After the thermal renovation of a dwelling, there exists a gap between the actual and predicted e... more After the thermal renovation of a dwelling, there exists a gap between the actual and predicted energy performance. One of the reasons contributing to this gap is the poor assumptions of building thermal characteristics during the prediction stage. Nowadays, smart meters for gas and electricity, and home automation systems are becoming increasingly prominent in dwellings. Hence, there is potential to use the on-board monitored data from these sources to estimate the thermal characteristics of the actual dwellings. If it was possible to measure everything in a dwelling, then the estimation of these characteristics would become easy. However, the amount of data from the dwellings is limited. Hence with the available data, assumptions have to be made to estimate characteristics reflective of the actual dwelling. Therefore, this study investigates the impact these assumptions have on the estimated characteristics. First, a simple equation requiring minimum data is formulated to represent the heat dynamics in a building. Then, the characteristics are determined for one Dutch dwelling for the following conditions: 1. Different measurement periods, 2. Different time granularities, 3. With total (space heating + domestic hot water) and decomposed (only space heating) gas consumption data, 4. With different representations of indoor air temperature, and 5. Using electricity data to account for internal heat gains. In general, the estimated characteristics deviated for all the conditions. And thus, this study establishes the importance of well-chosen on-board monitored data.
In Europe the Energy Performance of Building Directive (EPBD) prescribes a compulsory energy labe... more In Europe the Energy Performance of Building Directive (EPBD) prescribes a compulsory energy labelling of existing dwellings. In the Netherlands a national labelling scheme is applied since 2008. The label is based on a theoretical calculation of the gas and electricity consumptions accounting for the physical characteristics of the dwelling, its heating, ventilation and cooling systems and standard use characteristics. In addition to the label, occupants are provided since 2010 with the theoretical gas and electricity consumptions. This paper reports on a large scale study comparing labels and theoretical energy usage with data on actual energy usage. A database of about 200.000 labels was coupled with data from Statistics Netherlands on actual gas and electricity consumptions provided by energy companies. Significant discrepancies between the actual and theoretical energy usage were found and analysed. The study showed that the less efficient energy labels consume much less energy...
Following the sensitivity analysis on heating energy consumption in Chapter 3, Chapter 4 is an an... more Following the sensitivity analysis on heating energy consumption in Chapter 3, Chapter 4 is an analysis on the determinants of electricity consumptions in Dutch dwellings. The OTB sample was used for analysis, and it was validated with analysis of the WoON sample. The work was published as: This Chapter deals with the Research Question II of this thesis: (Chapter 1, Section 3, pg. 16-17) “II. What is the influence of lighting and appliance use on the total electricity consumption in dwellings?" The sub-questions are: 1. What are the main direct and indirect determinants of electricity consumption? (Direct determinant: such as number of appliances and duration of appliance use … Indirect determinant: such as household size, dwelling size, dwelling type …) 2. How much of the variance in electricity consumption in dwellings can be explained by direct and indirect determinants?” The research reported in this Chapter was conducted by Bedir. The data was collected by a questionnaire ...
Abstract Current symptom detection methods for energy diagnosis in heating, ventilation and air c... more Abstract Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared.
After the thermal renovation of a dwelling, there exists a gap between the actual and predicted e... more After the thermal renovation of a dwelling, there exists a gap between the actual and predicted energy performance. One of the reasons contributing to this gap is the poor assumptions of building thermal characteristics during the prediction stage. Nowadays, smart meters for gas and electricity, and home automation systems are becoming increasingly prominent in dwellings. Hence, there is potential to use the on-board monitored data from these sources to estimate the thermal characteristics of the actual dwellings. If it was possible to measure everything in a dwelling, then the estimation of these characteristics would become easy. However, the amount of data from the dwellings is limited. Hence with the available data, assumptions have to be made to estimate characteristics reflective of the actual dwelling. Therefore, this study investigates the impact these assumptions have on the estimated characteristics. First, a simple equation requiring minimum data is formulated to represent the heat dynamics in a building. Then, the characteristics are determined for one Dutch dwelling for the following conditions: 1. Different measurement periods, 2. Different time granularities, 3. With total (space heating + domestic hot water) and decomposed (only space heating) gas consumption data, 4. With different representations of indoor air temperature, and 5. Using electricity data to account for internal heat gains. In general, the estimated characteristics deviated for all the conditions. And thus, this study establishes the importance of well-chosen on-board monitored data.
In Europe the Energy Performance of Building Directive (EPBD) prescribes a compulsory energy labe... more In Europe the Energy Performance of Building Directive (EPBD) prescribes a compulsory energy labelling of existing dwellings. In the Netherlands a national labelling scheme is applied since 2008. The label is based on a theoretical calculation of the gas and electricity consumptions accounting for the physical characteristics of the dwelling, its heating, ventilation and cooling systems and standard use characteristics. In addition to the label, occupants are provided since 2010 with the theoretical gas and electricity consumptions. This paper reports on a large scale study comparing labels and theoretical energy usage with data on actual energy usage. A database of about 200.000 labels was coupled with data from Statistics Netherlands on actual gas and electricity consumptions provided by energy companies. Significant discrepancies between the actual and theoretical energy usage were found and analysed. The study showed that the less efficient energy labels consume much less energy...
Following the sensitivity analysis on heating energy consumption in Chapter 3, Chapter 4 is an an... more Following the sensitivity analysis on heating energy consumption in Chapter 3, Chapter 4 is an analysis on the determinants of electricity consumptions in Dutch dwellings. The OTB sample was used for analysis, and it was validated with analysis of the WoON sample. The work was published as: This Chapter deals with the Research Question II of this thesis: (Chapter 1, Section 3, pg. 16-17) “II. What is the influence of lighting and appliance use on the total electricity consumption in dwellings?" The sub-questions are: 1. What are the main direct and indirect determinants of electricity consumption? (Direct determinant: such as number of appliances and duration of appliance use … Indirect determinant: such as household size, dwelling size, dwelling type …) 2. How much of the variance in electricity consumption in dwellings can be explained by direct and indirect determinants?” The research reported in this Chapter was conducted by Bedir. The data was collected by a questionnaire ...
Abstract Current symptom detection methods for energy diagnosis in heating, ventilation and air c... more Abstract Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared.
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