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As wind energy development increases, accurate wind energy forecasting helps to develop sensible power generation plans and ensure a balance between supply and demand. Machine-learning-based forecasting models possess exceptional predictive capabilities, and data manipulation prior to model training is also a key focus of this research. This study trained a deep Long Short-Term Memory (LSTM) neural network to learn the processing results of the Savitzky-Golay filter, which can avoid overfitting due to fluctuations and noise in measurements, improving the generalization performance. The optimum data frame length to match the second-order filter was determined by comparison. In a single-step prediction, the method reduced the root-mean-square error by 3.8% compared to the model trained directly with the measurements. The method also produced the smallest errors in all steps of the multi-step advance prediction. The proposed method ensures the accuracy of the forecasting and, on that basis, also improves the timeliness of the effective forecasts. [ABSTRACT FROM AUTHOR]

The randomness and instability of wind power bring challenges to power grid dispatching. Accurate prediction of wind power is significant to ensure the stable development of power grid. In this paper, a new ultra-short-term wind power forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short-term memory (LSTM) network optimized by improved whale optimization algorithm (IWOA) is proposed. Firstly, CEEMDAN is applied to decompose the power history data into several intrinsic mode functions (IMFs) and a residual (RS) to reduce the complexity and unsteadiness of the original data. Then the partial autocorrelation method is used to analyze and select the input variables of each IMF and the residual. Finally, the IWOA-LSTM prediction model is established, and the parameters of LSTM are optimized by using the improved whale optimization algorithm. Each IMF and the residual are predicted respectively. The prediction results are superimposed to obtain the final wind power prediction value. The hybrid model is applied to the ultra-short-term wind power prediction of a wind farm in northern China. The prediction results of comparison experiments with other 11 models prove the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimized Deep Learning (DL) network design for time series load forecasting. At first, DL's hyper parameters are optimized using the Levy flight-particle swarm optimization (LF-PSO) technique; then, the optimized DL model is used for load prediction. Furthermore, the results are compared with the existing state-of-the-art techniques to show prediction accuracy. Experiment and measured values indicate that the proposed new DL model is highly efficient for load prediction. [ABSTRACT FROM AUTHOR]

In the power system operational/planning studies, it is a crucial task to provide the load consumption information in the look‐ahead times. The huge variation of the power system infrastructure in recent years has led to significant changes in the consumers' consumption pattern. Therefore, short‐term load forecasting (STLF) is transformed to a more complicated problem in recent years. To address this issue, this paper proposes a graph‐based deep neural network to capture full spatial‐temporal features and be able to oversee high volatility time series including load sequence. The proposed spatial deep learning structure benefits from learning the spatial feature using Gabor filter‐oriented layers and full understanding the temporal behaviour based on bidirectional networks. The designed learning‐based system is developed as a graph‐based learning system to improve the accuracy considering the meteorological information behaviour. To verify the performance of the designed deep graph network, the actual load data of Shiraz, Iran, is used. Besides, to demonstrate the superiority and effectiveness of the proposed, the designed deep graph network is compared with three well‐known shallow and deep networks in different cases including yearly performance, seasonal performance, and sensitivity analysis on the meteorological data. [ABSTRACT FROM AUTHOR]

• Examines resources allocation issue for minimizing the solar power usage in fog computing-enabled wireless IoTs using NOMA. • A typical case is considered to support several IoT devices for offloading computations together to the similar FN. • As a way of achieving an optimal efficiency-complexity balance, a whale optimization algorithm (WOA) is introduced. • The suggested layout is more efficient than current methods in terms of throughput, latency, blackout likelihood, and power usage. Fog computing is used to handle applications that require a lot of calculations at the network's edges for wireless Internet of Things (IoT) networks that have source-limited equipment. In addition to reducing computational time and fronthaul traffic information, fog computing still poses challenges in allocating the existing computing and communication sources, while maintaining the strict quality of service (QoS) standards. The current study examines the issue regarding task management and heterogeneous resource allocation among equipment for wireless IoT networks. As a means of transferring the information to the fog computing nodes (FNs), IoT devices collecting large amounts of information require appropriate offloading decisions. This paper investigates how non-orthogonal multiple access (NOMA) can be deployed for IoT networks in order to enable many devices for transmitting information of the load and the market to a similar FN simultaneously, in a similar frequency range, and in the same code domain. Several IoT devices are optimized together based on their resource allocation and transmission power in this paper while meeting the QoS standards. Additionally, the optimization problem can be expressed in the form of a mixed-integer nonlinear programming problem with the goal of minimizing the system's power usage. The problem would be NP-hard so a whale optimization algorithm (WOA) has been proposed for solving. According to simulation outcomes, the suggested architecture performs well in terms of throughput, delays, blackout likelihood, and power usage. [ABSTRACT FROM AUTHOR]

