A new recursive dynamic factor analysis for point and interval forecast of electricity price

The functional principal component analysis (FPCA) is a recent tool in multivariate statistics and it has been shown to be effective for electricity price forecasting. However, its online implementation is expensive, which requires the computation of eigen-decomposition at each update. To reduce the arithmetic complexity, we propose a recursive dynamic factor analysis (RDFA) algorithm where the PCs are recursively tracked using efficient subspace tracking algorithm while the PC scores are further tracked and predicted recursively using Kalman filter (KF). From the latter, the covariance and hence the interval of the forecasted electricity price can be estimated. Advantages of the proposed RDFA algorithm are the low online complexity, and the availability of the prediction interval thanks to the KF framework. Furthermore, a robust extension is proposed to tackle possible non-Gaussian variation. Finally, the RDFA algorithm can be extended to predict electricity price in a longer period using a multi-factor model by capturing trends in different time horizon. Experimental results on the New England and Australian datasets show that the proposed RDFA approach is able to achieve better prediction accuracy than other conventional approaches. It thus serves as an attractive alternative to other conventional approaches to forecast electricity price and other related applications because of its low complexity, efficient recursive implementation and good performance.