A recommender system for the optimal combination of energy resources with cost-benefit analysis

Owing to the over-exploitation of fossil fuels, many governments have been promoting renewable energy to resolve the limitation of fossil energy and environmental problems. Nevertheless, most of the electricity data lack of systematic analysis to provide useful information. Furthermore, due to the development of cloud technology, these big data vary in type and time. Without appropriate big data analysis and user interface, data would provide error messages. Besides, few of the websites are built for enterprise to provide suggestion as a recommender. In summary, this study intends to develop a recommender system including cloud data base, analytical module and user interface. Based on continuous Markov chain, we analyze data according to the historical electricity data; through time series analysis and multi-objective programming models, a long-term investment of renewable energy decision supports and the best combination of renewable energy can be revealed. The research integrates these modules to construct an enterprise-oriented cloud system. To ensure the effectiveness of the platform, validation test will be performed. The result demonstrates that the recommender system can be used to assist the company in making the best investment of renewable energy and the best combination of energy consumption.