Application of the diagonal recurrent wavelet neural network to solar irradiation forecast assisted with fuzzy technique

It is expected that solar energy plays an important role in the strategy of sustainable energy before long. In the case of various solar energy applications, accurate forecast of solar irradiation is increasingly required in recent years. Thanks to the progress of artificial intelligence. its application to various engineering fields vitalizes many conventional techniques. Forecast of solar irradiation is one of the techniques that benefit a lot from the progress, e.g., the progress of artificial neural networks (ANNs). Comparatively, various irradiation forecast models based on ANN perforin much better in accuracy than many conventional prediction models. However, a fact could not be neglected that most of such existing ANN-based models have not yet satisfied researchers and engineers in forecast precision so far. and the generalization capability of these networks needs further improving. Combining the prominent dynamic characteristics of recurrent neural network (RNN) with the enhanced ability of wavelet neural network (WNN) in mapping nonlinear functions. it diagonal recurrent wavelet neural network (DRWNN) is newly established in this paper so as to carry out fine forecasting of the hourly global solar irradiance. Some additional steps, e.g., using fuzzy technique to apply historical information of cloud cover to sample data sets for network training and the forecasted cloud cover in weather program to network input for the irradiation Forecasting, are adopted to help enhancing forecast precision. Besides, if specially scheduled 2-phase-training algorithm is adopted. As an example, an hourly irradiance forecast is completed using the sample data set in Shanghai, and comparisons between irradiation models show that the DRWNN model is definitely more accurate. (C) 2008 Elsevier Ltd. All rights reserved.