Solar irradiance forecasting using fuzzy logic and multilinear regression approach: A case study of Punjab, India - Sahil Mehta; Prasenjit Basak (International Journal of Advances in Applied Sciences, 2019)
The accurate forecasting of solar irradiance depends on various uncertain parameters like time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an important role in calculating PV power output. In this paper, a novel approach for forecasting of solar irradiance using flexible and accurate fuzzy logic and robust multi-linear regression approach has been proposed considering the above mentioned five variables. Based on the simultaneous consideration of those five variables, the solar irradiance is forecasted using the proposed methodology at a particular location in India, and the results are compared with the real time measured value of solar irradiance at that location on the days for which solar irradiance are forecasted. The proposed method is validated by comparing the results with real time data. The error analysis of the fuzzy logic based proposed system shows the root mean square error of 10.011 and mean absolute percentage error of 1.703%, while compared with real time data measured by instruments pyranometer, anemometer etc. The same results are found better while compared with the results obtained using multilinear regression approach.