• A combined approach is proposed according to MMI, deep learning (FCRBM), and MTLA to forecast short-term load quickly. • A FCRBM-enabled prediction and training model is developed for increasing the accuracy. • A digital twin concept is developed to handle the problem complexities. Because users behave randomly and in a nonlinear way, predicting electrical loads proves to be a difficult procedure. Due to the development of the smart grid (SG) and advanced metering infrastructure (AMI), humans will be capable of recording, monitoring, and analyzing these non-linear behaviors. The use of electric load projection layouts is a necessity in order to make decisions, plan, and evaluate contracts in electrical systems. Consequently, there have been several load prediction methods in the research that demonstrate trade-offs among prediction precision and runtime (convergence rate). The current paper presents a method for short-term load prediction of market in trade-by-trade data that would be quick and precise. Modified mutual information (MMI) is used to extract abstractive characteristics from historic information. Learning empowers the factored conditional restricted Boltzmann machine (FCRBM) for predicting the electrical loads. Ultimately, the efficiency has been optimized using the suggested modified teaching–learning algorithm (MTLA). The suggested architecture has the advantage of improving prediction precision and convergence rate. The MMI method and FCRBM layout improve prediction precision. In addition, MTLA has been used to enhance the convergence rates. Based on simulation outcomes, the suggested quick and precise layout performs better than conventional layouts when it comes to forecasting precision and convergence rates, including Bi-level, MI-artificial neural network (MI-ANN), and accurate fast converging short-term load forecast (AFC-STLF). [ABSTRACT FROM AUTHOR]

Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations' data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic ensemble method is proposed to forecast the residential short-term load accurately. The main idea is to utilize the state-space approaches to dynamically adjust the weight coefficients used to combine the base models. The dynamic ensemble method is divided into two stages. In the first stage, the least square method is utilized to estimate the weight coefficients. In the second stage, the particle filter is employed to exclude the estimation error in the first stage and update the weight coefficients dynamically to improve the ensemble accuracy. Then, three heterogeneous models (i.e., vector autoregression model, Gaussian process regression model, and the long short-term memory neural network) are employed as the base models and combined based on the weight coefficients to forecast the residential load. The numerical tests are conducted considering two scenarios in two public datasets to investigate the performance of the proposed method. The first scenario assumes the base models are accurate and stable, while the second scenario considers the dynamics of the base models. Root mean square error, mean absolute error, and mean absolute percentage error are used to evaluate the performances. These indicators show that the proposed method outperforms in both scenarios and datasets. [ABSTRACT FROM AUTHOR]

Load forecasting is one of the main concerns for power utility companies. It plays a significant role in planning decisions, scheduling, operations, pricing, customer satisfaction, and system security. This helps smart utility companies deliver services more efficiently and analyze their operations in a way that can help optimize performance. In this paper, we propose a study of different techniques: multiple linear regression (MLR), random forests (RF), artificial neural networks (ANNs), and automatic regression integrated moving average (ARIMA). This study used electricity consumption data from Dubai. The main objective was to determine the load demand for the next month in the whole country and different municipal areas in Dubai, as well as to assist a utility company in future system scaling by adding new power stations for high-demand regions. The results showed that the accuracy of using ARIMA was about 93% when working with only a single district, but both ANN and RF achieved excellent accuracy of about 97% in all cases. In addition, the mean absolute percentage errors improved from 2.77 and 2.17 to 0.31 and 0.157 for ANN and RF, respectively, after anomaly elimination and the use of our proposal. Therefore, the use of an ANN for such data types is recommended in most cases, particularly when working on a complete dataset. Additionally, both the ANN and RF models are good choices when working on a single-category region because they both attained the same accuracy of almost 91.02 percent. [ABSTRACT FROM AUTHOR]

