Defining data-driven analytical methods on improving energy-efficiency in apartment buildings

Heating of the properties generates 56 percent of the carbon emissions in the city of Helsinki. Privately owned and rental properties have seen very few improvements in energy efficiency in recent years and as part of their climate programs, the cities look for initiatives and incentives to tackle the issue. In some research (1), reasons for the lack of action in rental properties are also a range of market barriers and market failures including misinformation, split incentives and an uneven power dynamic between renters and landlords. Finland has quite a rare model on the way how the private buildings are formed as a single legal body that owns the apartments. Instead of owning an apartment as a property, the right to hold the apartment is given by owning shares in the limited liability housing company. This structure may sometimes affect decision making on not only investments but also all the spending, including consulting and analysis that might prove some actions to be reasonable. It is expected that standardized analytical methods on commonly available data such as room sensor temperature and humidity values could provide a way to identify the first steps on energy efficiency measures in ways that could be replicated to larger scales, even city-wide analysis. For the sensors, data quality definition was created by implementing the ISO 19157 requirements and the goal is to create self-explanatory datastreams that can be processed live with meaningful results being achieved focusing on the three analyses described in this study. The three methods together should result with 10-20% savings on primary energy consumption without additional investment on equipment.