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Accurate short‐term load forecasting can ensure the reliable power supply of the power system and the regular operation of the grid economy. This article proposes a hybrid model based on convolutional neural networks and bidirectional long short‐term memory (CNN‐BiLSTM) with Bayesian optimization (BO) and attention mechanism (AM) for short‐term load forecasting. CNN is used to capture the significant features of input data. BiLSTM is adept in time series forecasting and AM can reduce the computational complexity of model. BO can help to tune the hyperparameters automatically. The input features of models are recent load, time slot, date type, and meteorological factors. In order to eliminate the seasonality, the data set is divided into four subsets according to four seasons. The performance of the proposed model is compared with other models by MAE, RMSE, MAPE, and R2 score. The forecasting results represent that the proposed model is most suitable for short‐term load forecasting among the contrast models and the meteorological factors have an impact on forecasting accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the nonlinear and non‐stationary characteristics of the power load, in order to improve the accuracy of load forecasting, a short‐term load forecasting method based on variational modal decomposition and the combination of convolutional neural network and bi‐directional long short‐term memory (Bi‐LSTM) network is proposed. The load sequence is decomposed into components with different frequencies by variational modal decomposition, and each component is predicted by Bi‐LSTM. Then the convolution neural network is introduced to process multi‐source data. The load, temperature, day and other data are constructed according to the time sliding window as the input, and the convolution neural network is used to extract the effective feature vector, the eigenvector is constructed in a time series and used as the input data of Bi‐LSTM network. Taking the public data set provided by a US public utility department as an example, the proposed model is compared with other methods. The results show that the proposed method can better track the change of load and effectively improve the accuracy of short‐term load forecasting. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [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]

In this paper, a novel swarm intelligence optimization algorithm is presented based on the sparrow search algorithm, namely, an intensified sparrow search algorithm (ISSA). Specifically, the newly proposed neighbor search strategy takes into account both exploring the entire feasible solution space as much as possible in the iterative process and effectively preventing the weakening of exploration ability in the late iteration. In addition, a new foraging method called the saltation learning strategy is put forward to improve the search capability of the scrounger. Firstly, the effectiveness of the ISSA is comprehensively evaluated by competition functions, known as CEC-BC-2017. The simulation results show that the ISSA substantially improves the convergence accuracy of the basic SSA and also outperforms three SSA-based variants and six state-of-art optimization algorithms. Then, to further demonstrate the real-world application potential, the ISSA is successfully used in two engineering design problems (including the pressure vessel and the welded beam designs). Finally, the proposed ISSA is employed to optimize the hyper-parameters of the long short-term memory (LSTM) network, which leads to a novel ISSA-LSTM model. The developed ISSA-LSTM model is applied to the short-term load forecasting of the power system. The experimental results show that the mean absolute percentile error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) values of the proposed ISSA-LSTM model are 1.2778%, 1.2171 and 0.9267 respectively, which is superior to several LSTM-based variants. [ABSTRACT FROM AUTHOR]

Residential Short-term load forecasting is an important basis for power department to make dispatching plan reasonably. A short-term load forecasting framework was proposed that is based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering algorithm, Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). In this framework, based on historical load data collected by smart meters, BIRCH clustering algorithm is adopted to cluster users into multiple user groups according to different users’ electricity consumption habits. A multivariate time series dataset composed of load data, time data, and climate data are into the CNN network, which is composed of a one-dimensional convolutional layer and a pooling layer. The CNN network is responsible for extracting the potential relationship and high dimensional characteristics of different variables. After that, the data are input into the GRU network, and the sequence characteristics of the time sequence data are studied. Finally, the short-term load prediction results are output from the full-connected layer. Taking the open dataset provided by the Commission for Energy Regulation (CER) of Ireland as an example, the Mean Absolute Percentage Error (MAPE) of the proposed method reached 2.932 1%. Compared with the prediction efficiency and accuracy of ANN network, CNN network, and CNN-GRU network under the same conditions, the experimental results showed that the proposed method has higher prediction accuracy and higher training efficiency.