Daily load forecasting is the basis of the economic and safe operation of a power grid. Accurate prediction results can improve the matching of microgrid energy storage capacity allocation. With the popularization of smart meters, the interaction between residential electricity demand and sources and networks is increasing, and massive data are generated at the same time. Previous forecasting methods suffer from poor targeting and high noise. They cannot make full use of the important information of the load data. This paper proposes a new framework for daily load forecasting of group residents. Firstly, we use the singular value decomposition to address the problem of high dimensions of residential electricity data. Meanwhile, we apply a K-Shape-based group residential load clustering method to obtain the typical residential load data. Secondly, we introduce an empirical mode decomposition method to address the problem of high noise of residential load data. Finally, we propose a Bi-LSTM-Attention model for residential daily load forecasting. This method can make full use of the contextual information and the important information of the daily load of group residents. The experiments conducted on a real data set of a power grid show that our method achieves excellent improvements on five prediction error indicators, such as MAPE, which are significantly smaller than the compared baseline methods. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is a significant component of safe and stable operations and economical and reliable dispatching of power grids. Precise load forecasting can help to formulate reasonable and effective coordination plans and implementation strategies. Inspired by the spiking mechanism of neurons, a nonlinear spiking neural P (NSNP) system, a parallel computing model, was proposed. On the basis of SNP systems, this study exploits a fresh short-term load forecasting model, termed as the LF-NSNP model. The LF-NSNP model is essentially a recurrent-like model, which can effectively capture the correlation between the temporal features of the electric load sequence. In an effort to validate the effectiveness and superiority of the proposed LF-NSNP model in short-term load forecasting tasks, tests were conducted on datasets of different time and different variable types, and the predictive competence of various baseline models was compared. [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]

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively. [ABSTRACT FROM AUTHOR]

In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term PV forecasting technique using Delaunay triangulation, of which the vertices are three weather stations that enclose a target PV site. By leveraging a Transformer encoder and gated recurrent unit (GRU), the proposed TransGRU model is robust against weather forecast error as it learns feature representation from weather data. We construct a framework based on Delaunay triangulation and TransGRU and verify that the proposed framework shows a 7–15% improvement compared to other state-of-the-art methods in terms of the normalized mean absolute error. Moreover, we investigate the effect of PV aggregation for virtual power plants where errors can be compensated across PV sites. Our framework demonstrates 41–60% improvement when PV sites are aggregated and achieves as low as 3–4% of forecasting error on average. [ABSTRACT FROM AUTHOR]

Deep learning techniques excel at capturing and understanding the symmetry inherent in data patterns and non-linear properties of photovoltaic (PV) power, therefore they achieve excellent performance on short-term PV power forecasting. In order to produce more precise and detailed forecasting results, this research suggests a novel Autoformer model with De-Stationary Attention and Multi-Scale framework (ADAMS) for short-term PV power forecasting. In this approach, the multi-scale framework is applied to the Autoformer model to capture the inter-dependencies and specificities of each scale. Furthermore, the de-stationary attention is incorporated into an auto-correlation mechanism for more efficient non-stationary information extraction. Based on the operational data from a 1058.4 kW PV facility in Central Australia, the ADAMS model and the other six baseline models are compared with 5 min and 1 h temporal resolution PV power data predictions. The results show in terms of four performance measurements, the proposed method can handle the task of projecting short-term PV output more effectively than other methods. Taking the result of predicting the PV energy in the next 24 h based on the 1 h resolution data as an example, MSE is 0.280, MAE is 0.302, RMSE is 0.529, and adjusted R-squared is 0.824. [ABSTRACT FROM AUTHOR]

• Short-term electricity generation data is used to calculate PGF for Khartoum. • Peaks over threshold method is utilized to model the highest power generation. • The obtained PGF equals 4, whereas in PV potential map equals 4.8. • This factor shall facilitate PV system sizing for local engineers. In this research, the authors used the Peaks over Threshold (POT) method alongside short-term electricity generation data belonging to a 5.5 kW p off-grid photovoltaic (PV) system installed on the premises of the National Energy Research Center in Soba district, Khartoum, to estimate the panel generation factor (PGF) of the city that rests within Sudan's capital. PGF is a fundamental technical figure primarily used in the design/sizing of solar PV systems. The addressed problem here is to calculate this coefficient with an acceptable accuracy constrained by insufficient electricity generation data from a PV system operating at its maximum capacity, relying on statistical modeling methods, which is the research novelty. Accordingly, we obtained an approximate PGF of 4 utilizing the POT technique that resulted in a threshold of 19 and Generalized Pareto distribution estimators of 2.2 × 10 - 16 and 2.3 for the shape and scale parameters, respectively. In the literature, the corresponding factor value is 4.8, illustrated in Sudan's PV potential map, based on historical long-term solar irradiance satellite records. Consequently, a 16.67% Percent Error between the two values is present due to the big difference in data amount, favoring the literature. However, in front of relatively abundant data, we believe the Extreme Value Theory methodology applied here will demonstrate robustness in approximating the target factor digits and validating Sudan's map numbers. Finally, this effort shall enable local engineers to blueprint well-assessed PV systems and support the endeavors to scientifically document Sudan's PV energy capacity, which the country lacks very much. [ABSTRACT FROM AUTHOR]

