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Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of multi-agent STLF due to the high heterogeneity, uncertainty and volatility, traditional local methods usually make predictions based on load aggregation or clustering, the complexity of which will rise greatly with the increase of agent types. It has become more and more difficult for traditional methods to meet the STLF demand of smart grid. Meanwhile, global time series forecasting method emerges gradually in many fields, which can make predictions for many agents only by one model with much lower complexity. However, there is few researches on the multi-agent global STLF. This study proposes a global deep-Long Short-Term Memory (LSTM) STLF model based on a pretraining method. The model fully applies the information such as electricity consumption pattern, weather and calendar in a structural and orderly way. The empirical results show that our model can effectively predict the daily load of heterogeneous households, with prediction accuracies of 87.9–90.2% on the test set across various tasks. Compared with the base model, our model achieves an 8.7% higher accuracy with a much faster convergence speed. Furthermore, the fluctuations of accuracy on different tasks are within 2.3%, showing that our model is robust. The superiority of the global method is proved through the comparison with local method in the empirical results. This study is expected to make contribution to global STLF of heterogeneous households and providing experience for the global prediction of heterogeneous time series based on deep-LSTM and pretraining method. [ABSTRACT FROM AUTHOR]

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, thus leading to increased economic and social benefits. Currently, some simple AI and hybrid models have issues to deal with and struggle with multivariate dependencies, long-term dependencies, and nonlinear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while lasso regression serves as the metalearner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in the forecasting accuracy, with a substantial reduction in the mean absolute percentage error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, thus suggesting potential operational efficiency benefits for the power industry. [ABSTRACT FROM AUTHOR]

Abstract Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction.

Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country's power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessarily include the target series. In the present study, we investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement feed-forward NN model and perform a clustering analysis to identify similar patterns among the load series and enhance TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered. [ABSTRACT FROM AUTHOR]

Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilities by formulating the generation, transmission and distribution of electric energy in advance. However, the nonlinear patterns and dynamics of load data still make accurate load forecasting a challenging task. To address this issue, a deep temporal convolutional network (TCN)-based hybrid model combined with variational mode decomposition (VMD) and self-attention mechanism (SAM) is proposed in this study. Firstly, VMD is used to decompose the original load data into a series of intrinsic mode components that are used to reconstruct a feature matrix combined with other external factors. Secondly, a three-layer convolutional neural network is used as a deep network to extract in-depth features between adjacent time points from the feature matrix, and then the output matrix captures the long-term temporal dependencies using the TCN. Thirdly, long short-term memory (LSTM) is utilized to enhance the extraction of temporal features, and the correlation weights of spatiotemporal features are future-adjusted dynamically using SAM to retain important features during the model training. Finally, the load forecasting results can be obtained from the fully connected layer. The effectiveness and generalization of the proposed model were validated on two real-world public datasets, ISO-NE and GEFCom2012. Experimental results indicate that the proposed model significantly improves the prediction accuracy in terms of evaluation metrics, compared with other contrast models. [ABSTRACT FROM AUTHOR]

The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model's significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model's ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks. [ABSTRACT FROM AUTHOR]

Short-Term Load Forecasting (STLF) is the process of predicting the amount of electricity demand that will be required over a short period of time, ranging from a few hoursdays. The purpose of STLF is to help utility companies and power system operators to optimize the allocation and usage of energy resources, ensuring that enough electricity is generated and delivered to meet the expected demand at a given time. This involves analyses of factors that influences energy demand, such as weather conditions, time of day, weekdays, special events and other techniques to generate accurate and reliable predictions of future energy demand. Feed Forward Neural Network (FFNN) model is used for STLF in order to meliorate load accuracy. The data of three regions has been reckoned for training proposed model. Parameterstemperature, relative humidity, liquid precipitation and wind speed at a distance of 2m of every place is manipulated as training data set for presented model. Adam Optimization is applied for training model as it has robust learning rate, which makes this most pertinent algorithm to be applied for deep learning. This algorithm exhibits faster computational time and entails less tunning variables that embodies Adam Optimization as better algorithm and hence will improve accuracy outcomes of FFNN model. [ABSTRACT FROM AUTHOR]

