Problem Of Short-term Load Forecasting Research Articles

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting.

Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This article proposes a novel hybrid hierarchical deep-learning (DL) model that deals with multiple seasonality and produces both point forecasts and predictive intervals (PIs). It combines exponential smoothing (ES) and a recurrent neural network (RNN). ES extracts dynamically the main components of each individual TS and enables on-the-fly deseasonalization, which is particularly useful when operating on a relatively small dataset. A multilayer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS. To improve the internal TS representation and thus the model's performance, RNN learns simultaneously both the ES parameters and the main mapping function transforming inputs into forecasts. We compare our approach against several baseline methods, including classical statistical methods and machine learning (ML) approaches, on STLF problems for 35 European countries. The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations. In fact, it outperforms both statistical and state-of-the-art ML models in terms of accuracy.

An Improved Self-Organizing Migration Algorithm for Short-Term Load Forecasting with LSTM Structure Optimization

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM.

Research on short-term forecasting of power load based on big data BP neural network

The power network system is an indispensable part of the economy development, which directly affects the stable operation of various industries and people’s daily life. During the stable operation of the power system, the prediction of the power load plays an important role in the load scheduling of the power system. Aiming at the problem of short-term load forecasting of power system, this paper established a short-term forecasting model of power load based on the BP neural network forecasting model through the collection of big data and modified the network weights and thresholds through model training. Finally, a short-term prediction of the power load of a certain community was carried out. The results show that the prediction model based on BP neural network can accurately predict the short-term power load with small prediction errors and good prediction performance. It can meet the precision requirements of power system operation scheduling.

A Comprehensive Study of Random Forest for Short-Term Load Forecasting

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts.

Short-term residential load forecasting using Graph Convolutional Recurrent Neural Networks

The abundance of energy consumption data collected by smart meters has inspired researchers to employ deep neural networks to solve the existing problems in the power industry, such as Short-Term Load Forecasting (STLF). Most studies addressing the STLF problem, focus on historical load data and to achieve higher performance, they supplement costly accessible environmental and calendar variables with data. This approach ignores the existing spatial information among the consumers which subsequently might lead into the emergence of similar consumption patterns. In this paper, we present a Graph Convolutional Recurrent Neural Network, a novel neural architecture, for STLF problem that combines Graph Convolutional Networks and Long Short-Term Memory networks to simultaneously extract spatial and temporal information from users with similar consumption patterns. Our model captures spatial information from users without prior knowledge of their geographic location and does not rely on additional environmental variables. We compared our model to traditional baseline models for STLF using two real-world electricity consumption datasets. The empirical results demonstrate a significant improvement in prediction compared with the baseline models, exhibiting a 9.5% and an 8% improvement in terms of Mean Absolute Percentage Error, in the Customer Behavior Trials and Low Carbon London datasets, respectively.

Comparison of Deep Learning Architectures for Short-Term Electrical Load Forecasting Based on Multi-Modal Data

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

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

A hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting for holiday load forecasting

In short-term load forecast (STLF), forecasting holiday load is one of the most challenging problems. Aimed at this problem, a hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting (XGBoost) is presented. It divides holiday STLF problem into the predictions for proportional curve and daily extremum of electricity demand, which are relatively independent and relate to different factors. It is benefit for holiday STLF by task decomposing. Based on the shape similarity measured by Euclidean distance, the proportional curve is predicted by pattern sequence-based matching method. Daily extremum of electricity demand is predicted by XGBoost considering holiday classification. Finally, the predicted holiday load profile is synthesized from the above two prediction results with segment correction. The proposed methodology can analyze holiday load characteristics more effectively and get a higher prediction accuracy independent of sufficient data and expert experience. We evaluate our methodology with many algorithms on a real data set of one provincial capital city in eastern China. The results of case studies show that the proposed methodology gives much better forecasting accuracy with an average error 2.98% in holidays.

Blind Kalman Filtering for Short-Term Load Forecasting

In this work we address the problem of short-term load forecasting. We propose a generalization of the linear state-space model where the evolution of the state and the observation matrices is unknown. The proposed blind Kalman filter algorithm proceeds via alternating the estimation of these unknown matrices and the inference of the state, within the framework of expectation-maximization. A mini-batch processing strategy is introduced to allow on-the-fly forecasting. The experimental results show that the proposed method outperforms the state-of-the-art techniques by a considerable margin, both on load profile estimation and peak load forecast problems.

