Research On Computational Power Generation Forecasting Of Photovoltaic Building

Solar photoelectric architecture is the concept of producing energy by architecture,which is the main form in the adoption of distributed photovoltaic power generation andwhich drives the tendency and hotspot in the field of urban and rural construction accordingto reproducible energy technology. In the design phase of solar photoelectric architecture,power generation not only acts as the gist assessing the quality of design, but also plays a vitalrole in the estimation of index such as initial investment, earning cycle and total earnings.Therefore, forecasting power generation precisely is too significant to whole decision-makingin the design of photoelectric architecture project. In present, the means of power generationforecasting of solar photoelectric architecture follows forecasting method and software ofphotoelectric power station, which cannot integrate the influence factors of power generationby architecture into the procedure of forecasting, let alone aims at the special photoelectriccomponents of solar photoelectric architecture to forecasting the power generation exactly.Thus, the current power generation forecasting method of photoelectric architecture has pooradaptively and less accurate, which cannot provide the reliable basis for the judgment ofproject design.Essay that states the effects of photoelectric systematic power generation by architectureanalyses and concludes the factors that affect the power generation of photoelectricarchitecture. It introduces the method and flow how to build the photoelectric architecturemodel to forecast power generation according to the analysis of each factor effected powergeneration. Determine the concept of photoelectric architecture model, the components ofmodel and each section·s functions. Definite the established method of model, the storageformat of data and the way of use, which employs the photoelectric architecture model to readthe data information needed by each photoelectric component to forecasting power generation.Improve current computational method of solar radiant quantity. Combine the datainformation of photoelectric architecture model. Add the hourly shade square and dust fallquantity of photoelectric components and calculate the timely solar radiant quality ofphotoelectric component. Then, acquire the hourly computational power generation of eachphotoelectric component according to the photoelectric transition procedure. Use thecomputational power generation forecast platform that is specially developed by the essay tocomplete the procedure of read, storage and interaction aimed by photoelectric architecturemodel and to compute each component·s computational power generation. At last, obtain theeach month and total year s computational power generation of photoelectric architecture.Compare to the forecasting results and detective data, essay sets example of X solarphotoelectric architecture in the Baoding city, Herbei province. Adopt computational powergeneration forecasting method of solar photoelectric architecture putted forward by the essayand build the photoelectric architecture model, at same time, calculate the computationalpower generation in the forecasting platform of computational power generation. Compare todetective data of computational power generation by the photoelectric architecture project and forecast results by this method, the result reflect that forecast results adopted by the essay aremuch closer to detective data of the project. What is more, it has a higher predicted accuracyand reflects the fluctuation rule of computational power generation in the architecture.Essay introduces a method constructed a photoelectric architecture model to forecastingcomputational power generation, which can reflect sufficiently the great effect in thephotoelectric systematic computational power generation according to architecture.Meanwhile, it meets the demand of computational power generation forecasting ofphotoelectric architecture, which faces to photoelectric architecture, adapts the characteristicsof photoelectric architecture and forecasts the computational power generation ofphotoelectric architecture accurately as well.