Short-term forecasting of direct normal irradiance (DNI), which refers to photons that did not interact with the atmosphere on their way to the observer, is of great interest in the advanced management of concentrated solar power systems. So, this paper exhibits a hybrid model dedicated to the intrahour forecasting of DNI (horizons are 5, 10, and 15 min). This hybrid model combines a knowledge-based model with a machine learning model: the knowledge-based model is used for clear-sky DNI forecasting from DNI measurements; the machine learning model evaluates the impact of the atmospheric disturbances on the solar resource, through the processing of high dynamic range sky images provided by a ground-based camera. The hybrid model is evaluated by comparing its performance, for different sky conditions, with that of two machine learning models based on past DNI observations only. The results highlight the pertinence of combining knowledge-based models with data-driven models, and of integrating sky-imaging data in the DNI forecasting process: the proposed hybrid model successfully manages clear-sky, overcast, and mixed situations. Regardless of the forecast horizon, the hybrid model outperforms the reference models both in normalized root mean squared error and in DNI ramp detection. • Short-term forecasting of direct normal irradiance (DNI) is obtained using a hybrid model, using both DNI measurements and ground-based sky imagery. • This hybrid model is compared to the smart persistence model and two machine learning models. • The results highlight the pertinence of combining knowledge-based models with data-driven models, and of integrating sky-imaging data. • Regardless of the forecast horizon, the hybrid model outperforms reference models, both in root mean squared error and in DNI ramp detection. [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]

Real‐time monitoring and accurate prediction of photovoltaic (PV) power generation operation parameters are essential to ensure stable operation. In this paper, a set of online PV power generation parameter measurement and monitoring devices characterized by simple structure, high sampling accuracy, small data fluctuations, and ease of measurement, are designed. Sensors based on the zero‐flux principle are employed in the real‐time collection of the output electrical signals in the process of PV power generation, realizing the accurate collection of electric parameter signals. Next, the basic structure and working principle of PV cells are analyzed, a mathematical model of PV cells for engineering purposes is established, a wavelet neural network is selected to predict the short‐term PV power generation, and particle swarm optimization and adding momentum are used to optimize the weight of wavelet neural network (WNN) as well as the parameters of the wavelet basis function. Finally, the historical power generation data and meteorological data of the power station are taken as the training samples to train and simulate the prediction sub‐models of different weather types to verify the effectiveness and accuracy of the PV power generation short‐term prediction model for optimizing the WNN based on the particle swarm optimization algorithm. The research results of this paper can realize real‐time monitoring of the output parameters and accurate prediction and evaluation of power generation during the operation of the PV‐power‐generation system. [ABSTRACT FROM AUTHOR]

Smart grid is an advanced electrical grid that enables more efficient distribution of electricity. It counters many of the problems presented by renewable energy sources such as variability in production through techniques like load forecasting and dynamic pricing. Smart grid generates massive amounts of data through smart meters, this data is used to forecast future load to adjust distribution. To process all this data, big data analysis is necessary. Most existing schemes use Apache Hadoop for big data processing and various techniques for load forecasting that include methods based on statistical theory, machine learning and deep learning. This paper proposes using Apache Spark for big data analysis and a modified version of the transformer model for forecasting load profiles of households. The modified transformer model has been tested against several state-of-the-art machine learning models. The proposed scheme was tested against several baseline and state-of-the-art machine learning models and evaluated in terms of the RMSE, MAE, MedAE and R2 scores. The obtained results show that the proposed model has better performance in terms of RMSE and R2 which are the preferred metrics when evaluating a regression model on data with a large number of outliers. [ABSTRACT FROM AUTHOR]

