A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM

Under the liberalization and deregulation of the power industry, price forecasting has become a cornerstone for market participants' decision-making such as bidding strategies and purchase plans. However, the exclusive nonlinearity dynamics of electricity price is a challenge problem that largely affects forecasting accuracy. To address this task, this paper presents a hybrid forecasting framework for short-term electricity price forecasting by exploiting and mining the important information hidden in the electricity price signal. Moreover, a hybrid feature selection method (HFS) is introduced into the forecasting strategy. To exhibit the dynamical characteristics of electricity price, we primarily perform a singular spectrum analysis (SSA)-based systematic analysis process by using the merit of SSA and analyzing the multiple seasonal patterns of short-term electricity price series, providing a meaningful representation of the hidden patterns and time-varying volatility of electricity price series. Aiming at selecting the key features, the candidate variables are constructed considering the dynamic behavior of price series; further, to capture the optimal features from the candidates, the correlation threshold θ is defined for the adjustable parameters in HFS and optimally determined by the intelligent search algorithm. Additionally, triangulation based on the Pearson, Spearman and Kendall rank correlation coefficient is performed to strengthen the reliability of the proposed method. The proposed hybrid forecasting framework is validated in the New South Wales electricity market, which demonstrates that the developed approach is truly better than the benchmark models used and a reliable and promising tool for short-term electricity price forecasting.