Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm

• Published in: Energies (Basel) Vol. 13; no. 8; p. 1879
• Main Authors: Aprillia, Happy, Yang, Hong-Tzer, Huang, Chao-Ming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Accuracy; Algorithms; Clean energy; Climate change; Climate models; convolutional neural network; day ahead forecasting; Deep learning; Economic forecasting; Forecasting; Humidity; Long short-term memory; Meteorological data; Methods; Neural networks; Outdoor air quality; Photovoltaics; Power; PV power forecasting; Regression analysis; renewable energy; salp swarm algorithm; Short term; Support vector machines; Variables; Weather forecasting
• DOI: 10.3390/en13081879
• ISSN: 1996-1073

The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.