FML-based decision support system for solar energy supply and demand analysis

Because of the coming of high oil price and the trend of curbing the greenhouse gas emission, promoting the establishment of renewable energy is regarded as one of the main strategies in the world. Electricity supply in Taiwan is highly dependent on overseas imports. As a result, promotion of development and use of renewable energy not only increases the diversification of energy sources but also achieves a win-win-win situation for energy safety, environmental protection, and economic development. In Taiwan, wind power, solar energy, and bio-fuel are the three mainly promoted renewable energies, while in this paper we focus on the solar energy. This paper proposes a fuzzy markup language (FML)-based decision support system for the supply-demand analysis of the solar energy to discuss if the photovoltaic (PV)-generated electricity can supply enough one for the PV-installed household. First, the domain experts construct the ontology for solar energy supply and demand analysis (SESDA). Then, according to the power generation level from the installed PV system, appliances power consumption level from the housing loads, daily rainy probability, and today temperature forecast, the proposed system infers the power purchase possibility and then stores the results in the SESDA repository. The household could retrieve the results to try to improve his habit of electricity utilization to save electricity.