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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.

Individual metering of energy in flats is a real opportunity to save energy and money for the occupiers of collective-ownership buildings with centralised heating systems. The individual management of energy, through such devices, make users more responsible and also stimulates them to implement energy retrofit interventions in their flats. The use of individual metering systems in existing apartments, however, could have some critical aspects. The purpose of this paper is to analyse the application of individual energy systems in existing collective-ownership residential buildings, highlighting the critical aspects and providing practical solutions to overcome them. A study has been made of an individual metering application in an existing residential block of flats located in Milan (Italy) with poor thermal insulation between the various dwellings themselves and for the outside walls of the building. Although a net reduction of 50.4 % in the global costs for winter heating, obtained exclusively through plant-modification works, represents an optimum goal for energy savings, the study highlight some critical aspects. The savings for different apartments differ greatly and depend upon the criteria chosen for burden sharing. This study shows that the phenomenon of “heat theft” can prove to be important and provides the possibility of reducing to zero the quota of expenses from consumption whilst still benefiting from comfortable temperatures. Once the problems have been identified, the paper then proposes solutions to overcome them through a revision of the criteria for the allocation of energy costs.

Predicting short term electricity load accurately is critical to facilitate demand side management in the building sector. For buildings that have electricity sub-metering systems installed, it is possible to predict both the total electricity load and the loads of individual building service systems (air conditioning, lighting, power, and other equipment). In this paper, a method based on Support Vector Machine (SVM) is proposed to predict the loads at system level. For each type of system, 24 SVM models (one model per hour) were trained and deployed to predict the hourly electricity load. The inputs for the prediction method are simply weather predictions and hourly electricity loads in two previous days. A case study shows that the proposed method outperforms three other popular data mining methods (ARIMAX, Decision Tree, and Artificial Neural Network) in both CV_RMSE and N_MBE. Thus, SVM method is suggested for predicting system level electricity loads of public buildings.