Journal
IEEE JOURNAL OF PHOTOVOLTAICS
Volume 9, Issue 3, Pages 832-841Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPHOTOV.2019.2898521
Keywords
Correlation analysis; correlation coefficient; mean absolute percentage error (MAPE); prediction model; regression analysis; weather variable
Funding
- 2017 Open R&D Program of Korea Electric Power Corporation [R17XH02]
- New and Renewable Energy Technology Program of the Korea Institute of Energy Technology Evaluation and Planning - Ministry of Trade, Industry and Energy, Republic of Korea [20183010014260]
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With increasing installations of photovoltaic (PV) systems, interest in power forecasting has also increased. Inaccurate forecasts would result in substantial economic losses and system reliability issues. The correlation between weather variables and PV power is critical to ensure the efficient use of energy in PV systems. A key step toward accurate power forecasting is estimating the output from a PV system based on known environmental input data. In this research, all available weather data are used to predict the PV power. Meteorological and power data are then analyzed using a statistical approach to identify the order of significance of the input variables. Then, a predictive model is suggested as a function of irradiance, ambient temperature, wind speed, and relative humidity. The model produces a root mean square error of 4.957% and a mean absolute percentage error of 5.468% during the measurement period and over the entire range of irradiation.
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