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The proposed energy performance diagnosis is intended to identify poor energy performance in a building and pinpoint the causes to provide suggestions for building operators to implement timely repair and maintenance. Many previous studies have probed the complicated problem of building energy performance diagnosis to achieve energy conservation and better performance. However, few of them have been successful because most of these methods rely on a large amount of data from an Energy Management and Control System (EMCS), and these data are unreliable. A detailed description of the methodology based on energy consumption data is presented in this paper along with the development of a prototype integrated toolkit. Weekly, daily and hourly diagnoses are developed at the whole building level, system level and component level, respectively. To validate the feasibility and applicability of the method, a case study on an office building demonstrating the proposed method was completed and was able to detect underperformance operation and energy waste.

Energy consumption monitoring and regulation on large-scale public buildings are always an indispensable part in building energy conservation. With the mounting establishment of the building sub-metering platform, massive amounts of historical power consumption data are provided. In this paper, different building types of six large-scale public buildings in Shanghai are selected, with their sub-metering data deeply analyzed. The concept of CDHs/HDHs (cooling/heating degree hours) is introduced and weekly prediction models of total building power consumption are proposed by the way of multiple linear regression algorithm which is relatively simple and easy to understand. The prediction models are validated to have great accuracy and general applicability in the paper, offering reliable instructions to the building facility manager and relevant competent authorities in terms of decision making and policy implementation.