Short‐term load forecasting based on a generalized regression neural network optimized by an improved sparrow search algorithm using the empirical wavelet decomposition method

Abstract

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.

Coal price forecasting using complete ensemble empirical mode decomposition and stacking‐based ensemble learning with semisupervised data processing

“…The effectiveness of the model is further examined using statistical measures including Mean Absolute Percentage Error (MAPE), Root-Mean-Square Error (RMSE), and the Coefficient of Determination (R 2 ). The formulas used to compute these metrics are shown in Equations ( 15)- (18), and the results are quantitatively displayed in Figures 8 and 9.…”

“…Chai et al 17 introduced a forecasting model based on wavelet neural networks, aiming to facilitate real-time monitoring and precise forecasting of photovoltaic power generation. Fan et al 18 utilized a generalized regression neural network in combination with empirical wavelet decomposition and the sparrow search algorithm to achieve accurate short-term electric load forecasting. Neural-network-based models have excellent nonlinear fitting ability, and the forecasting accuracy can be improved by optimizing the structure and hyperparameters, whereas these types are less interpretable and easy to overfit Lu et al 5 Support Vector Machine (SVM) and its derivative algorithm have been tried by some scholars to forecast energy prices due to their prominent ability in solving small sample and nonlinear problems.…”