Energy Efficiency Research On Electrical Systems In Villas

In recent years,With the increase of the people's quality of life,Numbers of people request more for their living quality.Meanwhile,With the increase of demanding quality and numbers of villa products,The effective research of electrical system of which draws more attention.Nowadays,There are no uniform electrical indicators about villas and flat villas,Major area of residence.In cunent standard,which cause the irrationality of distribution system and transformer capacity during the electric design stage of villas,Increasing the burden of power grid and the loss of energy efficiency.Thus,It is most important to predict the burden of villas and transformer capacity.In this thesis,According to the data of power distribution room about villas,modeling and simulation is made to take advantage of BP nerve network and Support Vector Machine method,modeling and predicting of transformer capacity is made during the design stage and actual operation.Meanwhlie,modeling and predicting of villas transformer capacity,It lays a theoretical foundation which improving electrical system energy efficiency of villa buildings.Also,the transformer capacity is predicted during the design stage and actual operation in this paper,Configuration is rational optimized which improve the electrical energy efficiency,design standards of villa budings is supported by predicting basis of theoretical and practical.It provides theoretical and practical basis for the power department to approve the transformer capacity,electrical design specifications of villa building electrical is supported by predicting transformer capacity.The theory of BP nerve network and Support Vector Machine method is introduced in detail,The models and processes established by the modeling and predicting the building transformer capacity.The prediction accuracy of the two control algorithms is optimized with BP neural network and support vector machine.The quantitative analysis of their stability is carried out,and the optimal prediction model is determined for the energy efficiency analysis of villa residence electrical system.