Short time solar power forecasting using P-ELM approach - Shuqi Shi; Boyang Liu; Long Ren; Yu Liu (Scientific Reports)
Abstract Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting. The performance of the P-ELM algorithm is evaluated using mean absolute error (MAE) and root mean square error (RMSE), and it is compared with the extreme learning machine (ELM) algorithm. The results indicate that the P-ELM algorithm achieves higher accuracy in short-term prediction, demonstrating its suitability for ensuring accuracy and reliability in real-time solar power forecasting.