Affiliation:
1. Department of Electrical Engineering, National Chung-Cheng University, 168 University Road, Minhsiung, Chiayi 62102, Taiwan
2. Department of Electrical Engineering, National Taipei University of Technology, Section 3, Chung-hsiao E. Road, Taipei 10608, Taiwan
3. Centre of Electrical Energy Systems, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
Abstract
The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.
Subject
General Materials Science,Renewable Energy, Sustainability and the Environment,Atomic and Molecular Physics, and Optics,General Chemistry
Cited by
88 articles.
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