The accurate prediction of short-term load is crucial for the grid dispatching department in developing power generation plans, regulating unit output, and minimizing economic losses. However, due to the variability in customers' electricity consumption behaviour and the randomness of load fluctuations, it is challenging to achieve high prediction accuracy. To address this issue, we propose an ensemble deep learning model that utilizes reduced dimensional clustering and decomposition strategies to mitigate large prediction errors caused by non-linearity and unsteadiness of load sequences. The proposed model consists of three steps: Firstly, the selected load features are dimensionally reduced using singular value decomposition (SVD), and the principal features are used for clustering different loads. Secondly, variable mode decomposition (VMD) is applied to decompose the total load of each class into intrinsic mode functions of different frequencies. Finally, an ensemble deep learning model is developed by combining the strengths of LSTM and CNN-GRU deep learning algorithms to achieve accurate load forecasting. To validate the effectiveness of our proposed model, we employ actual residential electricity load data from a province in northwest China. The results demonstrate that the proposed algorithm performs better than existing methods in terms of predictive accuracy. [ABSTRACT FROM AUTHOR]

High-precision load forecasting is crucial for the power system planning and electricity market transactions. Recently, deep learning models have been widely used due to their powerful data mining capabilities. However, the existing research mainly focus on model structure adjustment and input feature selection, ignoring the influence of model ensemble on prediction. A single deep learning model is not yet able to address the various complex challenges that arise in short-term load forecasting. To improve the accuracy of short-term load forecasting, this paper proposes a novel multi-scale ensemble method and multi-scale ensemble neural network. This neural network uses long short-term memory, gate recurrent units, and temporal convolutional network as the basic model. By coupling the stochastic weight averaging ensemble method and differential evolution ensemble method, these deep learning networks were assembled from single-model scale and multi-model scale, respectively, thereby effectively improving the model prediction accuracy. For predicting the power load of Hubei Province in China, meteorological features and time features were in consideration. The proposed model was trained and compared with eleven intelligent short-term load forecasting models, including machine learning, deep learning and ensemble deep learning models. Simulations show that the proposed model has the best comprehensive prediction performance. This study highlights the power of ensemble deep learning models coupled with multiple ensemble techniques and the promising prospect of our proposed model in short-term load forecasting.

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessarily include the target series. In the present study, we investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement feed-forward NN model and perform a clustering analysis to identify similar patterns among the load series and enhance TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

Short-term load forecasting is essential for power companies because it is necessary to ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic. The studied electric load data with hourly resolution from the beginning of 2020 to the first seven days of 2021 for the New York Independent Operator is the basis for the modeling. The new components used in this article include the coordination of stacked long short-term memory-based models and feature engineering methods. Also, more accurate and realistic modeling of the problem has been implemented according to the existing conditions through COVID-19 epidemic data. The influential variables for short-term load forecasting through various feature engineering methods have contributed to the problem. The achievements of this research include increasing the accuracy and speed of short-term electric load forecasting, reducing the probability of overfitting during model training, and providing an analytical comparison between different feature engineering methods. Through an analytical comparison between different feature engineering methods, the findings of this article show an increase in the accuracy and speed of short-term load forecasting. The results indicate that combining the stacked long short-term memory model and feature engineering methods based on extra-trees and principal component analysis performs well. The RMSE index for day-ahead load forecasting in the best engineering method for the proposed stacked long short-term memory model is 0.1071. [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]

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]

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week's load, the previous day's load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R2 of 0.75 and MAPE of 5.70%. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-series with the original load data. Secondly, to achieve the relationships between load series and external factors during an hour scale and the correlations among these data points, a strategy based on the 1D-CNN and TCN is proposed to comprehensively refine the feature extraction. The SAM was introduced to further enhance the key feature information. Finally, the feature matrix was fed into the long short-term memory (LSTM) for STLF. According to experimental results employing the North American New England Control Area (ISO-NE-CA) dataset, the proposed model is more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM models. The proposed model outperforms the 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM by 21.88%, 51.62%, 36.44%, 42.75%, 16.67% and 40.48%, respectively, in terms of the mean absolute percentage error. [ABSTRACT FROM AUTHOR]

Short‐term electricity load forecasting plays an essential role in power system operation and energy trading. However, the strong volatility, seasonality, and uncertainly of the load series pose challenges to accurate short‐term load forecasting using existing forecasting models. To tackle these challenges, in this paper, we investigate a novel deep learning‐based probabilistic short‐term load forecasting model to predict the hourly ahead electricity load accurately. The proposed deep learning forecasting model embeds nonconvex minimax concave penalty in the robust long‐ and short‐term memory (LSTM) network to simplify its complex structure while extracting the important information. The model parameters in the proposed regularized and robust LSTM network are fine‐tuned using whale optimization algorithm which is a meta‐heuristic approach. For empirical applications, electricity load data collected from New York Independent System Operator and ISO New England are considered, and numerical results show that the proposed method outperforms other state‐of‐the‐art competitors in terms of both point and interval forecasting accuracy. [ABSTRACT FROM AUTHOR]

