Abstract
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.
Highlights
General measuring instruments are used in the traditional energy management mode
In order to meet the needs of modern energy management, energy saving monitoring and management platform systems of different scales came into being
As a brand-new campus energy management, energy monitoring platform will effectively improve the level of the energy management work so as to get recognition from the administrators in the universities [2]
Summary
All kinds of energy consumption data adopt the traditional management mode of combination of manual meter reading and manual statistical report statistics. Power consumption of the university in 2019 is about 14 million kWh. The energy saving monitoring and management platform of Shandong University, Weihai was constructed in 2015. More than 1200 terminals are laid on the platform, collecting and transmitting real-time data of energy consumption in the whole campus per hour (the highest frequency is four times per hour) [5]. It can monitor buildings Lighting, power, special services and air conditioning electricity consumption. After running for four and a half years, the system has collecting more than 180GB of data
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