horizon, and improves with increased historical data by which to estimate them. For the data analyzed, our results show that 7 to 10 weeks of historical data points are enough to produce good fits for a two-weeks predictive model of infected case numbers with a NRMSE of 1% to 2%. The preferred model is an important quick-deployment tool to support data-informed short-term pandemic related decision-making at all levels of governance. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation is greatly affected by weather factors. To improve the prediction accuracy of photovoltaic power generation, complete ensemble empirical mode decomposition with an adaptive noise algorithm (CEEMDAN) is proposed to preprocess the power sequence. Then, the full convolutional network (FCN) model optimized based on the sparrow search algorithm (SSA) is used to predict the short-term photovoltaic power. SSA can more reasonably determine the parameters of FCN and improve the prediction performance of FCN. Therefore, the FCN model optimized by the SSA algorithm is used to establish prediction models for subsequences and predict each subsequence, respectively. Finally, the predicted value of each subsequence is superimposed. Taking the actual data of a photovoltaic power station in Jiangsu province of China as an example, by comparing some different common prediction models, it is proved that the proposed method is reasonable and feasible. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of medium and long-term load forecasting in the new normal economy, this paper combines the PSO-optimized twin support vector machine to build a medium and long-term load forecasting model under the background of the new normal economy. Moreover, this paper uses the algorithm to debug the filter and studies the accurate prediction of the fine model by the progressive spatial mapping algorithm and the processing of the data by the vector fitting algorithm. In addition, this paper combines the PSO algorithm to optimize the twin support vector machine, constructs the optimized algorithm according to the flow chart, and applies it to the medium and long-term load forecasting under the background of a new normal economy. The experimental results verify that the PSO-optimized twin support vector machine has a very good application effect in medium and long-term load forecasting under the background of the new normal economy. [ABSTRACT FROM AUTHOR]

With the continuous development of the smart grid, rational power planning can greatly increase economic benefits. The power load is affected by temperature, humidity, and other factors, which presents typical nonlinear and random characteristics. Therefore, a combined prediction model based on grey model GM (1, N) and BP neural networks is proposed, where GM is improved by weighting the original data, selecting appropriate initial conditions and self-adaptive optimizing model parameters, while PSO algorithm is used to automatically optimize the global optimization. Finally, an experiment is carried out based on the actual power load data of a certain area, and the results show that the proposed method realizes higher accuracy in load prediction, and has a stronger generalization ability for small samples and low SNR conditions. [ABSTRACT FROM AUTHOR]

Power system inertia is declining and is increasingly variable and uncertain in regions where the penetration of non-synchronous generation and interconnectors is growing. This presents a challenge to power system operators who must take appropriate actions to ensure the stability and security of power systems relying on short-term forecasts of the system’s inertial response. Existing models to forecast inertia fail to quantify uncertainty, which may prevent their utilization given the risk aversion of the system operators when handling stability issues. This paper is the first to develop a model to produce calibrated, data-driven probabilistic forecasts of the inertia contribution of transmission-connected synchronous generators. The model provides a necessary tool for system operators to quantify forecast uncertainty, allowing them to manage the risk of frequency instability cost-effectively. The paper demonstrates that the assumption of a Gaussian distribution of uncertainty applied in existing models is not acceptable to accurately forecast the inertial response and provides a satisfactory forecast model by combining non-parametric density forecasting with parametric tail distributions. Moreover, the paper shows that satisfactory predictive performance can only be achieved by adopting a rolling horizon forecast approach to deal with the rapidly changing characteristics of the inertial response in power systems. [ABSTRACT FROM AUTHOR]

The chaotic nature of wind speed will damage power system seriously, and cause economic losses. Therefore, timely wind prediction is crucial for the safety of power system. However, the traditional prediction method is hard to fully learn the characteristic of wind speed. This paper proposes an optimal component IGSCV-SVR ensemble model to predict ultra-short-term wind speed. It changes the traditional single parameter optimization method of time series prediction. Firstly, the VMD based component correlation is applied to decomposing the original wind speed dataset to obtain multiple subsequences. Our model can find the dissimilarity of each subsequence, and then the model fully learns the feature of each subsequence. It can help improve the overall efficiency of ultra-short-term wind speed prediction accuracy. Finally, estimates are obtained by summing the prediction of all components. The case study proves the feasibility of our method through the comparative experiments with some previous prediction models in MSE, MAE, MAPE and running time in the experimental part of this paper. [ABSTRACT FROM AUTHOR]

Power load forecasting is becoming increasingly important as power system management on offshore platforms flourishes in modernization. When forecasting the power load, increasing the forecasting step causes the forecasting error to increase. This paper proposes the VMD-Prophet-Seq2seq model, which performs multistep prediction on single-feature original data. First, the model uses the variational modal decomposition (VMD) method, which decomposes the original power load sequence into multiple integral mode functions (IMFs). Correspondingly, the Prophet method decomposes the original power load series into subsequences such as 'trend' and 'daily'. We process each IMF and subsequence separately into independent supervised-type data. Then, we encode each supervisor-type data with Seq2seq's encoder and output a corresponding number of subcoding vectors. Finally, the decoder sums these subcoding vectors and decodes the summed coding vectors to obtain the forecasting sequence. The experimental results show that the mean absolute percentage error (MAPE) value is at most 1.78% lower than that of the suboptimal model for a prediction length of 30. [ABSTRACT FROM AUTHOR]

Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy and ensure timely feedback to perform reconfiguration of the distribution network. In this context, edge computing is expected to be an enabling technology to ensure decentralized data collection, management, processing and delivery. At the same time, federated learning is an emerging paradigm that fits naturally in such an edge-computing environment, providing an AI-powered and privacy-preserving solution for time-series forecasting. In this paper, we present a performance evaluation of different federated-learning configurations resulting in different privacy levels to the forecast residential energy consumption with data collected by real smart meters. To this aim, different experiments are run using Flower (a popular federated learning framework) and real energy consumption data. Our results allow us to demonstrate the feasibility of such an approach and to study the trade-off between data privacy and the accuracy of the prediction, which characterizes the quality of service of the system for the final users. [ABSTRACT FROM AUTHOR]

The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Maintaining stability and control over the electric system requires increasing information about the consumers' profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. Nonetheless, predicting the profile of an individual consumer is a difficult task. The main challenge lies in the uncertainty related to the individual consumption profile, which increases forecasting errors. Thus, this paper aims to implement a load predictive model focused on individual consumers taking into account its randomness. For this purpose, a methodology is proposed to determine and select predictive features for individual STLF. The load forecasting of an individual consumer is simulated based on the four main machine learning techniques used in the literature. A 2.73% reduction in the forecast error is obtained after the correct selection of the predictive features. Compared to the baseline model (persistent forecasting method), the error is reduced by up to 19.8%. Among the techniques analyzed, support vector regression (SVR) showed the smallest errors (8.88% and 9.31%). [ABSTRACT FROM AUTHOR]

Accurate prediction models enable the efficacious utilization and integration of solar energy into the power system. Therefore, this study aims to develop novel hybrid prediction models by employing correlation analysis (CA), decomposition techniques, sample entropy (SE), and spatio-temporal attention (STA) based sequence2sequence (S2S) algorithm for accurate prediction of global horizontal irradiance (GHI). The decomposition techniques include variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and maximum overlap discrete wavelet transform (MODWT). The VMD-STA-S2S hybrid model surmounts the associated hybrid and standalone prediction models by revealing the highest prediction efficiency and the lowest error. Compared to the SARIMAX, SVR, ANN, XGB, GRU, LSTM, and S2S models, the VMD-STA-S2S model reduced RMSE during testing by 80.927 W/m2, 75.426 W/m2, 73.487 W/m2, 62.394 W/m2, 57.811 W/m2, 52.007 W/m2, and 41.836 W/m2, respectively. Similarly, the reductions in RMSE by VMD-STA-S2S model compared to SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models are 24.054 W/m2, 20.951 W/m2, 12.702 W/m2, 15.396 W/m2, 9.921 W/m2, 6.103 W/m2 and 0.484 W/m2, respectively, during testing. Furthermore, considering NSE during testing, the VMD-STA-S2S model is 8.66%, 7.72%, 7.41%, 5.71%, 5.07%, 4.31%, 3.11%, 1.43%, 1.19%, 0.64%, 0.81%, 0.47%, 0.35% and 0.07%, more efficient than the SARIMAX, SVR, ANN, XGB, GRU, LSTM, S2S, SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models, respectively. The superior performance of VMD-STA-S2S over its counterparts corroborates the integration of the VMD technique and STA-based S2S algorithm for GHI prediction. The multivariate meteorological data of this study is decomposed by VMD into subcomponents more effectively than the ICEEMDAN and MODWT techniques. VMD decomposed subcomponents are further fed to the STA-S2S to efficiently extract and learn the spatial and temporal features, resulting in the enhanced and superior prediction outcomes of the VMD-STA-S2S model compared to all the counterpart models. Besides GHI prediction, the proposed model is also appropriate for other time-series data, including renewable energy, electrical load, and environment monitoring. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate ultra‐short‐term wind speed prediction is extremely important for the power control of wind farms, the safe dispatch of power systems, and the stable operation of power grids. At present, most wind farms mainly rely on supervisory control and data acquisition systems to obtain operation and maintenance data which includes operating characteristics of wind turbines. In the ultra‐short‐term wind speed prediction, a long short‐term memory network is one of the commonly used deep learning methods. To address the problem that improper selection of long short‐term memory network's hyperparameters may affect the prediction results, In the present study, a hybrid prediction model based on the long short‐term memory and the modified tuna swarm optimization algorithm was established, and was used to predict after the wind speed sample data had been decomposed by successive variational mode decomposition method. The experimental results reveal that the proposed model effectively improved the accuracy of wind speed prediction for wind farms compared with the support vector regression, deep belief networks, and long short‐term memory models optimized by particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]