Photovoltaic Power Generation Pricing Model Design Verification And Prediction Based On Business Intelligence

The mature of renewable energy will be the trend of energy development infuture due to the increasing intensive conflict between human being and the resourceswith the continuous development of productivity of Human being and the less and lessresources.Photovoltaic power generation is a huge step for the renewable energydevelopment. While as the PV industry in China is still in early stage, and due to itsparticularity, how to price it is a problem remain to resolve as the tradition pricingmodel for power generation such as "Peak-valley tariff setting model" or "Two-TieredOn-grid Electricity Rating Pricing Model" is not suitable for it. The existing approachof pricing is depend on the market inquiry or just "one-size-fit-all" model. However,as China is immense and the natural condition is very difference among areas, so the"one-size-fit-all" is not suitable for the development of PV power station. While inforeign country LCOE model becomes a hot topic. It has its special advantage forpricing-through dynamic analyses of multi-factor to check the actual cost of PVpower generation. However, as LCOE model is created by foreign specialists, it is notentirely adapted to the real situation in China. So this article is to make someimprovement by introducing the CDM factor.The article took the approach of business intelligence, based on the data groupconstructed by the improved LCOE model, putting data of various factors which hasimpacts on the cost of PV power generation into Multidimensional data group by ETLtechnology in business intelligence. Then it designed the mining structure of eachfactor, and finally put the collected multidimensional data into file to do data miningthrough the mining structure.The article first incited the historic data of Germany PV power generation. Andthen it verified the reliability of LCOE model by comparing the real price and thetheoretic price calculated by LCOE model. Then it introduced the price of thecomposition and system of PV power generation and calculated the theoretic price ofin different areas considering the length of sunshine in difference area, combining itwith the revenue data produced by CDM system. It compared the theoretic price toactual price. On one hand it is to justify the improved LCOE model, on another handto demonstrate the constraint of "one-size-fit-all pricing model. Finally, it made an estimation of the price in future by adopting the data dig technology of businessintelligence to show the level of accurate and advance of the model brought by thisresearch.