Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning
This work addresses uncertainty quantification in distribution system load forecasting, offering an incremental improvement over existing prediction interval methods.
The paper tackles the problem of generating adaptive prediction intervals for electricity load forecasting by proposing an online reinforcement learning method that dynamically selects symmetric or asymmetric quantile pairs, resulting in improved interval quality and robustness against concept drift compared to traditional methods.
Prediction intervals offer an effective tool for quantifying the uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to unforeseen changes in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles. It relies on the online learning ability of reinforcement learning to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve the quality of PIs. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.