Context. The current stage of development of the electric power industry is characterized by an intensive process of microgrid development and management. The feasibility of using a microgrid is determined by the fact that it has a number of advantages compared to classical methods of energy generation, transmission, and distribution. It is much easier to ensure the reliability of electricity supply within the microgrid than in large energy systems. Energy consumers in a microgrid can affect the power balancing process by regulating their loads, generating, storing, and releasing electricity. One of the main tasks of the microgrid is to provide consumers with electrical energy in a balance between its generation and consumption. This is achieved thanks to the intelligent management of the microgrid operation, which uses energy consumption forecasting data. This allows to increase the efficiency of energy infrastructure management. Objective. The purpose of this work is to develop short-term electricity consumption forecasting models for various types of microgrid electricity consumers, which will improve the efficiency of energy infrastructure management and reduce electricity consumption. Method. The SARIMA autoregressive model and the LSTM machine learning model are used to obtain forecast values of electricity consumption. AIC and BIC information criteria are used to compare autoregressive models. The accuracy of forecasting models is evaluated using MAE, RMSE, MAPE errors. Results. The experiments that forecast the amount of electricity consumption for the different types of consumers were conducted. Forecasting was carried out for both LSTM and AR models on formed data sets at intervals of 6 hours, 1 day, and 3 days. The forecasting results of the LSTM model met the forecasting requirements, providing better forecasting quality compared to AR models. Conclusions. The conducted study of electricity consumption forecasting made it possible to find universal forecasting models that meet the requirements of forecasting quality. A comparative analysis of developed time series forecasting models was performed, as a result of which the advantages of ML models over AR models were revealed. The predictive quality of the LSTM model showed the accuracy of the MAPE of forecasting electricity consumption for a private house – 0.1%, a dairy plant – 3.74%, and a gas station – 3.67%. The obtained results will allow to increase the efficiency of microgrid management, the distribution of electricity between electricity consumers to reduce the amount of energy consumption and prevent peak loads on the power grid. [ABSTRACT FROM AUTHOR]

Probabilistic forecasting can provide quantitative and comprehensive information about future wind speed uncertainty, which exhibits increasing importance for the vital integration of wind power into power systems. However, many conventional forecasting models cannot better handle the uncertainty generated by the intermittent and stochastic nature of wind patterns. This paper develops a hybrid versatile forecasting framework that presents point estimation and different forms of probabilistic wind speed forecasting, such as interval prediction and probability density prediction. In this proposed model, the hybrid of empirical wavelet transform and neural network-based quantile regression is proposed to improve the generalization and robustness of probabilistic forecasts and to capture the probabilistic meaning output of wind speed forecasts. To further emphasize the importance of probabilistic forecasting, an adaptive density estimation method based on the Gaussian kernel function as determined by the firefly algorithm is developed to generate more predictive probability information. Wind datasets from three sites in Penglai, China, are used to validate the proposed approach. The experimental results demonstrate that the developed probabilistic wind speed forecasting model can provide value and comprehensive information about future wind speed and is a promising tool for the decision-making process in wind power operation and management. [ABSTRACT FROM AUTHOR]

In this paper, a Median Grid Search algorithm based on the median values is proposed to develop a more accurate algorithm used to investigate the optimal hyperparameter of the SVR model for load forecasting. In addition, the methodology to benchmark the proposed Grid Search and the conventional Grid Search is built and sufficiently utilized. The data gathered from the South Australia state, Australia and Ho Chi Minh City load demands are used in experiments. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithm. [ABSTRACT FROM AUTHOR]

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Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term load forecasting framework (DCNN-LSTM-AE-AM) based on deep learning, which combines dilated convolutional neural network (DCNN), long short-term memory network (LSTM), autoencoder (AE), and attention mechanism (AM) to improve the prediction results. First, we design a T-nearest neighbors (TNN) algorithm to preprocess the original data. Further, a DCNN is introduced to extract the long-term feature. Secondly, we combine the LSTM with the AE (LSTM-AE) to learn the sequence features hidden in the extracted features and decode them into output features. Finally, the AM is further introduced to extract and fuse the high-level stage features to achieve the prediction results. Experiments on two real-world datasets show that the proposed method is good at capturing the oscillation characteristics of low-load data and outperforms other methods. [ABSTRACT FROM AUTHOR]

Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting. [ABSTRACT FROM AUTHOR]

