Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems

This paper introduces a dynamic forecasting of irradiance and ambient temperature. The medium term forecasting (MTF) gives a daily meteorological behaviour. It consists of a neuro-fuzzy estimator based on meteorological parameters’ behaviours during the days before, and on time distribution models. As for the short term forecasting (STF), it estimates, for a 5 min time step ahead, the meteorological parameters evolution. It is ensured by the Auto-Regressive Moving Average (ARMA) model of the MTF associated to a Kalman filter. STF uses instantaneous measured data, delivered by a data acquisition system, so as to accomplish the forecast. Herein we describe our method and we present forecasting results. Validation is based on measurements taken at the Energy and Thermal Research Centre (CRTEn) in the north of Tunisia. Since our work delivers accurate meteorological parameters forecasting, the obtained results can be easily adapted to forecast any solar conversion system output.