| dc.description.abstract | In recent years, Environmental Protection turns to a hot-issue from a concept the world are actively developing new and clean renewable energy, to reduce the pollutions arising in traditional power generation.Therefore, it, Solar energy industry been taken seriously, moreover, it becomes a trend of the future energy. Also because of this new trend, many manufacturers fancy piece of the market and have a large number of inputs to the production of solar products, and also, governments have cut the subsidy case in recent years,
therefore, lots of firms lowering their prices in order to digest inventory, then turns the market prices fall all the way down, many manufacturers also go down the tubes. Nowadays, when the standard solar module technology is getting mature, there is another solar module market is rising, it is ＂ Building Integrated Photovoltaics Module＂, it can be used as combination on building, and also benefit to the aesthetic and environmental protection. Therefore, this study will focus on the process of building integrated solar module parameters,applying the data mining technology of neural networks, linear regression, generalized linear models, classification and regression tree model to find the yield-limiting factor,and thus improve the yield, reduce their costs, and produce quality products, to enhance the ability to mature technology in this competitive environment, which is big difference from the traditional solar module. | en_US |