This study examines a new approach for short‐term wind speed and power forecasting based on the mixture of Gaussian hidden Markov models (MoG‐HMMs). The proposed approach focuses on the characteristics of wind speed and power in the consecutive hours of previous days. The proposed method is carried out in two steps. In the first step, for the hourly prediction of wind speed, several wind speed features are employed in MoG‐HMM, and in the second step, the results obtained from the first step along with their characteristics and wind power features are used to predict wind power estimation. To increase the prediction accuracy, the data used in each step are classified, and then for each class, one HMM with its specific parameters is used. The performance of the proposed approach is examined using real NREL data. The results show that the proposed method is more precise than other examined methods. [ABSTRACT FROM AUTHOR]

Reliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub‐predictor selection rule, and a multi‐objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches. Highlights: A novel combined system is proposed to conduct point and interval forecasting.Sub‐model selection scheme is used to choose component sub‐predictors.A new multi‐objective optimization algorithm is proposed to optimize weights.Reduction coefficient is proposed to improve interval forecasting performance.Theoretical proof and experiments verify the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with "day as covariates" remained better than the 1, 2, 3, and 4-week scenarios. [ABSTRACT FROM AUTHOR]

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena. [ABSTRACT FROM AUTHOR]

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTechLAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions. [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]

The traffic flow forecasting proposed for a series of problems, such as urban road congestion and unreasonable road planning, aims to build a new smart city, improve urban infrastructure, and alleviate road congestion. The problems encountered in traffic flow forecasting are also relatively difficult; the reason is that traffic flow forecasting is uncertain, dynamic, and nonlinear. It is challenging to build a reliable and safe model. Aiming at this complex and nonlinear traffic flow forecasting problem, this paper proposes a solution of an ABC-ELM model optimized by an artificial bee colony algorithm to solve the above problem. It uses the characteristics of the artificial bee colony algorithm to optimize the model so that the model can better and faster find the optimal solution in space. Moreover, it also uses the characteristics of the limit learning machine to quickly deal with this nonlinear specific problem. Experimental results on the Amsterdam road traffic flow dataset show that the traffic flow prediction model proposed in this paper has higher prediction accuracy and is more sensitive to data changes. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system. [ABSTRACT FROM AUTHOR]

Building electricity load forecasting plays an important role in building energy management, peak demand and power grid security. In the past two decades, a large number of data-driven models have been applied to building and larger-scale energy consumption predictions. Although these models have been successful in specific cases, their performances would be greatly affected by the quantity and quality of the building data. Moreover, for older buildings with sparse data, or new buildings with no historical data, accurate predictions are difficult to achieve. Aiming at such a data silos problem caused by the insufficient data collection in the building energy consumption prediction, this study proposes a building electricity load forecasting method based on a similarity judgement and an improved TrAdaBoost algorithm (iTrAdaBoost). The Maximum Mean Discrepancy (MMD) is used to search similar building samples related to the target building from public datasets. Different from general Boosting algorithms, the proposed iTrAdaBoost algorithm iteratively updates the weights of the similar building samples and combines them together with the target building samples for a prediction accuracy improvement. An educational building's case study is carried out in this paper. The results show that even when the target and source samples belong to different domains, i.e., the geographical location and meteorological condition of the buildings are different, the proposed MMD-iTradaBoost method has a better prediction accuracy in the transfer learning process than the BP or traditional AdaBoost models. In addition, compared with other advanced deep learning models, the proposed method has a simple structure and is easy for engineering implementation. [ABSTRACT FROM AUTHOR]

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers' performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier "RF+SVM+DT" has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling. [ABSTRACT FROM AUTHOR]

