Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it’s necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatically identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatically. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.
When planning a residential community, an important factor鈥搊ccupant behavior鈥搃s often omitted. Previous research suggested that the age of occupants may significantly affect the dwelling time and use of air conditioners, thus should be considered during low carbon oriented residential community planning. In this study, an energy related occupant behavior survey recently conducted in Qingdao city is presented. Through this survey, the thermal preference, dwelling time, and air conditioners usage behavior of three different family structures (young couple family, old couple family, and couple with parents family) are analyzed. These information, together with urban planning parameters (floor area ratio, building coverage ratio, aspect ratio, etc.) are then fed into energy simulation models, to investigate the role of occupant behavior in low carbon oriented residential community planning. The results show that the energy demand of old couple family is more affected by community planning. Aspect ratio is more important than height in terms of space cooling and heating demand. The optimal aspect ratio strongly depends on the type of occupants and HVAC system. In general, aged occupants need more heating energy, thus are better located in buildings with lower aspect ratio. Communities with district heating system and decentralized cooling system need lower aspect ratio than that with other types of HVAC systems. The results have important implications to low carbon oriented residential community planning.
Residential building sector is one of the major contributors to global electricity energy consumption. Current researches have demonstrated that the residential building energy consumption is determined by many factors, including climate conditions, household and building characteristics, and occupant behavior. However, the extent to which each factor contributes to the total energy consumption has remained unclear, especially in developing countries such as China. To partially answer this question, an empirical study was conducted in five residential real estates in Qingdao city. Questionnaires were distributed to around 500 families, whose electricity consumption from Feb to Aug, 2015 was then collected from local electricity bureaus. Based on the collected data, correlation analysis was performed to exploit the relative role of each factors. Results reveal that occupant behavior is the most important parameter on cooling energy use, compared with household characteristics and urban geometry.
Most modern buildings are equipped with building energy management and control systems. These systems can store tremendous amounts of data on buildings’ performance and energy usage. A signiﬁcant amount of data on buildings’ mechanical devices, particularly electricity-consumption data, is now available for analysis. However, the quality of the collected data is questionable. Some data are mislabeled, and others contain gaps and errors. In this article, a methodology based on a correlation coefﬁcient and a wavelet-based support vector machine predictor is proposed to detect and recover the proportional deviation data faults and faults caused by network communication. After testing this methodology with electricity data collected from a large commercial building, it is found that a high accuracy of faulty data alerts and automated data recovery can be achieved. Considering the wide use of building energy management and control system data for performance monitoring, fault detection and diagnostics, and demand responsive control, this method is useful and practical in many engineering situations.