Improving building heating efficiency using machine learning : An experimental study

Abstract: While global efforts are made to reduce the emission of greenhouse gases and move towards a more sustainable society, the global energy demand is continuing to increase. Building energy consumption represents 20-40% of the world's total energy use, and Heating, Ventilation, and Air Conditioning (HVAC) answer for around 50% of this amount. Only a small share of the European Union's building stock is considered to be energy efficient, and many of these buildings will continue to operate until the year 2050 and on-wards. The main objective of this thesis was to benchmark the economic and environmental implications of increasing building heating efficiency. To answer the framed research questions, an experimental study was carried out. In the study, a machine learning based solution was constructed and then implemented in a multi-tenant building for 24 days. Using an Artificial Neural Network a new heating curve was predicted, based on historical data from the building. The post-experimental data was then analyzed using STATA as statistical software tool. The results show that the new heating curve was able to reduce the heating system supply temperature by 1.9°C, with a decrease in average indoor temperature of 0.097°C. The decrease in supply temperature resulted in a reduction of energy expenditure by approximately 10%. Using the new building specific heating curve, yearly cost reductions of almost 11,700SEK could be achieved. Furthermore, the increased efficiency was able to reduce CO2 emissions by 127,5kg yearly. This results helps shed light on the general weaknesses in building heating systems out there today, and shows that there is great potential of reducing building energy consumption in cost effective ways. Although the implemented solution might not be generally applicable for all building owners out there, it should act as an eye opener for building owners and help motivate them into assessing their building operation and start looking into new technologies. Moreover, the study provides legible incentives for both building owners and the society to further work together towards a more efficient and sustainable society.