DAILY INSOLATION FORECASTING USING A MULTI-STAGE NEURAL NETWORK

So far a single-stage neural network has been proposed to forecast the insolation of the next day. The mean error of the forecast insolation by the single-stage neural network is about 30%. In this paper, a multi-stage neural network is developed for further reduction of the mean error. A first-stage neural network forecasts the average atmospheric pressure of the next day from atmospheric pressure data of the previous day. A second-stage neural network forecasts the insolation level of the next day from the average atmospheric pressure and weather data of the previous day. A third-stage neural network forecasts the insolation of the next day from the insolation level and weather data of the previous day. Meteorological data at Omaezaki, Japan in 1988–1993 are used as input data, and the insolations in 1994 are forecast. The insolations forecast by the multi-stage and the single-stage neural networks are compared with the measured ones. The results show that the mean error reduces from about 30% (by the single-stage) to about 20% (by the multi-stage).