Solar Forecasting using ANN with Fuzzy Logic Pre-processing

Abstract Lack of predictability of solar power remains one major hindrance to the introduction of large-scale solar energy production. Comprehensive solar forecasting technologies are required to manage the intermittent nature of solar energy supply. However, one of the most challenging aspects of solar forecasting is the requirement for very short-term forecasting (in terms of minutes ahead) due to cloud movement, ambient temperature variation and humidity levels, which result in rapid ramp up and ramp down rates. This paper proposes an improved solar forecasting algorithm based on artificial neural network (ANN) model with fuzzy logic pre-processing. A three-layer (input layer, hidden layer and output layer) feed forward with back-propagation model is proposed as the neural network training algorithm. In this paper, an error correction factor based on the previous 5-min forecasted output is included to the input layer to minimize the forecast error. This is expected to produce a more accurate forecast. Weather information is a key input for the ANN model. In the case of rapid changes in solar irradiation or temperature on the forecast day, produced solar power changes greatly and forecast error would sensibly increase. In traditional prediction methods, the ANN uses all similar days’ data to learn the trend of similarity. However, learning all similar days’ data is quite complex, and it does not help if weather conditions change suddenly during the same day. Therefore, it is necessary to integrate into the ANN, a pre-processing stage that could perform real time analysis of weather information and factor it into the forecasting output. A fuzzy pre-processing toolbox is introduced into the proposed ANN model to find data correlation between cloud cover, temperature, wind speed, and wind direction with irradiance value. The accuracy of the proposed forecasting algorithm is compared with other ANN forecast algorithms.