A novel online kernel ridge to forecast next-day electricity price. (2018) - Yang, Jiancheng; Yan, Weiwu; Xu, Renchao; Zhang, Xi
Accurate prediction for electricity price plays a great role in developing bidding strategy in the competitive energy market. In this brief, a novel variant of kernel ridge, namely multivariate slide-window online kernel ridge, is proposed to capture the nonlinearity and non-stationarity of electricity price as seasonal time series, by handling the timestamps in one period synchronously. Compared to traditional time series techniques like autoregressive integrated moving average (ARIMA) and other techniques like random forest and support vector regression, it provides much higher accuracy with lower computation cost, and can be easily integrated with other related data. Results from EPEX France spot market are presented.