Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

Although the output of a photovoltaic power generation system is significantly positively correlated with solar irradiance, the latter variable is intermittent, random, and volatile. Volatility in solar irradiance is particularly marked when weather conditions are complex, and so, this factor has proved to be difficult to predict. A neural network (NN)-based approach is applied for short-term predictions in this study based on a timescale that encompasses the amount of irradiance each hour throughout the next day. Thus, a backpropagation NN (BPNN), a radial basis function NN (RBFNN), and an Elman NN (ENN) were selected for use in this analysis. A predictive model was established to evaluate the accuracy of different approaches, given variable meteorological conditions. To reduce the influence of solar irradiance, samples used for forecasts were subdivided into spring, summer, fall, and winter, and the forecast results of sunny and rainy as well as cloudy days in different seasons were investigated. The results of this study reveal that the predictive accuracies of the BPNN and RBFNN were poor on rainy and cloudy days, while the efficiency of the ENN was high and stable in variable meteorological conditions.