Short-term wind power forecasting by bidirectional attention mechanism LSTM and its probability interval prediction by sliding-window KDE

Abstract

Deterministic point wind power forecasting (DP-WPF) and its probability interval prediction (PIP) are indispensable to short-term peak alleviation and frequency regulation in power systems with large-scale wind power injection. To improve short-term DP-WPF by long short-term memory (LSTM), a horizontal/vertical bidirectional feature attention (BFA) based LSTM model is proposed. More specifically, the BFA-LSTM model has three parts: first, multivariate time series are fed into LSTM to extract long-short-term temporal features; second, the LSTM outputs are processed horizontally as well as vertically for retrieving step-wise/multistep-wise temporal features, respectively, namely, in the bidirectional attention sense; third, both the horizontal and vertical attention weights are adaptively adjusted according to the feature importance in DP-WPF. Cases comparison shows that the suggested modeling is stably superior to most common counterparts. To address PIP by kernel density estimation (KDE), sliding-window KDE is leveraged for probability analysis. More precisely, probability density functions (PDF) and probability intervals are estimated with sliding-window samples, which are non-parametric operations and involve finitely many local samples. Superior performances of PIP by sliding-window KDE at different confidence levels indicate that the sliding-window PDF approach is highly effective in contrast to those with all samples.