Predictors of magnetoionospheric disturbances occurring in phenomena in the Sun and in the solar wind and allowing one to forecast the probability of the occurrence of magnetoionospheric disturbances are described. In the study of the relation between ionospheric and magnetic storms, an empirical dependence of the beginning, intensity, and duration of ionospheric disturbances on magnetic ones is established. Methods of global representation of spatiotemporal variations in parameters of the F2 layer during disturbances with the use of both empirical and physical models are considered. The possibility of using a simplified single-ion model consisting of a system of two differential equations for NmF2 and hmF2 in short-term forecasting of maximum parameters of the F2 layer of the middle-latitude ionosphere is demonstrated. The input parameters of the model are the coordinates, solar and magnetic activity, as well as prediction of the beginning, type, and intensity of the disturbance. Correction in the forecast is performed by the drift velocity and recombination coefficient according to current observations. The materials accumulated by this model are compared with forecasts using the empirical model constructed in the Institute of Terrestrial Magnetism, Ionosphere, and Radiowave Propagation, Russian Academy of Sciences, by identifying regular disturbed variations. It is shown that the accuracies of these models are comparable. Based on statistical analysis of histograms of δ foF2, it is shown that the distribution law for the population of δ foF2 during disturbances is far from normal. A method of interpreting experimental probability density functions by the model with kurtosis and skewness constructed based on the Poisson random process both for data with an hourly resolution and by more frequent 5-min observations is proposed. The main stages of the further development of short-term ionospheric forecasting based on numerical theoretical models of the ionosphere are proposed. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to determine the most suitable method for electricity price forecasting in the Turkish day ahead electricity market and to test the selected method using real data. In order to forecast the electricity price, forecasting models were created in Python programming language with four different forecasting methods: linear regression, polynomial regression, artificial neural networks, XGBoost analysis method It is aimed that models can make predictions with low deviations, react quickly to short-term changes in price, and have short running times. Models were trained and tested with real data obtained from the Energy Markets Operations (EPİAŞ) Transparency Platform. The data used for analysis is hourly Market Clearing Price (MCP) data and hourly energy production data for each electricity generation source. The data used is hourly data covering the years 2015-2020 and is a large dataset consisting of approximately 40,000 rows. The test data used in the methods were randomly selected from five years of data to ensure a homogeneous distribution. Considering the dynamic structure of the Turkish electricity energy market, actual values and estimated values are compared both graphically and with the mean square error rates (RMSE) metric for four forecasting methods. In addition, the four forecasting methods were compared in terms of running times. When both estimation error rates and running times are evaluated together, XGBoost model was found to be the most appropriate estimation model. Making consistent price estimations will enable both electricity producers and large-capacity consumers to provide accurate supply offers and demand bids and to determine electricity prices precisely within the electricity market structure. [ABSTRACT FROM AUTHOR]

The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data. [ABSTRACT FROM AUTHOR]

An intelligent transport system (ITS) is fully valuable only if it can dynamically and aptly integrate all the latest cutting‐edge technologies. An ITS focuses on providing services like promptly offering real‐time road traffic information to interested parties, finding ways to reduce the average waiting time and offer secure and reliable services for commuters using past statistics. Short‐term traffic prediction is one such area in which the research community has focused in the past decade. Existing models developed for prediction has scope to improve in terms of accuracy and training time. There is a necessity to develop a best‐performing model that is computationally affluent to train with the optimal hyperparameter configuration as input which leads to improved performance. This article proposes a model that captures the traffic flow trend present in the past data to predict the flow for a future time interval. This model is an amalgamation of seasonal global trend (SGT) model and long short‐term memory (LSTM) model with attention mechanism. A novel hyperparameter tuning algorithm is also proposed which is based on multi‐armed bandit strategy with context, incorporating the right trade‐off between exploitation and exploration of the hyperparameter space using successive halving. Experimental results conducted proves that our forecast model in combination with the proposed hyperparameter tuning algorithm outperforms the existing models like SGT, LSTM models in terms of accuracy and time. [ABSTRACT FROM AUTHOR]

In this study, we aim to provide an efficient load prediction system projected for different local feeders to predict the Medium- and Long-term Load Forecasting. This model improves future requirements for expansions, equipment retailing or staff recruiting to the electric utility company. We aimed to improve ahead forecasting by using hybrid approach and optimizing the parameters of our models. We used Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Multilayer perceptron (MLP) and hybrid methods. We used Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and squared error for comparison. To predict the 3 months ahead load forecasting, the lowermost prediction error was acquired using LSTM MAPE (2.70). For 6 months ahead forecasting prediction, MLP gives highest predictions with MAPE (2.36). Moreover, to predict the 9 months ahead load forecasting, the highest prediction has been attained using LSTM in terms of MAPE (2.37). Likewise, ahead 1 years MAPE (2.25) was yielded using LSTM and ahead six years MAPE (2.49) was provided using MLP. The proposed methods attain stable and better performance for prediction of load forecasting. The finding indicates that this model can be better instigated for future expansion requirements. [ABSTRACT FROM AUTHOR]