To reduce the short‐term load forecasting (STLF) error of off‐line forecasting model, a VMD‐IWOA‐LSTM (VIL) method for STLF is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the historical power load signals. Then, the decomposed signals are reconstructed according to the similarity of Pearson correlation coefficient (PCC), and meteorological data are chosen for each reconstructed component based on the set PCC threshold. The long short‐term memory (LSTM) models are used to predict the corresponding components, and improved whale optimization algorithm (IWOA) is used to optimize the parameters in LSTM. Finally, the forecast results of each component are added together to get the final forecast result. The experimental results of power load data in a certain area show that the proposed method has the advantages of strong anti‐interference performance and high prediction accuracy compared with other methods, and has strong practicability. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting has been an important approach for economical and sustainable power systems. Various methods have been proposed for obtaining an accurate forecasting result, among which deep learning models achieve state‐of‐the‐art performance. While external factors have been considered in the modeling of the load forecasting process, there is a lack of comparison between the effect of calendar and weather information. In this letter, a TCN‐based load forecasting model incorporating calendar and weather information is proposed and outperforms three deep learning and four machine learning baselines on an open real‐world load dataset, with and without leveraging the calendar or weather information. It is found that weather information is more helpful for improving the load forecasting performance than calendar information through numerical experiments. [ABSTRACT FROM AUTHOR]

Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is an important but a difficult task due to the uncertainty and complexity of electric power systems. In recent times, an attention-based model, Informer, has been proposed for efficient feature learning of lone sequences. To solve the quadratic complexity of traditional method, this model designs what is called ProbSparse self-attention mechanism. However, this mechanism may neglect daily-cycle property of load profiles, affecting its performance of STLF. To solve this problem, this study proposes an improved Informer model for STLF by considering the periodic property of load profiles. The improved model concatenates the output of Informer, the periodic load values of input sequences, and outputs forecasting results through a fully connected layer. This makes the improved model could not only inherit the superior ability of the traditional model for the feature learning of long sequences, but also extract periodic features of load profiles. The experimental results on three public data sets showed its superior performance than the traditional Informer model and others for STLF.

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.

Accurate load forecasting is the foundation for power systems to develop the optimal combination of generating units, power economic dispatch and other cooperative operation strategies. In order to solve the problem of uncertainty and volatility of load changes due to weather conditions and users' energy use characteristics in low-voltage distribution networks, a short-term forecasting model based on Residual Stacking Frequency Attention (RSF-Attention) is proposed. First, RSF-Attention is an integrated model that stacks multiple sub-models, and the input information of sub-models is changed by residual stacking technique so that each sub-model pays attention to the different characteristics to mine the load information exhaustively. Subsequently, a new attention mechanism named FFT-Attention is designed to focus the model on the overall trend of the signal by converting the feature information into frequency domain and reducing the high-frequency noisy signal characteristics of the load information. Finally, prediction experiments are conducted on the Australian power dataset and power data of the Yun Hai station area in Lianyungang, Jiangsu Province, China. Ablation experimental results show the effectiveness of constructed prediction model; comparison experiments with a variety of deep learning models show that the proposed model has higher prediction accuracy. • Residual stacking frequency attention network achieves accurate load forecasting. • Residual method makes each sub-model pay attention to distinct information. • Frequency attention focuses the model on the overall trend of the signal. • Experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources in power systems has resulted in increased complexity in dispatch management, necessitating higher accuracy in short-term load forecasting. Although numerous deep learning models have been developed, their training data requirements and difficulties in effectively combining different model structures remain challenges. This study proposes a multi-model transfer-based approach for dynamic short-term load forecasting to address these issues. The method consists of two phases: (1) conducting correlation analysis to determine transferability between datasets and implementing an adaptive parameter transfer policy to optimize data utilization and training efficiency, and (2) constructing a multi-model cooperation platform to fully leverage the strengths of different models and enhance forecasting accuracy. Experimental case studies demonstrate the effectiveness of the proposed method in improving forecasting accuracy when historical load data is limited. This research contributes to advancing load forecasting techniques by leveraging transfer learning and model cooperation, facilitating efficient power system operations in the context of renewable energy integration. • This study develops two transfer strategies for the models of complex structures by utilizing the parameters transfer flexibility, which is helpful to avoid negative transfer issues. • The proposed method emphasizes combining different models to address forecasting tasks for the same time series. • The proposed method focuses on improving the forecasting accuracy at each time point of the load time series. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply and demand. Recently, the challenge in STLF has been the load variation that occurs in each period, day, and seasonality. This work proposes the bagging ensemble combining two machine learning (ML) models—linear regression (LR) and support vector regression (SVR). For comparative analysis, the performance of the proposed model is evaluated and compared with three advanced deep learning (DL) models, namely, the deep neural network (DNN), long short-term memory (LSTM), and hybrid convolutional neural network (CNN)+LSTM models. These models are trained and tested on the data collected from the Electricity Generating Authority of Thailand (EGAT) with four different input features. The forecasting performance is measured considering mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) parameters. Using several input features, experimental results show that the integrated model provides better accuracy than others. Therefore, it can be revealed that our approach could improve accuracy using different data in different forecasting fields. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is very important for an efficient operation of the power system because the exact and stable load forecasting brings good results to the power system. This manuscript presents the application of two new models in STLF i.e. Cross multi-models and second decision mechanism and Residential load forecasting in smart grid using deep neural network models. In the cross multi-model and second decision mechanism method, the horizontal and longitudinal load characteristics are useful for the construction of the model with the calculation of the total load. The dataset for this model is considered from Maine in New England, Singapore, and New South Wales of Australia. While, In the residential load forecasting method, the Spatio-temporal correlation technique is used for the construction of the iterative ResBlock and deep neural network which helps to give the characteristics of residential load with the use of a publicly available Redd dataset. The performances of the proposed models are calculated by the Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. From the simulation results, it is concluded that the performance of cross multi-model and second decision mechanism is good as compare to the residential load forecasting. [ABSTRACT FROM AUTHOR]

Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.

While the general belief is that the best way to predict electric load is through individualized models, the existing studies have focused on one-for-all models because the individual models are difficult to train and require a significantly larger data accumulation time per individual. In recent years, applying deep learning for forecasting electric load has become an important research topic but still one-for-all has been the main approach. In this work, we adopt transfer learning and meta learning that can be smoothly integrated into deep neural networks, and show how a high-performance individualized model can be formed using the individual's data collected over just several days. This is made possible by extracting the common patterns of many individuals using a sufficiently large dataset, and then customizing each individual model using the specific individual's small dataset. The proposed methods are evaluated over residential and non-residential datasets. When compared to the conventional methods, the meta learning model shows 7.84% and 15.07% RMSE improvements over the residential and non-residential datasets, respectively. Our results suggest that the individualized models can be used as effective tools for many short-term load forecasting tasks.

Short-term load forecasting is a topic of considerable interest as it is of major importance for specifying and managing power resources and needs. In the literature, Deep Neural Networks have been successfully recently applied in load forecasting using single modalities as an improvement to traditional Artificial Neural Networks (ANN). In this paper, the main objective is to tackle the load forecasting problem with the intention of enhancing the prediction performance by combining and testing different multi-modal deep learning approaches and architectures in order to process and relate information from multiple modalities. The benefits of using multi-modality instead of one modality, applied to the time series modeling problem of short-term load forecasting is therefore investigated. The models are trained and evaluated using the hourly temperature and electrical consumption in addition to auto-regressive variables as the first modality. The day type characteristic, such as: weekends, week days, bank holidays, religious holidays etc., may be considered as a second modality. The approach liability is tested by comparing the empirical results of different Deep architectures namely: Stacked Denoising Auto- Encoders (SDAEs) and Convolutional Neural Network (CNN), with multiple and single modalities where processing the same task of one day ahead load forecasting. [ABSTRACT FROM AUTHOR]

As the development of smart grids and electricity markets around the world, short-term load forecasting (STLF) plays an increasingly important role in safe and economical operations of power systems. Facing massive multivariate and heterogeneous data from smart grid environment, traditional STLF methods no longer meet the requirements of comprehensive prediction performance in big data era. Therefore, a novel STLF model named EGA-STLF is proposed in this paper. The core idea of the proposed model is mainly based on distributed representation, bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. EGA-STLF encodes non-numerical variables in input data into sparse vectors by distributed representation during preprocessing, which can more reasonably reflect the correlations between different variables across the temporal sequence. Moreover, the Bi-GRU layer in EGA-STLF processes the past and the future information simultaneously to fully extract temporal and nonlinear features from input data for the improvement of forecasting accuracy. In addition, the introduction of attention mechanism highlights the role of key features in load forecasting, which is beneficial to generate more accurate forecasting results for EGA-STLF. The validity and superiority of EGA-STLF is verified by taking the actual data from Victoria state and Queensland state in Australia for experimental research in which mean absolute percentage error (MAPE) and root mean square error (RMSE) are selected as evaluation metrics. The experimental results indicate that EGA-STLF outperforms the state-of-the-art models based on SVR and MLP in term of comprehensive forecasting accuracy. Hence, EGA-STLF is a promising method to create economic benefits in power industry.

