The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

Abstract

Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting. regression analysis was introduced to forecast short-term power load. Zhao et al. [6] presented an improved grey model optimized by a new optimization algorithm Ant Lion Optimizer for annual power load forecasting and the rolling mechanism was introduced to improve the forecasting accuracy. Coelho et al. [7] used a novel hybrid evolutionary fuzzy model with parameter optimization for load forecasting in a MG scenario. As can be seen from the above references, this kind of forecasting method has a mature theoretical basis, and its calculation process is simpler and easier to understand; however, this kind of method is unable to deal with non-linear forecasting problem due to theoretical limitation, its application object is often relatively simple, and the prediction accuracy usually fails to meet the expectations.With the development of modern computer technology and the continuous improvement of mathematical theory, intelligent forecasting methods are favored by many scholars for deep research. Artificial neural network (ANN) and support vector machine (SVM) are usually attractive to many scholars for solving short-term power load forecasting problems. As shown in paper [8], an artificial neural network (ANN) model with a back-propagation algorithm was presented for short-term load forecasting in micro-grid power systems. In paper [9], several univariate approaches based on neural networks were proposed and compared, which included multi-layer perceptron, radial basis function neural network, generalized regression neural network, fuzzy counter propagation neural networks, and self-organizing maps. Rana et al. [10] introduced an advanced wavelet neural networks for very short-term load forecasting, and the complex electricity load data was decomposed into different frequencies by the proposed method to separately realize the load forecasting. In paper [11], an effective short-term load forecasting model based on the generalized regression neural network with decreasing step fru...

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