Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation

Abstract

Solar energy plays a vital role in the field of sustainable energy by providing clean, efficient and reliable alternative source of energy. Where, the output of solar energy systems is highly dependent on the solar radiation. Thus, accurate prediction of solar radiation is considered as a very important factor for such applications. In this paper, standalone adaptive neuro-fuzzy inference system and hybrid models have been developed to predict monthly global solar radiation from different meteorological parameters such as sunshine duration S(h), and air temperature. The proposed hybrid models include particle swarm optimization, genetic algorithm and differential evolution. To evaluate the capability and efficiency of the proposed models, several statistical indicators such as; root mean square error, co-efficient of determination and mean absolute bias error are used. All prediction models’ results showed good agreements with measured datasets. The performance evaluation over different statistical indicators showed high correlation for all developed modules. Whereas, hybrid particle swarm optimization has achieved the best statistical indicators over all models in training and testing models. A detailed comparison with other studies is carried out to validate the prediction accuracy and suitability of the proposed models. The results showed that the developed hybrid models have the most reliable and accurate estimation capability and deemed to be the efficient methods for predicting global solar radiation for various applications.