| Summary: | With the development of computer technology and Internet technology, more and more energy saving monitoring and management platform systems have been established. The energy saving monitoring and management platform has incomparable advantages in automation and real-time performance compared with traditional manual management. After a long time of operation, the energy saving monitoring and management platform has accumulated a lot of data. Due to various reasons, there is a lack of data in the process of collecting energy consumption, which affects the overall operation effect of the system. Based on the operation of an energy saving monitoring and management platform in a university in north China, this paper analyzes the data of building power consumption accumulated in recent years. This paper selects the typical metering branch data, establishes the exponential smoothing model, predicts the daily power consumption and analyzes the prediction results compared with the actual value to verify the effect of the prediction model. At the same time, it also provides a reference for the data prediction of energy conservation supervision platform of other universities.
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