Day-ahead electricity price forecasting based on panel cointegration and particle filter

An accurate forecasting of energy price is important for generation companies (GENCOs) to develop their bidding strategies or to make investment decisions. Nowadays, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price is volatile and its volatility changes overtime due to the uncertainties from the multi-market. This paper proposes a two-stage hybrid model based on panel cointegration and particle filter (PCPF). Panel cointegration (PC) model, which utilizes information of both the inter-temporal dynamics and the individuality of interconnected regions, provides powerful forecasting tool for electricity price. Particle filter (PF) has achieved significant successes in tracking applications involving non-Gaussian signals and nonlinear systems. This paper has two main focuses: (1) To expand the dimension of electricity price dataset from time series to panel data so that the dynamics of interconnected regions can be analyzed simultaneously and considered as a whole. (2) Regarding the model coefficients as a time-varying process, PF is used to forecast electricity price adaptively. In the case study, the proposed PCPF model is applied to the real electricity market data of PJM in the year 2008. Promising results show clearly the superior predicting behavior of the proposed modeling.

► A forecasting model capturing linear and nonlinear patterns has been developed. ► The proposed hybrid model has two stages: panel cointegration and particle filter. ► Panel cointegration expands the dimension of the dataset to panel data. ► Particle filter regards the model coefficients as a time-varying process. ► The hybrid model can forecast electricity prices adaptively.