Method For Short-term Load Forecasting Research Articles

A reinforcement learning-based online learning strategy for real-time short-term load forecasting

Real-time Short-Term Load Forecasting (STLF) is crucial for energy management and power system operations. Conventional Machine Learning (ML) methodologies for STLF are often challenged by the inherent variability in energy demand. To tackle the challenge associated with inherent variability, this paper presents a novel Reinforcement Learning (RL)-enhanced STLF method. Different from conventional methods, our method dynamically improves the STLF model by selecting optimal training data to capture recent power usage trends and possible variations in demand patterns. By doing so, our method can significantly reduce the impact of unforeseen fluctuations in real-time forecasting. In addition to the novel RL-enhanced STLF method, we propose a comprehensive evaluation framework, encompassing three key dimensions: accuracy, runtime efficiency, and robustness. Tested on three distinct real-world energy datasets, our RL-enhanced method demonstrates superior forecasting performance across three evaluation metrics by achieving accurate and robust predictions in real-time under varying scenarios. Furthermore, our approach provides uncertainty bounds for practical predictions applications. These results underscore the significant advancements made by our RL-based method in forecasting precision, efficiency, and robustness. We have made our algorithm openly accessible online to promote continued development and advancement of STLF methods.

Short-term load forecasting of regional integrated energy system based on spatio-temporal convolutional graph neural network

Accurate short-term load forecasting has significant reference value for the preliminary planning and subsequent coordinated control of regional integrated energy systems. However, main limitations are: (1) Insufficient exploration of the complex correlations among diverse loads and between loads and meteorological factors; and (2) Feature information extraction only from the time dimension, neglecting the spatial dimension, resulting in incomplete feature information extraction. This paper proposes a new method for short-term load forecasting, namely the Attention Spatial–temporal Graph Convolutional Network (ATSTGCN) method. Firstly, the Grey Relational Analysis (GRA) method is used to analyze the correlation between diverse loads, meteorological features, and load features, constructing the input data for the graph neural network. Then, synchronous graph convolution is introduced to extract information between features. Next, the spatio-temporal attention mechanism is incorporated into the synchronous graph convolution to capture spatio-temporal features and reduce information loss using a spatio-temporal window. Finally, a short-term load forecasting method based on ATSTGCN for regional integrated energy systems is constructed. Extensive simulation experiments are conducted on the publicly available dataset from Arizona State University’s online system. The experimental results show that compared to other methods, the proposed method improves MAPE, MAE, and RMSE by 5.6%, 11.7%, and 11.6%.

Short-term load forecasting based on CEEMDAN and dendritic deep learning

Short-term load forecasting (STLF) plays a key role in improving the operational efficiency of real-time electricity markets by reducing load uncertainty, optimising the allocation of generation resources and increasing the reliability of electricity supply. Despite significant advances in the field, achieving high accuracy in STLF remains a challenge due to the inherent complexity and volatility of load data. This study proposes a novel STLF method that synergistically combines the strengths of a dendritic neuron model (DNM), long short-term memory, and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to address these challenges. First, historical load data is decomposed into components of different frequencies using CEEMDAN. Then, LSTM is used to extract the temporal characteristics of these load subsequence components in combination with various influencing factors such as temperature and humidity. Uniquely, this study innovatively integrates a dendritic neuron model instead of the traditional fully connected layer, a structural innovation that enhances the model’s ability to explore the intrinsic correlations of the time series data in depth. This allows the model to process and predict each component individually, and then later reconstruct and sum these independent prediction results to obtain a final prediction value with higher accuracy. Experiments are conducted on the Panama electricity load dataset to compare the proposed dendritic learning model with several state-of-the-art models, including CrossFormer and Transformer. The results confirm that the proposed model has significantly better prediction accuracy.

Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.

A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

Electric vehicle (EV) charging load is greatly affected by many traffic factors, such as road congestion. Accurate ultra short-term load forecasting (STLF) results for regional EV charging load are important to the scheduling plan of regional charging load, which can be derived to realize the optimal vehicle to grid benefit. In this paper, a regional-level EV ultra STLF method is proposed and discussed. The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles, and then constructed by our collected EV charging transaction data in the field. Secondly, these usage degrees are combined with historical charging load values to form the input matrix for the deep learning based load prediction model. Finally, long short-term memory (LSTM) neural network is used to construct EV charging load forecasting model, which is trained by the formed input matrix. The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditional methods. In addition, load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us, and these fluctuation factors are used to assess the prediction accuracy of the EV charging load, together with the mean absolute percentage error (MAPE).