Accurate power load forecasting is crucial to the safe and stable operation of power systems. In the context of spot market, the dynamically changing real-time market tariff gives the “commodity” property of electricity and changes the electricity consumption behavior of customers and the electricity consumption at each time, which significantly aggravates the difficulty of electricity load forecasting. To address the problems of many influencing factors, difficult input sequence selection and insufficient feature extraction capability of the prediction model, we propose a hybrid prediction model that combines novel input sequence selection and longitudinal crossover algorithm (CSO) to optimize deep belief networks. Firstly, the real-time electricity price is incorporated as an influencing factor into the prediction model input, which improves the prediction model influencing factor system in the market environment; secondly, the similarity between the historical load sequence influencing factor and the sequence of load influencing factors of the day to be predicted is considered from two perspectives of distance and trend, and the historical load sequence is reasonably selected using the comprehensive similarity, and then the prediction model input sequence is determined; finally, the deep belief Finally, the load forecasting model is constructed by using deep belief network, and the key parameters such as threshold value of the forecasting model are optimized by CSO algorithm to achieve accurate power load forecasting. The proposed hybrid forecasting model is validated by comparing and analyzing the forecasting results of different input sequences and different methods with the simulation of Singapore electricity market data. In addition, input series of different time scales are set to find the best sample set to avoid the reduction of forecasting accuracy caused by data redundancy in the sample set, and a sample set of 6 months is appropriate for load forecasting by the method in this paper.

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.

The load in the power grid is often affected by many factors, and the coupling relationship among them changes dynamically with time. This study proposed a short-term load forecasting technique based on time pattern attention and a long short-term memory network considering time-dependent intrinsic cross-correlation (TDICC) for smart grids with massive amounts of data. Based on source-load dynamic correlation analysis, The proposed TDICC to track and correct the multi-timescale dynamic correlation of two signal overruns or lags, which realizes the transformation of correlation description from two-dimensional space to three-dimensional space and expands its ability to describe multi-timescale dynamic correlation. Finally, the actual load data are used for example analysis, and the results show that the proposed method can tap the dynamic correlation between multiple influencing factors and load and has higher prediction accuracy compared with other models, which provides a more accurate database for power system dispatching.

The intermittent non-dispatchable power produced by Renewable Energy Sources (RESs) in distribution networks caused additional challenges in load forecasting due to the introduced uncertainties. Therefore, high-quality load forecasting is essential for distribution network planning and operation. Most of the work presented in literature focusing on Short-Term Load Forecasting (STLF) has paid little consideration to the intrinsic uncertainty associated with the load dataset. A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. To avoid the divergence of conventional Kalman filter and improve the system stability, Adaptive Extended Kalman Filter (AEKF) is introduced through incorporating a moving-window method with the Extended Kalman Filter (EKF). Nonetheless, the moving window adds an extra computational burden. In this regard, this paper employs the concept of Forgetting Factor AEKF (FFAEKF) for STLF in distribution networks to avoid the computational burden introduced by the AEKF. The forgetting factor improves the estimation accuracy and increases the system convergence when compared with the AEKF. In this paper, the AEKF and FFAEKF are compared in terms of their performance using Maximum Absolute Error (MaxAE) and Root Mean Square Error (RMSE). Matlab/Simulink platform is used to apply the AEKF and FFAEKF algorithms on the load dataset. Results have demonstrated that the FFAEKF improves the forecasting performance through providing less MaxAE and less RMSE. In which, the FFAEKF MaxAE and RMSE are reduced by two and three times, respectively, compared to the AEKF MaxAE and RMSE.

Accurate load forecasting is critical for reliable and efficient planning and operation of electric power grids. In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction. Our framework adopts a modular design with good generalization capability. First, the feature-weighting mechanism assigns input features with temporal weights. Second, a recurrent encoder-decoder structure with hierarchical attention is developed as a load predictor. The hierarchical attention enables a similar day selection, which re-evaluates the importance of historical information at each time step. Third, we develop an error correction module that explores the errors and learned feature hidden information to further improve the model’s forecasting performance. Experimental results demonstrate that our proposed framework outperforms existing methods on two public datasets and performance metrics, with the feature weighting mechanism and error correction module being critical to achieving superior performance. Our framework provides an effective solution to the electric load forecasting problem, which can be further adapted to many other forecasting tasks.

Short-term load forecasting is mainly utilized in control centers to explore the changing patterns of consumer loads and predict the load value at a certain time in the future. It is one of the key technologies for the smart grid implementation. The load parameters are affected by multi-dimensional factors. To sufficiently exploit the time series characteristics in load data and improve the accuracy of load forecasting, a hybrid model based on Residual Neural network (ResNet) and Long Short-Term Memory (LSTM) is proposed in this paper. First, the data with multiple feature parameters is reconstructed and input into ResNeT network for feature extraction. Second, the extracted feature vector is used as the input of LSTM for short-term load forecasting. Lastly, a practical example is used to compare this method with other models, which verifies the feasibility and superiority of input parameter feature extraction, and shows that the proposed combined method has higher prediction accuracy. In addition, this paper also carries out prediction experiments on the variables in the weather influencing factors.

