Abstract
The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting.
Highlights
Electric power is a clean and efficient energy which plays an increasingly vital role in our daily life.Compared with traditional energy such as natural gas, coal and oil, electric power is more suitable and efficient for the requirement of environment-friendly society
Pappas et al [7] established an auto-regressive moving average (ARMA) model for short-term electric load forecasting, and the results showed the good performance of the proposed model
Based on the above considerations, this paper aims to establish an ensemble model combining variational mode decomposition (VMD) and an improved extreme learning machine (ELM) for short-term electric load forecasting
Summary
Electric power is a clean and efficient energy which plays an increasingly vital role in our daily life. Compared with traditional energy such as natural gas, coal and oil, electric power is more suitable and efficient for the requirement of environment-friendly society. In the field of power system planning, the accuracy of electric load forecasting is of great importance for energy generating capacity scheduling and power system management [1,2]. An overestimation may waste many energy resources and significantly improve the operational costs, and an underestimation will decrease the reliability of the power system and cannot meet the requirement of electricity utilization [3]. Accurate electric load forecasting is essential and significant for power systems. Because the electric load series is affected by many complicated influencing factors, it is really a challenging job to accurately predict the electric load
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