The construction industry consumed 35% of worldwide energy, with domestic buildings accounting for 22%. Providing a healthy, positive environment in domestic buildings raised energy demand by around 80% in building operations, with thermal comfort accounting for about half of that increase. Furthermore, building energy consumption is 5 to 10 times greater than predictions given during the design phase. The discrepancy between the actual and intended design is called the performance gap. Although the term is widely used in the context of energy performance, it can also be found in indoor environmental parameters such as temperature, relative humidity, air quality, noise, and illumination. This thesis connects building performance simulation to building operational performance, focusing on real-time energy prediction for space heating in an indoor environment of domestic buildings.
The work presented in this research is a technical implementation framework for examining the energy consumption of indoor space heating in real-time, focusing on energy-related thermal comfort conditions at the zone level. Unlike building performance simulation tools, The developed framework can be used beyond the design phase to encompass operations and assist in diagnosing and detecting building underperformance or performance discrepancy over time. Focusing on zone level can offer a greater understanding of the thermal state and energy usage of specific individual spaces, which can also assist in identifying performance disparity.
Buildings with good indoor environmental quality are objectively assessed using simulation tools. However, the indoor environmental quality, especially thermal comfort, is experienced subjectively, making the building energy and thermal performance evaluation task challenging. The developed framework extends the use of the energy model to the operational stage by predicting thermal and energy performance based on indoor and outdoor environmental parameters. Moreover, using a parametric energy simulation and machine learning approach connected to an IoT sensor system enable users to identify thermal comfort conditions in the indoor environment and the amount of energy consumed for space heating. Finally, the research identified several lessons that can potentially inform and improve the existing domestic buildings, especially winter space heating.
Following the framework, an innovative device was developed and validated using an experimental approach that focuses on real-time energy prediction of space heating. In this process, the experimental case studies' thermal comfort conditions and energy consumption were monitored and analysed to identify thermal-energy performance-related issues, also used for validating the proposed real-time energy prediction module
