Forecasting of Global Solar Insolation Using Ensemble Kalman Filter Based Clearness Index Model

This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints such as latitude and whole precipitable water content in vertical column of that location, have been used. These parameters can be easily measurable with global positioning system (GPS). The aforesaid model has been developed by using the above datasets generated from different locations of India. The model has been verified by calculating theoretical global insolation for different sites covering east, west, north, south and central region with the measured values from the same locations. The model has also been validated on a region, from which data has not been used during the development of the model. In the model clearness index coefficients (KT) are updated using ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using KT model and EnKF algorithm have also been verified by comparing two popular algorithms namely recursive least square (RLS) and Kalman filter (KF) algorithms. Minimum mean absolute percentage error (MAPE), mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimation using EnKF in one of the location is 2.4%, 0.0285 and 0.9866 respectively.