Abstract
When a large percentage of energy (>90%) is generated by fossil fuel, carbon dioxide emissions increase the greenhouse effect. Therefore, renewable, sustainable, and economically viable energy sources are needed as alternatives to fossil fuels. The facilities and installation costs for generating renewable energy is much higher than the cost of fossil fuel facilities. Thus, governments need effective policies, regulations, and incentive programs to promote the usage of renewable energy. Renewable energy can be classified into different categories, including offshore and onshore wind power, photovoltaic solar, and geothermal. The policies used for promoting specific categories vary significantly. These policies depend on the policy goals, regulations, taxation, incentives and promotional schemes. The purpose of this study is to apply clustering techniques and AHP to analyze types of renewable energies and their attributes with respect to economic factors, energy resource and supply, and environmental effects. AHP method is used to evaluate actions that can resolve challenges found in development of renewable energy. The study provides scientific results to help the government plan renewable energy policies. The data for the case study are collected from Taiwan’s renewable energy statistics related to PV cells, wind farms, ocean thermal energy, geothermal energy, hydro power, and solid waste fuels. The research will have four major results and findings. (1) Constructing models for analyzing renewable energy policies using data mining techniques, (2) Using seven categories of renewable energy sources, i.e., wind power, photovoltaic, geothermal and solid waste power in Taiwan, as specific renewable energy types to find the best promotional policy. (3) Providing reliable advice to government (and the means to effectively analyze given scenarios) for policy planning and execution. (4) Giving suggestions of the renewable policy from some benchmarking countries and providing some strategies from another countries.