Application Of Algorithm For Power Energy Analysisand Prediction Of Office Building

In recent years, with the rapid increase of office buildings and the requirements to improve the office environment, office building energy consumption has increased significantly. That has brought great pressure to the user, environment and city power supply and demand. The electric energy consumption of office building conservation is the most important way to reduce the energy consumption and simulation is a major method to understand about it. At present there are problems in simulating power consumption with software,which is difficult to use and the results are highly specialized and cannot give assist and support to decision makers or managers directly.To solve this problem a holistic statistical distribution instead of complex thermodynamic iterative computation model, and the analysis method was presented. Using the mode "facing to the whole" for analysis of power energy consumption of office buildings. Using bivariate correlation analysis and theoretical discussion presented the factors of consumption individually. And in accordance with the principle of selecting the regression variables get these factors classified and quantified. Using multiple linear regression, get the significance of regression first. It proves that the existence of these factors between similar linear relationship between the variables and energy consumption. Using the model to predict the actual energy consumption, the error is very small. Such models offer different models designed for different buildings. Avoid the inevitable systematic errors generated when analyzed using the unified model for the different buildings.In this study, statistical theory is used to analysis energy consumption data, five important correlational external factors with consumption are screened. Not only proved the existence of a linear relationship between the power consumption and external environment variables, and it is feasible to predict. But also establish a complete office building electricity consumption forecast model based on these relationships. Final software engineering techniques is used to design and implement the theoretical model. This model can easily and quickly provide assistance and support for decision makers in the energy sector without sophisticated expertise.