Abstract 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.

The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified.

Abstract Energy systems face challenges due to climate change, distributed energy resources, and political agenda, especially distribution system operators (DSOs) responsible for ensuring grid stability. Accurate predictions of the electricity load can help DSOs better plan and maintain their grids. The study aims to test a systematic data identification and selection process to forecast the electricity load of Danish residential areas. The five-ecosystem CSTEP framework maps relevant independent variables on the cultural, societal, technological, economic, and political dimensions. Based on the literature, a recurrent neural network (RNN), long-short-term memory network (LSTM), gated recurrent unit (GRU), and feed-forward network (FFN) are evaluated and compared. The models are trained and tested using different data inputs and forecasting horizons to assess the impact of the systematic approach and the practical flexibility of the models. The findings show that the models achieve equal performances of around 0.96 adjusted R2 score and 4–5% absolute percentage error for the 1-h predictions. Forecasting 24 h gave an adjusted R2 of around 0.91 and increased the error slightly to 6–7% absolute percentage error. The impact of the systematic identification approach depended on the type of neural network, with the FFN showing the highest increase in error when removing the supporting variables. The GRU and LSTM did not rely on the identified variables, showing minimal changes in performance with or without them. The systematic approach to data identification can help researchers better understand the data inputs and their impact on the target variable. The results indicate that a focus on curating data inputs affects the performance more than choosing a specific type of neural network architecture.

With the rapid development of large-scale building energy monitoring platforms, it is of great significance to develop precise forecasting methods for buildings on a large scale to achieve better energy system design, system operation, energy management, and renewable energy integration in the grid. Traditionally, using all available historical data to train a data-driven model has been widely employed to ensure prediction performance because more historical information can be learned. However, this strategy may introduce more noise, especially for short-term load forecasting. Thus, this study proposes a novel approach for selectively utilizing building historical data to determine the amount of data that should be used to train the data-driven model. First, the CV(RMSE) curve of each building reflecting the relationship between training data length and forecasting accuracy is obtained using LightGBM. Second, clustering techniques such as k-means are used to identify buildings that are sensitive to the training data length based on CV(RMSE) curves. Finally, the optimal training data length for day-ahead forecasting is estimated for each building. The case study shows that approximately 20% of buildings in the Building Data Genome are labeled as length-sensitive buildings, and adopting appropriate training data lengths can reduce the prediction error of these buildings by up to 15%.

Accurate short-term load forecasting of distribution transformer in extreme weather will effectively assist power dispatching and enable safe and stable operation of power grid. Therefore, this paper proposes the Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model, which can warn the time period and detailed value of peak load in advance. Firstly, based on Extreme Gradient Boosting (XGBoost) algorithm, the idea of Bagging is introduced to reduce the output variance and enhance the generalization ability of the algorithm. Then, the mutual information (MI) between weather influencing factors and load is analyzed to adjust the input weight of the model and improve its ability to track weather changes. Next, considering the load, weather and time factors, the extreme weather identification model is established to determine the occurrence range of peak load. Finally, the specialized training set is selected based on the weighted similarity, and high-accuracy short-term load forecasting model is established. Compared with the traditional model, the model proposed in this paper reduces the average Mean Absolute Percentage Error (MAPE) of peak load by 3% to 10%.

A new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follows a two-step process. In the first step, PSO is applied to optimize the VMD decomposition method. The original electricity load sequence is decomposed, and the fitness function uses sample entropy to describe the complexity of the time series. The decomposed sub-sequences are combined with relevant features, such as meteorological data, to form the input sequence of the prediction model. In the second step, TCN is selected as the prediction model, and it is embedded with an attention mechanism to improve prediction accuracy. The above input sequence is fed to the model to obtain the PSO-VMD-TCN-Attention prediction framework. Load datasets and various prediction models validate the PSO-optimized VMD decomposition method and the TCN-Attention prediction model. Simulation results demonstrate that the PSO-optimized VMD decomposition method enhances the model's prediction accuracy, and the TCN-Attention prediction model outperforms other prediction models in terms of prediction accuracy and ability. [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]

Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as follows: firstly, the Hampel identifier (HI) is employed to correct outliers in the original data; secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract the waveform characteristics of the data fully; and, finally, the temporal convolutional network, extreme learning machine, and gate recurrent unit are selected as the basic learners for forecasting load power data. An ensemble reinforcement learning algorithm based on Q-learning was adopted to generate optimal ensemble weights, and the predictive results of the three basic learners are combined. The experimental results of the models for three real load power datasets show that: (a) the utilization of HI improves the model's forecasting result; (b) CEEMDAN is superior to other decomposition algorithms in forecasting performance; and (c) the proposed ensemble method, based on the Q-learning algorithm, outperforms three single models in accuracy, and achieves smaller prediction errors. [ABSTRACT FROM AUTHOR]

We have developed a conventional benchmark model for the prediction of two days of electricity consumption for industrial and institutional customers of an electricity provider. This task of predicting 96 values of 15 min of electricity consumption per day in one shot is successfully dealt with by a dynamic regression model that uses the Seasonal and Trend decomposition method (STL) for the estimation of the trend and the seasonal components based on (approximately) three years of real data. With the help of suitable R packages, our concept can also be applied to comparable problems in electricity consumption prediction. [ABSTRACT FROM AUTHOR]

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]

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

Today, the energy supply does not follow the demand in a controlled manner anymore. Thus, forecasting the electricity consumption became essential for the operation of power systems. Already numerous open source software tools exist that provide forecasting models, which are configurable for different forecasting tasks. In the case of electrical energy demand, a change in the geographical or temporal settings, requires specific domain knowledge on relevant data and influencing factors that are to be considered when developing data-driven forecasting models. With ProLoaF, we propose a holistic machine-learning based forecasting project, which offers the developer a continuous deployment of reliable forecasts for the power system domain. ProLoaF serves for probabilistic forecasts of the electric energy consumption and non-controllable generation in future power system operation. By overlapping Machine Learning (ML), DevOps and power systems engineering disciplines, we aim to accelerate future forecasting model development by reducing consultation work between domain experts.

In response to the complexity and high volatility of original load data affecting the accuracy of load forecasting, a combined method for short-term load forecasting considering the characteristics of components of seasonal and trend decomposition using local regression (STL) is proposed. The original load data are decomposed into a trend component, seasonal component, and residual component using STL. Then, considering the characteristics of each component, a long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and Gaussian process regression (GPR) are used to predict the trend component, seasonal component, and residual component, respectively. The final outcome of the load forecasting is obtained by summing the forecasted results of each individual component. A specific case study is conducted to compare the proposed combined method with LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR prediction methods. Through comparison, the proposed combined method exhibits lower errors and higher accuracy, demonstrating the effectiveness of this method.

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 Sustainable energy systems are characterised by an increased integration of renewable energy sources, which magnifies the fluctuations in energy supply. Methods to to cope with these magnified fluctuations, such as load shifting, typically require accurate short-term load forecasts. Although numerous machine learning models have been developed to improve short-term load forecasting (STLF), these models often require large amounts of training data. Unfortunately, such data is usually not available, for example, due to new users or privacy concerns. Therefore, obtaining accurate short-term load forecasts with little data is a major challenge. The present paper thus proposes the latent space-based forecast enhancer (LSFE), a method which combines transfer learning and data augmentation to enhance STLF when training data is limited. The LSFE first trains a generative model on source data similar to the target data before using the latent space data representation of the target data to generate seed noise. Finally, we use this seed noise to generate synthetic data, which we combine with real data to enhance STLF. We evaluate the LSFE on real-world electricity data by examining the influence of its components, analysing its influence on obtained forecasts, and comparing its performance to benchmark models. We show that the Latent Space-based Forecast Enhancer is generally capable of improving the forecast accuracy and thus helps to successfully meet the challenge of limited available training data.

Abstract Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method.

Many uncertain factors to the planning and distribution of the power grid have been brought by connecting to the distributed power grid and increasing active loads. To obtain more accurate and comprehensive information of power load forecasting value, a short-term power load-interval multi-step forecasting method based on ForecastNet is proposed in this paper. Firstly, single variable historical load data is used as input. Secondly, ForecastNet’s deep feedforward architecture is proposed to exactly capture the time-varying characteristics of load. Finally, the Gaussian distribution output is used to realize the uncertainty evaluation of the model. Deterministic point forecasting combines probabilistic forecasting to quantify the uncertainty of forecast results. Output power load forecasts in the form of probability intervals. The experimental results show that the ForecastNet forecasting model proposed in this paper outperforms the other three models in the four seasons. In the autumn when the load curve volatility and random components are large, MASE=0.357, SMAPE=4.869%, NRMSE=11.804%. It has the advantages of the high predictive range quality, high predictive accuracy, and great practical engineering value.

