A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

Liu Liu,Jin Jin,Gao Gao

doi:10.3390/en12081520

Abstract

Electrical power system forecasting has been a main focus for researchers who want to improve the effectiveness of a power station. Although some traditional models have been proved suitable for short-term electric load forecasting, its nature of ignoring the significance of parameter optimization and data preprocessing usually results in low forecasting accuracy. This paper proposes a short-term hybrid forecasting approach which consists of the three following modules: Data preprocessing, parameter optimization algorithm, and forecasting. This hybrid model overcomes the disadvantages of the conventional model and achieves high forecasting performance. To verify the forecasting effectiveness of the hybrid method, 30-minutes of electric load data from power stations in New South Wales and Queensland are used for conducting experiments. A comprehensive evaluation, including a Diebold-Mariano (DM) test and forecasting effectiveness, is applied to verify the ability of the hybrid approach. Experimental results indicated that the new hybrid method can perform accurate electric load forecasting, which can be regarded as a powerful assist in managing smart grids.

Highlights

Electric load forecasting acts an important part in power station operations, such as the expansion of power generation, dispatch scheduling of generation production, maintenance, and the insurance of continuously supplied electric power [1]
An accurate electric load forecasting model can assist the power station in the management of electricity and the arrangement of operations, but is able to reduce the loss of auxiliary power, which enhances the stability and the economic benefits of the station
This paper proposes a new hybrid approach combining ensemble empirical mode decomposition (EEMD), a Whale Optimization Algorithm (WOA), and a support vector machine (SVM)

Summary

Introduction

Electric load forecasting acts an important part in power station operations, such as the expansion of power generation, dispatch scheduling of generation production, maintenance, and the insurance of continuously supplied electric power [1]. To obtain a satisfying prediction result, the need to develop an accurate and effective electric load forecasting system is intensely high. To obtain an accurate forecasting result for electric power stations, many short-term predicting methods were introduced, and those can mainly be classified into three categories: conventional methods, modern methods, and hybrid methods. Conventional methods include multi-linear regression analysis, time series, state space models, general exponential smoothing, and knowledge-based methods [3,4,5,6,7,8].

Methods

Results

Discussion

Conclusion