An ensemble solar power output forecasting model through statistical learning of historical weather dataset

Due to its economical and environmental benefits to society and industry, integrating solar power is continuously growing in many utilities and Independent System Operators (ISOs). However, the intermittent nature of the renewable energy brings new challenges to the system operators. One key to resolve this problem is to have a ubiquitously efficient solar power output forecasting system, so as to help enhance system reliability, improve power quality, achieve better generation scheduling and formulate superior bidding strategies in market. This paper proposes an ensemble learning method to forecast solar power output, combining the state-of-art statistical learning methods. The performance of the model is evaluated through comparing with a benchmark with different metrics, and the numerical results validate the effectiveness of the model.