A novel reinforced online model selection using Q-learning technique for wind speed prediction

Abstract

An accurate wind speed prediction is a fundamental prerequisite for enhanced wind energy integration with grid. Existing forecasting models are trained on the wind speed data. And these models rely on global accuracy without considering local variation of wind. Due to the local variation of wind, performance of each model varies for every time-step. As a result, this paper implements a robust and novel hybrid framework i.e. online model selection using Q-learning (OMS-QL) provided forecasting model pool (FMP). The proposed framework is the first model developed for online selection of best forecasting model dynamically using reinforcement learning (RL) approach. The proposed framework is mainly clustered into two parts: FMP, and Q-learning agent for online model selection. First, FMP is constructed using nine robust approaches, which are trained on the wind speed data. Then, Q-learning agent is developed to dynamically select the best prediction approach at every time-step for improved accuracy. Two experiments are conducted using the real-time wind speed datasets. Experimental results indicate that the proposed OMS-QL framework improved by 47% and 48% in both case studies compared to benchmark models.