One of the most significant barriers to achieving deep building energy efficiency is a lack of knowledge about the factors determining energy use. In fact, there is often a significant discrepancy between designed and real energy use in buildings, which is poorly understood but are believed to have more to do with the role of human behavior than building design. Building energy use is mainly influenced by six factors: climate, building envelope, building services and energy systems, building operation and maintenance, occupants' activities and behavior, and indoor environmental quality. In the past, much research focused on the first three factors. However, the next three human-related factors can have an influence as significant as the first three. Annex 53 employed an interdisciplinary approach, integrating building science, architectural engineering, computer modeling and simulation, and social and behavioral science to develop and apply methods to analyze and evaluate the real energy use in buildings considering the six influencing factors. Outcomes from Annex 53 improved understanding and strengthen knowledge regarding the robust prediction of total energy use in buildings, enabling reliable quantitative assessment of energy-savings measures, policies, and techniques.

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Current district heating (DH) systems with high temperatures are facing many challenges that may decrease its competitiveness. Some of the challenges are decreased heat demands due to energy efficient buildings and high return temperatures that decrease possibilities for utilization of renewable heat sources. Low temperature DH (LTDH) systems have opportunities for utilization of waste heat and renewables and lower distribution losses. Therefore, the aims of the study were to analyze the challenges in the transition to LTDH and to estimate the increased competitiveness in low heat density areas. Since the heating density is an important factor for the DH competitiveness, the high and the low heat density area were analyzed. A building area consisting of the passive house and low energy buildings in Trondheim, Norway, was analyzed.The hourly DH network model was developed included both thermal and pressure losses. The results showed that the heat loss could be reduced by lowering the supply temperature from 80°C to 55°C. Analysis of the return temperature showed that LTDH could provide a lower return temperature than the existing DH system, regardless of the faults. Competitiveness of LTDH might be decreased for the heat densities lower than 1 MWh/m.

Part of the Engineering Commons, and the Science and Technology Studies Commons Recommended Citation Recommended Citation Ma, Zhenjun; Yan, Rui; and Nord, Natasa, "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings" (2017). Faculty of Engineering and Information Sciences-Papers: Part B. 484.

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District heating (DH) will play an important role in the future fossil-free energy systems by enabling increased utilization of waste heat and renewable heat sources to cover buildings' heat demand. A prerequisite for this is a reduction in the distribution temperature and shift towards decentralized heat production. In this study, dynamic modeling has been applied to study the technical, energetic and environmental impacts of including prosumerscustomers who both consume and produce heat -in a local low-temperature DH grid. Four different scenarios were studied for a planned building area in Trondheim, Norway: high-and low-temperature scenarios with the entire heat demand being covered by a heat central, and two low-temperature scenarios including heat supply from prosumers. A data center and two food retail stores were considered as the prosumers, each with different location and individual characteristics for the heat supply, allowing to study their impact on the water flow in different parts of the grid. The results show that utilizing local surplus heat is a significant measure to reduce the heat demand and the environmental impact of the DH grid. Decentralized heat supply additionally contributes to reduced heat losses, due to overall lower distances to transport the heat.

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