Soft computing in renewable energy system modeling

Low tolerance for imprecision and uncertainty in achieving low-cost solutions has increased the use of soft computing techniques in the decision-making process for renewable energy systems. The domino effect of this advancing technique is evident in increased understanding of resource variability and intermittency, hybrid system optimization, and system fault classification. This chapter presents a background to soft computing techniques and their mathematical modeling while their merits and demerits were highlighted. The application of soft computing in renewable energy systems (with focus on wind, solar, and biomass resources) was categorized into three: predictive modeling, hybrid energy system optimization, and system classification. Each category was discussed and further substantiated with two case studies. While many applications of soft computing were observed in predictive modeling and hybrid energy system optimization, little is known about their applications in system classification. Finally, future prospects and research areas for soft computing in renewable energy were presented.