Abstract
Due to the industrialization, people consume large amounts of fossil fuels and emit lots of carbon dioxide. Experts predict that fossil fuels are going to use up and the temperature of earth increases yearly. The climate change even results in some serious disasters. Therefore, countries execute policies to solve the problems of the demand of fossil fuels and the climate change. Because renewable energy comes from natural resources and will never deplete, governments put the emphasis on the development of renewable energy. Taiwan government also follows the trend of carbon reduction and passes the Statute for Renewable Energy Development. The statute guarantees that the government will buy back the electricity generated from renewable energy with a fixed price in twenty years. Therefore, if the price is set too low, it is unattractive to public; on the contrary, if the price is set too high, it is a huge financial burden for the government. The price is calculated yearly based on the installation costs. Thus, by studying the installation costs can help understand the buy-back price. This research uses a learning curve model to predict the change of the installation costs in the future. However, studies usually study the costs by one factor learning curve model, which cannot thoroughly depict the relationship between the installation costs and the cumulative capacity. Therefore, the research proposes a hierarchical learning curve model and uses Taiwan’s wind power and photovoltaic as case study. The results show that the hierarchical model predicts the installation costs more accurately than the basic one factor learning model. The hierarchical model can also be applied to other kinds of renewable energy and even other research area.