Dynamic Feature Selection for Solar Irradiance Forecasting Based on Deep Reinforcement Learning
Cheng Lyu, Sara Eftekharnejad, Sagnik Basumallik, Chongfang Xu
Abstract
A large volume of data is typically needed to achieve an accurate solar generation prediction. However, not all types of data are consistently available. Various research efforts have addressed this challenge by developing methods that identify the most relevant features for predicting solar generation. However, the optimal features vary with different weather patterns, making it impossible to select a fixed set of optimal features for all weather patterns. This study develops a new framework to accurately predict solar irradiance using dynamically changing optimal features. The developed model first incorporates feature extraction with clustering techniques to identify representative weather data from a dataset. Next, using deep reinforcement learning (DRL), a new feature selection method is developed to yield the minimum features required to accurately forecast solar irradiance from representative data. Benefiting from the model-free nature of DRL, the developed method is adaptive to various weather conditions, and dynamically alters the selected features. Case studies using real-world data have shown that the developed model significantly reduces the volume of data required for accurate irradiance forecasting for different weather patterns.