An Empirical Study on Enhancing Renewable Energy Efficiency through Information Entropy

Abstract

One of the most important things that many countries might do to solve their energy crises and their environmental problems is to promote the usage of renewable energy sources (RES). As the global population and economy continue to expand, more energy would be required, making diversification of energy sources essential to provide a reliable supply, stimulate economic growth, and promote the creation of a more sustainable energy infrastructure. In this research, a model is developed based on integrated Shannon entropy and Evaluation based on distance from average solution (EDAS) to find the optimal RES among the various sources for sustainable development planning. The results that are obtained from the integrated Shannon and EDAS are optimized using a Genetic Algorithm (GA) to obtain the optimal ranking. Finally, the obtained result demonstrated that the Solar Photo-voltaic (PV) and Wind energy sources obtained the highest ranking among all the other RES. The obtained appraisal score of solar PV and wind energy before the optimization is 0.8242 and 0.7864 respectively and after optimization, the appraisal score is 0.8412 and 0.8405. This shows that solar PV and wind energy are the optimum solutions for sustainable development and meeting future demand.