A novel composite neural network based method for wind and solar power forecasting in microgrids

Abstract

Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.