With the development of smart grids and the popularization of smart meters, grid companies have obtained a large amount of fine-grained user electricity consumption data, making it possible to forecast individual users’ electricity load. Traditional deterministic forecasting cannot measure the uncertainty of users’ future electricity consumption behavior. This paper aims to establish a short-term load probability forecasting model for individual users, which can output the probability prediction interval and the probability density curve. Firstly, historical data is applied to train a deterministic prediction model based on random forest. Then, when predicting the user load at a future time, the output of each tree in the forest is formed into a prediction set, and the kernel density estimation method is used to obtain the probability prediction result of the user load. The proposed RF-KDE method obtains promising results on a public dataset. It also shows the advantages of easy parameters adjustment and fast training speed. Besides, tests conducted on the public dataset confirm that the proposed method can be applied to users with different electricity consumption behaviors.

Traditional short-term load forecasting (STLF) methods for large utility grid systems usually provide the forecasted load with deterministic points. However, deterministic load forecasting cannot reveal the load pattern and uncertainty of controllable load in a microgrid, where the prediction errors may exceed the expected range due to the high volatility and strong randomness. In order to deal with this matter, a probability density forecasting method is proposed to predict the microgrid load with uncertainty for robust power scheduling in this paper. The proposed probability forecasting method effectively combines several data-driven and statistical algorithms, including the k-means algorithm, quantile regression long short-term memory neural network (QRLSTM), and kernel density estimation (KDE). Firstly, similar days related to the prediction day are selected through the k-means algorithm, and the historical load data of these selected days are divided into two subsets including the training dataset and the testing dataset. Secondly, a QRLSTM-based model is established and used to predict the microgrid load for different quantiles. Finally, the probability density function of the predicted points is obtained by KDE on the target day. The prediction accuracy is evaluated roundly and the results demonstrate that the proposed method can effectively reproduce the probability density distribution of the load and provide noticeably better performance than some benchmark methods.

Forecasting load is an integral part of the planning, operation, and control of power systems. This paper is part of a research effort aimed at developing better energy demand forecasting models for load dispatch centers (LDCs) in Indian states as part of an ambitious project utilizing artificial intelligence-based load forecasting models. In this paper, we present a half hourly load forecasting method for the energy management system of the project that will be used at 33 /11 kV and 0.415 kV substations with good accuracy. The paper uses the half-hourly load consumption dataset collected from MSEDCL for Maharashtra from July 1, 2020 through August 31, 2022. This paper evaluates 24 regression model-based half hourly based load forecasting algorithms for ALE PHATA load based on the load consumption dataset and the collected meteorological dataset. The 24 models in MATLAB Regression belong to five types of regression models: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), Ensemble of Trees, and Neural Networks. As a consequence of their nonparametric kernel-based probabilistic nature, the GPR family of models demonstrates the best load forecasting performance. Least squares estimation was used to determine the regression coefficients. There is a direct correlation between load in an electrical power system and temperature, due point, and seasons, as well as a correlation between load and previous load consumption. Therefore, the input variables are Wet Bulb Temperature at 2 Meters (C), Dew/Frost Point at 2 Meters (C), Temperature at 2 Meters (C), Relative Humidity at 2 Meters (%), Specific Humidity at 2 Meters (g/kg) and Wind Speed at 10 Meters (m/s). The mean absolute percentage error and the R squared are used to validate or verify the accuracy of the model, which is shown in the results section. Based on this study, two GPR models are recommended for load forecasting, the Rational Quadratic GPR and the Exponential GPR and Exponential GPR as final model.

Power load forecasting is an important part of smart grid, and its accuracy will directly affect the control and planning of power system operation. In the context of electricity market reform, real-time electricity prices affect users' electricity consumption patterns. A short-term load forecasting model based on support vector regression (SVR) with whale optimization algorithm (WOA) considering real-time electricity price is proposed in this paper. Meta-heuristics are very promising in optimizing the parameters of SVR, and the WOA algorithm is used to determine the appropriate combination of SVR's parameters to accurately establish a forecasting model. The initial value of the original WOA algorithm lacks ergodicity, and has defects such as easy to fall into local optimum and low convergence accuracy. Chaos mechanism and elite opposition-based learning strategy are introduced into WOA to balance the exploration and exploitation of the algorithm and improve the algorithm convergence speed. Numerical examples involving two power load datasets show that the proposed model can achieve better forecasting performance in comparison with other models, such as SVR, BPNN. At the same time, it proves that the forecasting accuracy with electricity price is higher than that without electricity price. [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]

