Improvement in artificial neural network-based estimation of grid connected photovoltaic power output |
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| Affiliation: | 1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region;2. School of Management, University of Texas at Dallas, Richardson, TX, USA;3. Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region;1. Laboratory of Environmental and Energy Efficient Design of Buildings and Settlements, Department of Environmental Engineering, Democritus University of Thrace, Vas.Sofias 12, Xanthi 67 100, Greece;2. Centre for Renewable Energy Sources and Saving, Solar Thermal Systems Department, 19th km Marathon Ave., Pikermi 19009, Greece;3. Process Equipment Design Laboratory, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece;1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy;2. Dipartimento Energia, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy;1. School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin, 300387, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China;3. Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, 226-8503, Japan |
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| Abstract: | This paper presents a method to improve the accuracy of artificial neural network (ANN)–based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression. |
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| Keywords: | Photovoltaic Power output Artificial neural network Solar zenith angle Solar azimuth angle Data-driven methods |
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