Residential demand response is vital for the efficiency of power system. It has attracted much attention from both academic and industry in recent years. Accurate short-term load forecasting is a fundamental task for demand response. While short-term forecasting for aggregated load data has been extensively studied, load forecasting for individual residential users is still challenging due to the dynamic and stochastic characteristic of single users' electricity consumption behaviors, i.e., the variability of the residential activities. To address this challenge, this paper presents a short-term residential load forecasting framework, which makes use of the spatio-temporal correlation existing in appliances' load data through deep learning. Multiple time series are conducted in the framework to describe electricity consumption behaviors and their internal spatio-temporal relationship. And a method based on deep neural network and iterative ResBlock is proposed to learn the correlation among different electricity consumption behaviors for short-term load forecasting. Experiments based on real world measurements have been conducted to evaluate the performance of the proposed forecasting approach. The results show that both the appliances' load data and iterative ResBlocks can help to improve the forecasting performance. Compared with existing methods, measurements on Root Mean Squared Error, Mean Absolute Error and Mean Absolute Percentage Error for the proposed approach are reduced by 3.89%-20.00%, 2.18%-22.58% and 0.69%-32.78%. In addition, further experiments are conducted to evaluate the impact of using appliances' load data, iterative ResBlocks as well as other factors for the proposed approach.

In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied. However, missing imputation and load forecasting have been considered independently so far. In this article, we provide a deep learning framework that jointly considers missing imputation and load forecasting. We consider a family of autoencoder/long short-term memory (LSTM) combined models for missing-insensitive load forecasting. Specifically, autoencoder (AE), denoising autoencoder (DAE), convolutional autoencoder (CAE), and denoising convolutional autoencoder (DCAE) are considered for extracting features, of which the encoded outputs are fed into the input of LSTM. Our experiments show that the proposed DCAE/LSTM combined model significantly improves forecasting accuracy no matter what missing rate or type (random missing, consecutive block missing) occurs compared to the baseline LSTM.

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data. [ABSTRACT FROM AUTHOR]

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week’s load, the previous day’s load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R2 of 0.75 and MAPE of 5.70%.

Electric load forecasting has always been a key component of power grids. Many countries have opened up electricity markets and facilitated the participation of multiple agents, which create a competitive environment and reduce costs to consumers. In the electricity market, multi-step short-term load forecasting becomes increasingly significant for electricity market bidding and spot price calculation, but the performances of traditional algorithms are not robust and unacceptable enough. In recent years, the rise of deep learning gives us the opportunity to improve the accuracy of multi-step forecasting further. In this paper, we propose a novel model multi-scale convolutional neural network with time-cognition (TCMS-CNN). At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. In addition, we design an innovative time coding strategy called the periodic coding strengthening the ability of the sequential model for time cognition effectively. At last, we integrate MS-CNN and periodic coding into the proposed TCMS-CNN model with an end-to-end training and inference process. With ablation experiments, the MS-CNN and periodic coding methods had better performances obviously than the most popular methods at present. Specifically, for 48-step point load forecasting, the TCMS-CNN had been improved by 34.73%, 14.22%, and 19.05% on MAPE than the state-of-the-art methods recursive multi-step LSTM (RM-LSTM), direct multi-step MS-CNN (DM-MS-CNN), and the direct multi-step GCNN (DM-GCNN), respectively. For 48-step probabilistic load forecasting, the TCMS-CNN had been improved by 3.54% and 6.77% on average pinball score than the DM-MS-CNN and the DM-GCNN. These results show a great promising potential applied in practice.

Short-Term Load Forecasting (STLF) for End-User Transformer Level (EUTL) is challenging due to the high penetration of Electric Heating Loads (EHLs), which exhibit significant uncertainty, nonlinearity, and variability. In this paper, a STLF model is proposed based on the Stacked Auto-Encoder Extreme Learning Machine (SAE-ELM) deep learning framework, which can be used to extract hidden features from the time series load data. In order to improve the capability of extracting deep and diverse features from the data and generate a useful knowledge representation structure, a novel specialized feature indices set is proposed to construct the training sample set. The sliding trend, fluctuation rate, grade of change, and smoothness of the time series are considered and quantified as elements of the training sample set. Then, deep nonlinear features are extracted by using the SAE-ELM with no iterative parameter tuning needed. To illustrate the validity of the proposed model, five numerical cases are conducted. Comparison of results shows that the proposed model improves the capability and sensitivity of dealing with load volatility and forecasting accuracy.

Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF.