Bringing an understanding of energy use and energy efficiency in buildings to the next level

This thesis will focus on bringing understanding of energy use and energy efficiency in buildings to the next level. Buildings represent a higher percentage of energy consumption compared to other economic sectors. Improved control of energy use can lead to a higher energy efficiency in non-residential buildings. Long term energy savings can be achieved by improving the building design as well as conserving energy during the operation phase. It is therefore beneficial to study how currently available big data about non-residential buildings can be used to give more knowledge and insight in the energy consumption of these buildings. This study will use the methodology of an exploratory case study with as a study design the framework of an energy analysis. The entity studied in this case is the library of the TU Delft, the BTUD. The energy analysis is a five-step framework and is used to investigate the energy flow inside the building. The big data streams which are gathered and research in this case are the smart meter data of electricity and heating energy use, the building management system data and the Wi-Fi data. With this data, estimations are done for the total energy use of the building. By using explanation building between each step a hypothesis is gradually formed until the final conclusion of the study is formed, which is; The estimations of the electricity use of the equipment of the HVAC system can largely explain the real electricity use of the HVAC system of the building. The lighting, plug load and elevator use can only be estimated based upon real electricity use. The energy use of the building can be shown in kWh/m and yearly data can be used as comparison between buildings and between different years. The patterns in occupancy of the building can be determined with Wi-Fi data. The electricity use of the lighting, plug load and elevator use during daytime slightly correlates with the occupancy. The heating energy correlates with the outside temperature. With the available data streams, the estimation of the HVAC system could be made. The central lighting, plug load and elevator use requires extra information for individual estimations, but the aggregated use can be determined by the smart meter data. This in total explains the total energy use of the building. The project will have results in itself, but the full data sets will be available to researchers and students, and hence form a solid basis for future results on energy efficiency in relation to the digitization of buildings. The estimations of the electricity use of the equipment of the HVAC system can largely explain the real electricity use of the HVAC system of the building. The lighting, plug load and elevator use can only be estimated based upon real electricity use. The energy use of the building can be shown in kWh/m and yearly data can be used as comparison between buildings and between different years. The patterns in occupancy of the building can be determined with Wi-Fi data. The electricity use of the lighting, plug load and elevator use during daytime slightly correlates with the occupancy. The heating energy correlates with the outside temperature. With the available data streams, the estimation of the HVAC system could be made. The central lighting, plug load and elevator use requires extra information for individual estimations, but the aggregated use can be determined by the smart meter data. This in total explains the total energy use of the building. This study achieves its goal of developing a framework and giving advice on how to implement this framework. The other goal was to create awareness. This will motivate researchers to further research this interesting topic. Since this is an exploratory case on which not much information is yet available, a lot of progress can be made in this topic. The first logical next step will be to expand the current research by involving multiple buildings and by extending the time frame. This in combination with more data streams, such as computer data, a deeper investigation of the available data, such as inflow temperatures, and a platform which can support all the big data shows that our knowledge and insights of energy flow in non-residential buildings can be further developed.