Predicating photovoltaic power generation using an improved hybrid heuristic method

Due to the rapid growth of energy consumption and environmental issue, the use of photovoltaic (PV) power, has attracted many attentions since such energy is renewable and clean. This study proposes a simple yet effective method for PV power prediction in which a dividing-stage strategy is investigated and a hybrid heuristic, denoted as GAPSO (genetic algorithm and particle swarm optimization) is applied to optimize the BP (Back-Propagation) neural network. In comparison with GABP neural network, the hybrid GAPSOBP neural network achieves a higher accuracy for PV power prediction. Moreover, by incorporating the dividing-stage strategy into GAPSOBP neural network, the prediction accuracy is improved even further.