Fuzzy Logic and ANFIS based Short Term Solar Energy Forecasting

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Meera Viswavandya, Bakdevi Sarangi, Asit Mohanty, Sthitapragyan Mohanty


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 all other sources of renewable energy. We know that the solar Energy is very irregular so the result output of solar voltaic systems (SPV) diverted by the atmospheric nature like temperatures, humidity, wind velocity, solar irradiance and other climatologically facts. It�s necessary to prediction of solar energy is most important to minimise uncertainty in power harness from solar photovoltaic system. In this work fuzzy logic model and ANFIS model have been developed for manipulating solar irradiation (w/m2) data to forecasting short term solar energy. In the month of September 2017 has been monitoring for an hourly data of solar irradiance used as input and actual desired output. In the present paper sets the Normalization of input and desired output in between 0.1 to 0.9 for reducing confluence problems. Acquired results are match up to the manipulated data and get valid result. The implementation of the model is estimated on the basis of mean absolute percentage error.

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How to Cite
, M. V. B. S. A. M. S. M. (2018). Fuzzy Logic and ANFIS based Short Term Solar Energy Forecasting. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(4), 631–636. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1582