Short term electric load forecasting using hybrid algorithm for smart cities

Many day-to-day operation decisions in a smart city need short term load forecasting (STLF) of its customers. STLF is a challenging task because the forecasting accuracy is affected by external factors whose relationships are usually complex and nonlinear. In this paper, a novel hybrid forecasting algorithm is proposed. The proposed hybrid forecasting method is based on locally weighted support vector regression (LWSVR) and the modified grasshopper optimization algorithm (MGOA). Obtaining the appropriate values of LWSVR parameters is vital to achieving satisfactory forecasting accuracy. Therefore, the MGOA is proposed in this paper to optimally select the LWSVR’s parameters. The proposed MGOA can be derived by presenting two modifications on the conventional GOA in which the chaotic initialization and the sigmoid decreasing criterion are employed to treat the drawbacks of the conventional GOA. Then the hybrid LWSVR-MGOA method is used to solve the STLF problem. The performance of the proposed LWSVR-MGOA method is assessed using six different real-world datasets. The results reveal that the proposed forecasting method gives a much better forecasting performance in comparison with some published forecasting methods in all cases.

A New Hybrid Model for Short-Term Electricity Load Forecasting

Nowadays electricity load forecasting is important to further minimize the cost of day-ahead energy market. Load forecasting can help utility operators for the efficient management of a demand response program. Forecasting of electricity load demand with higher accuracy and efficiency can help utility operators to design reasonable operational planning of generation units. But solving the problem of load forecasting is a challenging task since electricity load is affected by previous history load, several exogenous external factors (i.e., weather variables, social variables, working day or holiday), time of day, and season of the year. To solve the problem of short-term load forecasting (STLF) and further improve the forecasting accuracy, in this paper we have proposed a novel hybrid STLF model with a new signal decomposition and correlation analysis technique. To this end, load demand time series is decomposed into some regular low frequency components using improved empirical mode decomposition (IEMD). To compensate for the information loss during signal decomposition, we have incorporated the effect of exogenous variables by performing correlation analysis using T-Copula. From the T-Copula analysis, peak load indicative binary variable is derived from value at risk (VaR) to improve the load forecasting accuracy during peak time. The data obtained from IEMD and T-Copula is applied to deep belief network for predicting the future load demand of specific time. The proposed data driven method is validated on real time data from the Australia and the United States of America. The performance of proposed load forecasting model is evaluated in terms of mean absolute percentage error (MAPE) & root mean square error (RMSE). Simulation results verify that, the proposed model provides a significant decrease in MAPE and RMSE values compared to traditional empirical mode decomposition based electricity load forecasting.

Short-term electrical load forecasting using hybrid ANN–DE and wavelet transforms approach

This paper proposes a hybrid artificial neural networks–differential evolution (ANN–DE) and wavelet transforms (WTs)-based approach to forecast the short-term electrical load demand data. The input data ranging from 1 h to several days have a significant effect on the accuracy of short-term load forecasting (STLF). Different forecasting methods with good accuracy are developed for solving the STLF problem based on time series analysis and artificial intelligence system. However, there are several disadvantages of ANNs such as falling in trap of local minima during its parameter optimization process. Therefore, to avoid this problem, in this paper, a hybrid approach is developed by combining the ANNs, WTs and evolutionary-based DE algorithm. Here, the ANNs are used to model the nonlinear and complex behavior of electrical load demand. WTs are used to improve the forecasting ability by decreasing the ill-behaved load demand series into a more stable series. The chance of falling into local optimum can be overcome by using the evolutionary-based DE algorithm. In order to show the effectiveness and suitability of the proposed hybrid approach, the load demand data are taken from California Independent System Operator Web site.

Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting

Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research determining which features of an STLF system is better suited for a specific data set. In many occasions both classical and modern methods coexist, providing combined forecasts that outperform the individual ones. This paper presents a thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as forecasting engines. The objective of this paper is to determine the circumstances under which each model shows a better performance. It analyzes one of the models currently in use at the National Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques. The parameters that are tested are the availability of historical data, the treatment of exogenous variables, the training frequency and the configuration of the model. The performance of each model is measured as RMSE over a one-year period and analyzed under several factors like special days or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions. However, the NN model performs more accurately under certain stress situations.

Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem

Due to the explosion in restructuring of power markets within a deregulated economy, competitive power market needs to minimize their required generation reserve gaps. Efficient load forecasting for future demands can minimize the gap which will help in economic power generation, power operations, power construction planning and power distribution. Nowadays, neural networks are widely used for solving load forecasting problem due to its non-linear characteristics. Consequently, neural network is successfully combined with optimization techniques for finding optimal network parameters in order to reduce the forecasting error. In this paper, firstly a novel evolutionary algorithm based on follow the leader concept is developed and thereafter its performance is validated by COmparing Continuous Optimizers experimental framework on the set of 24 Black-Box Optimization Benchmarking functions with 12 state-of-art algorithms in 2-D, 3-D, 5-D, 10-D, and 20-D. The proposed algorithm outperformed all state-of-art algorithms in 20-D and ranked second in other dimensions. Further, the proposed algorithm is integrated with neural network for the proper tuning of network parameters to solve the real world problem of short term load forecasting. Through experiments on three real-world electricity load data sets namely New Pool England, New South Wales and Electric Reliability Council of Texas, we compared our proposed hybrid approach to baseline approaches and demonstrated its effectiveness in terms of predictive accuracy measures.

Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method

Short term load forecasting (STLF) is one of the important issues in the energy management of power systems. Increasing the accuracy of STLF results leads to improving the energy system scheduling and decreasing the operating costs. Different methods have been proposed and applied in the STLF problem such as neural network, fuzzy system, regression-based and neuro-fuzzy methods. This paper investigates the knowledge-based method that has less computation time and memory compared with other methods. The accuracy of knowledge-based STLF method is improved by proposing a novel priority index for selection of similar days. In this index, temperature similarity and date proximity are simultaneously considered. In order to consider the effect of temperature in STLF more efficiently, the system is partitioned into the smaller regions and the STLF of the whole system is calculated by gathering the STLF of all regions. The proposed method is implemented on a sample real data, Iran's national power network, to show the advantages of the proposed method compared with Bayesian neural network and locally linear neuro-fuzzy methods in aspects of accuracy and computation time. It is shown that the proposed method decreases yearly mean absolute percentage error (MAPE), and generates more reliable load forecasting.

A hybrid metaheuritic technique developed for hourly load forecasting

Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short‐term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two‐stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems. © 2016 Wiley Periodicals, Inc. Complexity 21: 521–532, 2016

Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting

Using artificial neural networks (ANN) for short-term load forecasting is an efficient method to get the best result. Considered problem of short-term load forecasting shows that the accuracy of short-term forecasting models and methods significantly influences on the further planning of operating conditions at the modern electricity market. The obtained error for short-term load forecasting using the neural network algorithm is 2.78%.

English

Short-term load forecasting (STLF) plays an essential role in the economic system and save the country's electricity supply. In this paper, are used a neural network with genetic learning algorithm for forecasting the electric power load of Khorasan area in Iran. Because the importance of neural network inputs, select the optimal inputs is deducted errors system. Consumption load is a nonlinear function of various factors such as weather conditions and periodic changes. This paper proposed a new variable together with the data load and temperature parameters for the problem of STLF. The variable obtained from the load curves and effect of periodic changes. The obtained results indicate that the proposed variable is effective for forecasting the short term load in electric power systems Keywords—Short-term load forecast, Neural Network, Genetic algorithm. I. I ntro d uct io n Load forecasting is an important part of management of electric power systems. Power supply planning is a major objective of short-term load forecasting. High forecast error is concluded large loss in safe economical operation in the power system.

Comparative Study on Short-term Electric Load Forecasting Techniques

In this paper, the problem of short-term load forecasting is divided into load classification and forecasting. Load classification is needed to obtain meaningful load data as input to train forecasting models. To this end, k-NN and K-mean algorithms are presented. K-mean and kNN algorithms can handle seasonal load classification and daily load classification, respectively. The classified load data are used to train forecasting models, which are Artificial Neural Networks, Simple Exponential Smoothing, and ARIMA models. As a real case study, we tried to forecast the electric power load of the Republic of Korea. A comparison between the classified and non-classified load forecasts demonstrates the efficiency of the proposed method.

Short-term load forecasting using a kernel-based support vector regression combination model

Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance. To get the optimal kernel function of STLF problem, this paper proposes a kernel-based SVR combination model by using a novel individual model selection algorithm. Moreover, the proposed combination model provides a new way to kernel function selection of SVR model. The performance and electric load forecast accuracy of the proposed model are assessed by means of real data from the Australia and California Power Grid, respectively. The simulation results from numerical tables and figures show that the proposed combination model increases electric load forecasting accuracy compared to the best individual kernel-based SVR model.