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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]

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]

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]

In order to improve the accuracy and calculating speed of load forecasting for the strong nonlinear problem of short-term load, this article proposes a Short-term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm. This method integrates the Extreme Learning Machine (ELM) algorithm into the Convolutional Neural Network (CNN): replace the fully connected layer in the original CNN network with ELM to form a CNN-ELM network. The purpose is to improve the calculation accuracy. An Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) was proposed to reduce the probability that the Fruit Fly Optimization Algorithm (FOA) would easily fall into a local optimal value. And then AMFOA is used to optimize the parameters in CNN-ELM network. The above model is used to predict the grid load of a certain area in northern China. Compared with other prediction algorithms, it is proved that the model proposed in this article has higher prediction accuracy and also proved that the model has higher calculation speed than other models. [ABSTRACT FROM AUTHOR]

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Summary Short‐term load forecasting at the distribution transformer level provides a basis for demand‐side aggregators to take part in the power market. However, under the competitive market environment, certain parties might be discriminated and do not have access to enough data and thus the challenge of load forecasting under limited dataset arises. To tackle the small sample forecasting problem at the distribution transformer level, this paper first explores the load unbalance phenomenon and correlation among three‐phase electrical loads. Then, the data augmentation strategy for learning models is proposed. The strategy includes three‐phase datasets fusion and rolling forecast. The aim is to ameliorate overfitting and make sure the distribution of the training and test data as close as possible. The strategy is validated by the realistic data of the distribution transformers from South and East China. Comprehensive case studies demonstrate that, by utilizing the proposed strategy, learning models can achieve better forecast accuracy and the generalization ability is improved. Also, the proposed strategy is proved to boost robustness against the measurement uncertainty. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) increases the complexity of forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from the data set. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to convert the continuity feature of input data into binomial distribution feature. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on the genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]

Abstract The introduction of a new pricing mechanism, the increasing-block tariff (IBT), will not only affect electricity bills for residents, but also lead to a change in residential electricity consumption behaviours. Understanding these consumption patterns will help create more accurate load forecasting and increase the efficiency of the IBT. This study proposes an innovative clustering-based approach for short-term load forecasting under the IBT in China. The new approach initially partitions households into homogeneous groups each of which has distinctive consumption patterns under the IBT, each consumer segment can then select the most appropriate model for load forecasting, and the predicted load demands of different clusters are aggregated to derive the total usage. In particular, the IBT-related attributes are newly introduced into the clustering analysis. The utility and effectiveness of the proposed model is confirmed through a realistic dataset that contains the daily household-level consumption data of 533 households from April 2014 to February 2015. Consequently, the households are classified into five clusters with distinctive consumption patterns, including low-demand and insensitivity to high temperature (C l u s t e r 1 ), ordinary users and sensitivity to high temperature (C l u s t e r 2 ), ordinary users and sensitivity to the IBT (C l u s t e r 3 ), high-demand consumers and sensitivity to high temperature (C l u s t e r 4 ), and luxury consumers (C l u s t e r 5 ). In addition, the obtained experimental results demonstrate that the proposed approach can not only achieve better prediction accuracy (e.g., the mean absolute percentage error (MAPE) improves from 3.82% to 2.28% by using autoregressive integrated moving average (ARIMA)), but also provide better flexibility for hybrid modelling. From the practical implication point of view, the proposed forecasting model can help power companies to provide a reliable and high-quality electricity supply as well as to establish appropriate schedules of operations and maintenance within a certain area. Moreover, the identified consumption behaviours can be analysed and used to improve the design and promote awareness/acceptance of the IBT. Highlights • A new approach is proposed for short-term load forecasting under the IBT in China. • Better prediction accuracy is achieved and diverse consumption patterns are captured. • The IBT-related attributes are firstly introduced to segment householders. • Theoretical and practical implications for the energy pricing policy are provided. [ABSTRACT FROM AUTHOR]

Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introduction and variable selection in STLF. Fisher information computation for one-dimensional and multidimensional weather variables is first described, and then the introduction of meteorological factors and variables selection for STLF models are discussed in detail. On this basis, different forecasting models with the proposed methodology are established. The proposed methodology is implemented on real data obtained from Electric Power Utility of Zhenjiang, Jiangsu Province, in southeast China. The results show the advantages of the proposed methodology in comparison with other traditional ones regarding prediction accuracy, and it has very good practical significance. Therefore, it can be used as a unified method for introducing weather variables into STLF models, and selecting their features. [ABSTRACT FROM AUTHOR]

In the ensemble learning-based load prediction systems, recent research studies have achieved remarkable results in terms of extracting the complementary merits of multiple individual models and reducing the forecasting uncertainty, but they lack appropriate guidance for the sample-specific ensemble weighting of actual load data showing dynamics and variability. To this end, this study proposes an adaptive ensemble framework to address the above challenging and still tricky problem, and an adaptive weight-based ensemble learning model (AW-ELM) is framed to forecast short-term electric load by introducing the representative subset based weight correction with respect to the data characteristics. In the model, a weight library is formed as representatives extracting from the given training data. Based on the representatives, probability distribution modeling is built via a distance method. Then, the adaptive weights of the test data are determined by using the best matching concept. Experiments of two electric power companies in Jiangxi Province, China are implemented to evaluate the performance of the ensemble model. The results show that the ensemble model achieves better forecasting accuracy than the average combined forecasting model and the comparison models. • Introduce an adaptive ensemble framework with representative subset. • A probability distribution system is built for the weight parameter. • The ensemble weight is dynamically updated by the characteristics of the data. • Illustrate the applicability and superiority by the experimental results of real case. • The best benchmark based AW-ELM can find a way to approximate the best single model. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China. [ABSTRACT FROM PUBLISHER]

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load. [ABSTRACT FROM AUTHOR]

• Proposed a decomposition-based quantile regression forest load forecasting method. • The proposed method reaches n-step prediction by training n models. • Temperature and humidity index is introduced as a relevant factor. • Tree-structured of Parzen Estimators based Bayesian optimization is introduced. • PICP, PINAW, and CWC are introduced to evaluate the interval prediction results. Short-term load forecast (STLF) determines the power system planning for unit commitment, which is of great significance in power dispatching. A large amount of research work has been carried out on STLF. However, with the increase of load consumption and penetration of beyond the meter distributed energy generation systems, new challenges are brought to power load forecasting. Therefore, the probability density interval prediction which can more accurately reflect the uncertainty of power grid load is particularly important. In this study, a novel new day-ahead (24 h) short-term load probability density forecasting hybrid method with a decomposition-based quantile regression forest is proposed. First, the stationarity analysis is performed, and the load sequence is decomposed into several sub-models by Variational mode decomposition (VMD). Secondly, the influence of relevant factors such as weighted temperature and humidity index (WTHI) and day type is considered and extended to each sub-model sequence. Finally, a multi-step prediction strategy is proposed to predict the result of each sub-model using Quantile Regression Forest (QRF), and the prediction results are reconstructed to obtain the complete prediction probability density by Kernel density estimation (KDE). Specifically, the Bayesian optimization algorithm based on Tree-structured of Parzen Estimators (TPE) is adopted to optimize the hyperparameters. Furthermore, to verify the performance of the proposed method, the day-ahead short-term load forecasting of the proposed method and the contrast methods including decomposition-based methods and non-decomposition based methods were studied by the real load data of Henan Province, China. The probability density prediction obtained by the experiment indicates that the proposed method can acquire the narrowest prediction intervals at different confidence. [ABSTRACT FROM AUTHOR]