TPE-based Short-Term Load Forecasting Neural Network Structure Design and Parameter Optimization Method

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Much research on short-term load forecasting using neural networks has been carried out at home and abroad. However, how to obtain the optimal network and structure evolution accurately and quickly has evolved into a key challenge for short-term load forecasting, because the prediction effect of the neural network method is more easily affected by the existing network structure and parameters, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, this paper proposes a TPE-based neural network structure design and parameter optimization method for short-term load forecasting. This method realizes fast optimization of parameters of different types, structures, parameters, and input and output of deep neural networks, and is widely applied in Guangxi power grid. It is verified by an actual example that the method can effectively improve prediction accuracy.

Load2Load: Day-ahead load forecasting at aggregated level

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

Review of Short-Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods

Forecasting electricity load demand is critical for power system planning and energy management. In particular, accurate short-term load forecasting (STLF), which focuses on the lead time horizon of few minutes to one week ahead, can help in better load scheduling, unit commitment, and cost-effective operation of smart power grids. In the last decade, different artificial intelligence (AI)-based techniques and metaheuristic algorithms have been utilized for STLF by the researchers and scientists with varying degrees of accuracy and efficacy. Despite the benefits of implemented methods for STLF, many drawbacks and associated problems have also been observed and reported by the researchers. This paper provides a comprehensive review of hybrid deep learning models based on nature-inspired metaheuristic techniques for STLF with respect to the analysis of the results and accuracy. Moreover, it also provides the research findings and gaps that will assist the researchers to have an early awareness of all important benefits and drawbacks of these integrated STLF methods scientifically and systematically. Especially, the hybrid forecast models using artificial intelligence-based methods for smart grids are focused. Several performance indices are used to compare and report the accuracy of these techniques including mean absolute percentage error (MAPE). Multiple other parametric and exogenous variable details have also been focused to figure out the potential of the intelligent load forecasting techniques from the perspective of smart power grids.

Spatial–temporal short-term load forecasting framework via K-shape time series clustering method and graph convolutional networks

Load forecasting, especially short-term load forecasting, is of great significance to the safe operation of power grids and power system optimization. Historical data is commonly applied in the most of existing methods of load forecasting to predict the future data in one time step, which is considered to have a positive effect on the forecasting performance. However, a serious problem is ignored, which is the assumption that the interactions between end-users are often not considered. Such interaction could be considered as a paired non-Euclidean relationship, which could enhance the accuracy of forecasting effects. In this paper, we propose a graph convolutional network prediction framework based on K-shape time series clustering method for short-term load forecasting. User clusters with similar electricity consumption habits could be separated from a large number of users by the proposed framework. Simultaneously, the paired non-Euclidean relationship between users can also be captured by this framework. The proposed framework is evaluated by the dataset of business users in the southeastern United States. The results show that compared with baselines, the framework significantly improves the prediction accuracy in any scale of user groups, as well as future data in one time step and multi-time steps.

Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network

With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) and time convolutional network (TCN) into a hybrid network is proposed in this paper. Firstly, original electrical load sequence data with noise are decomposed into intrinsic IMF components with different frequencies and amplitudes based on the VMD method. Secondly, a combined load forecasting method based on the GRU and TCN network is proposed for the high and low-frequency load subsequent signals, respectively. Finally, the high and low-frequency signals forecasting results of the GRU and TCN network are rebuilt for the final load forecasting. The experiment results based on actual operation data (data set 1) and simulation data (data set 2), which show that the proposed method can reduce the forecasting error by 36.20% and 10.8%, respectively, in comparison with VMD-GRU. The reliability and accuracy of the proposed method is verified through the comparison with other methods such as LSTM, Prophet and XG Boost.

A Reliable Short‐Term Power Load Forecasting Method Based on VMD‐IWOA‐LSTM Algorithm

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.

Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting

The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further enhance the input to the MLP by informing it explicitly with clustering information, which is a novel feature. All four models are designed, tested and evaluated using data from the Greek power system, although their development is generic and they could, in principle, be applied to any power system. The results obtained by the four models are compared to those of other STLF methods, using objective metrics, and the accuracy obtained, as well as convergence time, is in most cases improved.

Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

A Holistic Feature Selection Method for Enhanced Short-term Load Forecasting of Power System

Short-term load forecasting (STLF) is important for the operational security and economics of power system. However, most of STLF methods lack an efficient feature selection method to model the time series nonlinearities and feature interaction. In this paper, a new holistic feature selection method is presented. The feedforward long short-term memory network (F-LSTM) is proposed to learn the nonlinear mapping function between features and load. Then, a feature importance matrix is designed to reflect relevancy, redundancy and interaction among the candidate features. Moreover, a hybrid filter-wrapper approach is developed to select suitable features efficiently. The filter part separates useless information for the trained F-LSTM output. The wrapper part selects the optimal subset by fine-tuning the threshold. The results from an empirical study in Switzerland suggest that: 1) the selected subset of features shows high relevancy, low redundancy and high interaction, which is also consistent with the features selected by other feature selection methods, and 2) the proposed method has good prediction performance and can be applied to various artificial neutral network based short-term load forecasting models, which delivers an average 12.1% improvement.

