Abstract
Accurate power-load forecasting for the safe and stable operation of a power system is of great significance. However, the random non-stationary electric-load time series which is affected by many factors hinders the improvement of prediction accuracy. In light of this, this paper innovatively combines factor analysis and similar-day thinking into a prediction model for short-term load forecasting. After factor analysis, the latent factors that affect load essentially are extracted from an original 22 influence factors. Then, considering the contribution rate of history load data, partial auto correlation function (PACF) is employed to further analyse the impact effect. In addition, ant colony clustering (ACC) is adopted to excavate the similar days that have common factors with the forecast day. Finally, an extreme learning machine (ELM), whose input weights and bias threshold are optimized by a bat algorithm (BA), hereafter referred as BA-ELM, is established to predict the electric load. A simulation experience using data deriving from Yangquan City shows its effectiveness and applicability, and the result demonstrates that the hybrid model can meet the needs of short-term electric load prediction.
Highlights
Short-term load forecasting is an important component of smart grids, which can achieve the goal of saving cost and ensure a continuous flow of electricity supply [1]
Hu et al [10] put forward a generalized regression neural network (GRNN) optimized by the decreasing step size fruit fly optimization algorithm to predict the short-term power load, and the proposed model showed a better performance with a stronger fitting ability and higher accuracy in comparison with traditional back propagation neural networks (BPNN)
Wang [12] successfully conducted a support vector machine (SVM) for short-term load forecasting, and the results demonstrated the excellence of the forecasting accuracy as well as computing speed
Summary
Short-term load forecasting is an important component of smart grids, which can achieve the goal of saving cost and ensure a continuous flow of electricity supply [1]. Hu et al [10] put forward a generalized regression neural network (GRNN) optimized by the decreasing step size fruit fly optimization algorithm to predict the short-term power load, and the proposed model showed a better performance with a stronger fitting ability and higher accuracy in comparison with traditional BPNN. Considering the difficulty of the parameter determination that appeared in SVM, the least squares support vector machine (LSSVM) was put forward as an extension, which can transform the second optimal inequality constraints problem in original space into an equality constraints’ linear system in feature space through non-linear mapping and further improve the speed and accuracy of the prediction [13].
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