Abstract
For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.
Highlights
Due to the characteristic of being not easy to reserve, electricity suppliers need precise short term load forecasting results to guarantee the power supply and load dispatch of power plants and security strategies
The empirical mode decomposition (EMD) assumes that the original data set is derived from its inherent characteristics, and it can be decomposed into several intrinsic mode functions (IMFs) [40]
Note: * denotes that the H-EMD-support vector regression (SVR)-particle swarm optimization (PSO) model significantly outperforms other alternative models
Summary
Due to the characteristic of being not easy to reserve, electricity suppliers need precise short term load forecasting results to guarantee the power supply and load dispatch of power plants and security strategies. IMF, load can be forecast by an SVR model with only one suitable kernel function, successfully improving the forecasting performance, as demonstrated in Fan et al [35]. These IMFs contain random IMF and residual IMF, respectively. To demonstrate the superiority of the proposed model, the employed electric load data, collected from New South Wales (Australia) in two different sample sizes with 0.5-h type (i.e., 48 data points a day), are used to compare the forecasting performance among the proposed model and other compared models, namely, the original SVR model and the SVR-PSO model (hybridizing the PSO algorithm with the SVR model).
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