A Similar Day Based Short Term Load Forecasting Method Using Wavelet Transform and LSTM

With the deregulation of electricity markets, short‐term load forecasting (STLF) has gained importance for the operation of power systems. However, an effective STLF model is hard to achieve as the load is affected by various factors. Here we present a STLF method based on similar day approach to predict the electricity usage 24 h ahead and by employing long short‐term memory (LSTM) and wavelet transform to further improve the forecasting accuracy. Compared with other methods, the proposed method achieves higher accuracy, and brings out the significance of using similar day's load, wavelet transform, and LSTM network. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Short-Term Load Forecasting and Temperature Load Extraction Based on CEEMDAN and TDIC

With the intensification of urbanization in various countries worldwide, the temperature load which is greatly affected by ambient temperature, such as summer cooling loads and winter heating loads, accounts for a rising proportion of the total urban load. It causes an increasing peak-to-valley load difference. However, due to the complex composition and strong randomness of the load, it is necessary to study the multi-scale and multi-period correlation between temperature. Based on this, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the temperature and load into multi-scale components. The time-dependent intrinsic correlation (TDIC) is proposed to analyze the local correlation between temperature and load in multiple periods under a multi-scale framework, and obtain the dynamic change characteristics of the correlation between temperature and load. Based on the TDIC analysis results, a suitable sample period for short-term load forecasting (STLF) and input temperature data can be selected. Finally, extreme learning machine optimized by particle swarm optimization (PSO-ELM) is used to forecast each component of the load. The proposed STLF method is validated on real-time data from the Pennsylvania-New Jersey-Maryland (PJM) Company in the United States. The proposed method has greatly reduced in both mean absolute percentage error (MAPE) and root mean square error (RMSE) compared with other traditional methods, and the temperature load that fluctuates with temperature in the day to be forecasted is extracted.

Short Term Load Forecasting using Metaheuristic Techniques

The power systems are important by using short term load forecasting (STLF) because it predicts the load in 24 hours ahead or a week ahead. The artificial neural network (ANN) using short term load forecasting brings good result in the predicted load because of its accurateness, easiness in the processing of data, construction of the model as well as excellent performances. The optimization value of ANN is found by different methods which consist of some weights. This manuscript explains the work of ANN with back propagation (BP), genetic algorithm (GA) as well as particle swarm optimization (PSO) for the STLF. The detailed work of the GA and PSO based BP is presenting in this paper which helps for its utilization in the STLF and also able to find the good result in the predicted load. Finally, the result of GA and PSO are compared by simulation and after that, it concluded, the PSO-BP is a good method for STLF using ANN.

Improving the Performance of Short-Term Load Forecast Using a Hybrid Artificial Neural Network and Artificial Bee Colony Algorithm Amélioration des performances de la prévision de la charge à court terme à l’aide d’un réseau neuronal artificiel hybride et d’un algorithme de colonies d’abeilles artificielles

Tools such as short-term load forecast (STLF) play an ever-important role in the operation and planning of power systems. Improving STLF accuracy could reduce heavy penalties and missed financial opportunities. In this article, a new hybrid forecast method is proposed to improve STLF accuracy. The proposed hybrid STLF method is based on artificial neural network (ANN) and artificial bee colony (ABC) algorithms. The ABC algorithm is used to optimize the learning procedure of ANN. New load modeling is presented based on historical and weather data. Bad data elimination and calendar effects are considered in the STLF procedure. The proposed hybrid STLF method is verified by forecasting the Bushehr province demand. The results proved that the proposed method significantly improved STLF accuracy.

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

Research on Power Load Forecasting and Visualization Method Based on Deep Neural Network

Short-term load forecasting (STLF) has attracted much attention in the last decade, which uses data from historical power grid operation monitor to forecast future power demand for a few days. Previous works on STLF mainly focus on features, which are widely used to capturing external factors in power system. There are also approaches based on physics, which use linear combination of active and reactive power consumption to model dynamics of power demand. However, in many real-world applications they don’t work well. In the self-service power grid, STLF is a very important part in power scheduling and planning. But STLF is a challenging task for its complex power demand and the effects of weather. In this paper, we study the problem of combination of complex and seasonality of power demand, as well as effect of external weather. Therefore, we come up with an approach, called DNNCast, it’s a model based on deep neural network, which can achieve a good accuracy and fast training speed. In the structure it uses a deep neural network to model various power loads and external weather conditions. Finally, with visualization tools we can wisely make decision about power scheduling and planning. Compared with the current STLF methods, our model has a better prediction effect, and faster in the experiments of the extended experiment on 133 real power datasets.