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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]

Accurate electricity demand forecasting can provide a timely and effective reference for economic control and facilitate the secure production and operation of power systems. However, electricity data are well known for their nonlinearity and multi-seasonal features, making it challenging to construct forecasting models. This study investigates the combination of singular spectrum analysis to facilitate the construction of decomposition-based forecasting approaches for electricity load. First, we demonstrate and emphasize the importance of separability for specifically extracting different features hidden in the original data; moreover, only by using the separable feature subseries, the constructed individual model can capture the inner and distinct characteristics of original series more effectively. Second, this study decomposes the electricity load into several significant features using singular spectrum analysis. Each feature series is predicted separately to construct aggregate results. In particular, we propose SSA-based period decomposition to not only perform separable decomposition but also overcome the border effect, which has received little attention in previous work. Finally, to verify the effectiveness of the proposed method, we conduct an empirical study and compare the performance of the discussed models. The empirical results show that the proposed approach can obtain the expected forecasting performance and is a reliable and promising tool for extracting different features. [ABSTRACT FROM AUTHOR]

A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS approach consistently outperforms forecasters with larger feature sets. [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]

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]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Considering the non-linear, multi-dimensionality and time-series of load data, a short-term power load forecasting method based on TCN-DNN hybrid deep learning model is proposed. Firstly, considering the common influencing factors of short-term load, and analysing the correlation, the temperature variable quantitative model considering the accumulated temperature effect is established. Secondly, based on the deep learning, the load influencing factors are classified and processed; In order to study the internal dynamics of the data, the Temporal Convolutional Network (TCN) is used to extract and construct time-series feature vector; The outputs integrate with non-time-series data factors (date type) into new input features. Finally, the internal relationship between the characteristics and load changes is analysed as a whole by the Deep Neural Network (DNN), and the load forecasting is finally completed. Taking the actual load data of the power grid in a certain area of East China as practical example, the forecasting accuracy of the proposed method is 97.92%. The proposed method is compared with Long Short-Term Memory (LSTM), TCN, DNN and the hybrid TCN-DNN model without temperature correction, the experimental results show that the proposed method has higher optimization efficiency and forecasting accuracy.

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications. • The proposed model trained with an enhanced feature matrix achieves a high prediction accuracy. • Accurate load predictions help optimize energy use, leading to cost savings and enhanced sustainability • The deep learning model captures complex temporal dependencies, crucial for accurate load forecasting. • NILM enables detailed energy monitoring in smart buildings without requiring sub-metering for each appliance. • The proposed model performs well under varying conditions, showcasing its robustness and reliability. [ABSTRACT FROM AUTHOR]

• The secondary decomposition of the high frequency component reduces the influence of its randomness. • The sub-sequence personalization matrix was constructed, reduced training time by 23.6 %. • The sub-sequence of different frequency characteristics is optimized by different prediction models. • The influence of characteristic parameters and frequency characteristics on load prediction was studied. Rapid and accurate short-term load forecasting for distribution network is beneficial to ensure the safe and stable operation of power grid, reduce operating costs and improve the utilization rate of energy. Initially, through the data preprocessing minimizes the impact of outlier data on predictions. Subsequently, using variational mode decomposition and sample entropy methods separate modal components into high-frequency and low-frequency periodic sequences. Pearson correlation coefficient and principal component analysis are then employed to analyze feature parameter correlations, constructing distinct feature matrices for each Sub-sequence. High-frequency sequences are inputted into a prediction model combining time convolutional and bidirectional long short-term memory networks, while low-frequency periodic sequences are fed into a model combining auto regressive integral moving average and support vector regression. An illustrative analysis using January data from a Chinese province. Results indicate that compared with the 13-dimensional eigenmatrix, the proposed method saves 63 s in prediction time and improves the efficiency by 23.6 %. Mean absolute percentage error only decreased by 0.143 %, indicating that the method can ensure the prediction accuracy without losing robustness. Additionally, case analyses for different prediction durations (1 day and 1 week) exhibit promising results with mean absolute percentage error indices of 1.982 % and 2.022 %, indicating strong predictive performance. [ABSTRACT FROM AUTHOR]