Abstract
Accurate forecasting of solar energy is a key issue for a meaningful integration of the solar power plants into the grid. Solar photovoltaic technology is most preferable and vital technology in comparison with all other sources of renewable energy. We know that the solar energy is very irregular so the output of solar photo voltaic system (SPV) is diverted by the atmospheric conditions like temperatures, humidity, wind velocity, solar irradiance and other climatological facts. It’s necessary to predict solar energy to minimize the uncertainty in power harnessing from solar photovoltaic system. In this work fuzzy logic model and ANFIS model have been developed for manipulating solar irradiation (w/m2) data to forecast short duration solar irradiation. Further the normalization in between the input and output is set in between 0.1 and 0.9 for reducing confluence problems. Acquired results are matched up to the manipulated data and valid results are found out.
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Viswavandya, M., Sarangi, B., Mohanty, S., Mohanty, A. (2020). Short Term Solar Energy Forecasting by Using Fuzzy Logic and ANFIS. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_63
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DOI: https://doi.org/10.1007/978-981-13-8676-3_63
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