Presently, the grid-connected scale from photovoltaic (PV) system is getting higher among renewable power generations. However, the PV output power can be affected by different meteorological conditions due to PV randomness and volatility. Accordingly, reasonable generation plans can be well arranged using accurate PV power prediction among various types of energy sources, thus reducing the effect of PV system on the grid. To resolve this problem, a PV output power prediction model, namely IMWOASVM, is proposed based on the combination of improved whale optimization algorithm (IMWOA) and support vector machine (SVM). The IMWOA is used to optimize the kernel function parameter and penalty coefficient in SVM. The optimal parameter and coefficient values can then be input to SVM for enhancing the PV prediction. The performance results verify that the coefficient of determination using the IMWOA model can reach beyond 99% in both sunny and cloudy days. Simultaneously, the mean absolute errors on sunny and cloudy days are 0.0251 and 0.0705, respectively. The root mean square errors in sunny and cloudy days are 2.17% and 1.03%, respectively. The results confirm that the proposed model effectively increases the accuracy of the PV output power prediction and is superior to existing methods. [ABSTRACT FROM AUTHOR]

With aggressive government policies driving the electric power systems around the globe towards high renewable energy penetration, methodologies for considering forecast uncertainties in power system analysis and planning are gaining relevance. Probabilistic load flow (PLF) is a useful tool to carry out risk-based power system analysis and planning. This article discusses the need and philosophy of Normal-transformations, elaborating on Nataf transformation and polynomial normal transformation (PNT), to solve PLF and presents a unified approach for representation. Through detailed statistical modeling, the Gaussian mixture (GM) model is found to fit the historical wind and load power forecast error data better compared to widely used parametric models. Sampling of correlated marginal distributions, for scenario generation in PLF, requires the quantile function of the GM distribution, which is not available in a closed expression. A strategy, using quantile functions of Gaussian component sampling and interpolation, to approximate the GM's quantile function is presented. The presented approximation enables the use of GM for solving PLF with correlated marginals, which is not addressed in the literature. This work also contributes to prevailing research questions by two new performance comparisons: between various higher-order PNT embedded to point estimate methods (PEMs) for PLF, and between PEM and Quasi-Monte Carlo (QMC) methods for PLF analysis. When PEMs are employed for PLF, there is no improvement observed in going for higher than third order PNT (3PNT). Results show that estimating the first four moments of the output variables through QMC-PLF is more accurate compared to PEM-PLF. Formulae useful for implementation of various higher-order PNT, for QMC and PEM, are derived and presented for direct use. Finally, the impact of correlated non-normal modeling of inputs on PLF output variables' distribution is demonstrated. [ABSTRACT FROM AUTHOR]

A reliable and accurate short-term load forecasting (STLF) helps utilities and energy providers deal with the challenges posed by supply and demand balance, higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Recent advances in deep learning have been successively utilized to STLF. However, there is no certain study that evaluates the performances of different STLF methods at an aggregated level on different datasets with different numbers of daily measurements. In this study, a deep learning STLF architecture called Load2Load is proposed for day-ahead forecasting. Different forecasting methods have been evaluated and compared on two datasets with different temporal resolutions and features. An additive ensemble method as well as a selective ensemble method that selects the outputs of different forecasters in an hourly manner are proposed. Moreover; a modified sequential forward feature selection algorithm is proposed, resulting in better performance with a much smaller number of features. Numerical results show that the proposed Load2Load architecture has a competing performance compared to other advanced forecasters. When used together with the proposed ensemble methods, Load2Load can significantly improve the forecasting performance. The proposed feature selection algorithm results in better performance for the majority of the cases while reducing the dimensionality. According to the results with two different datasets; the proposed methods are shown to be robust to temporal resolutions, feature types and sequence lengths. [ABSTRACT FROM AUTHOR]

Aiming at the problem of strong randomness and low forecasting accuracy in short-term electric load, a method based on empirical wavelet transform and random forest is proposed. In this method, the noise is extracted and stripped by wavelet transform, and the original data are decomposed into several groups of low or high frequencies. The empirical wavelet transform is more adaptive, and details are demonstrated accurately. These decomposed modes are used as characteristic variables to forecast with random forest. This method has three advantages: (1) Because of the instability of electric data, the empirical wavelet decomposition can be used to characterize the non-stationary signal characteristics. (2) Empirical wavelet transform has more advantages in time domain analysis because of its correlation to signal removal and the tendency of noise whitening. (3) More adaptive details with empirical mode (noise is no exception) are expressed in the random forest, and it still demonstrates accurate results even after data features are lost. The Australian electricity data in different periods are used for case analysis. The results compared with other methods have shown that the model could reveal effectively the influence of random noise and improve the accuracy and reliability of short-term load forecasting. [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]