Abstract
High spatial resolution is critical for a building stock energy model to identify spatial hotspots and provide targeted recommendations for reducing regional energy consumption. However, input uncertainties due to lacking high-resolution spatial data (e.g. building information and occupant behavior) can cause great discrepancies between modeled and actual energy consumption. We present a modeling framework that can act as a blueprint model for most European countries based on geo- referenced data, building archetypes, and public algorithms. Further sophistication is added in a step-wise approach, including the shift from average to hourly weather data, refurbishment, and occupants’ heating schedules. The model is demonstrated for the city of Leiden, the Netherlands, and the simulated results are spatially validated against the measured natural gas consumption reported at postcode level. Results show that when these factors are considered, the model can provide a good estimate of the energy consumption at the city scale (overestimated by 6%). At postcode level, nearly 83% of the absolute differences between modeled and measured natural gas consumption are within one standard deviation (±25 kWh/m2a, about 30% of the mean measured natural gas consumption). Further research and data would be required to provide reliable results at the level of individual buildings, e.g. information on refurbishment and occupant behavior. The model is well suited to identify spatial hotspots of residential energy consumption and could thus provide a practical basis (e.g. maps) for targeted measures to mitigate climate change.
Highlights
The building sector is important for climate change mitigation [1], as it is responsible for approximately 40% of final energy consumption and 36% of the greenhouse gas (GHG) emissions in the European Union (EU) [2]
Kavgic et al [8] add the hybrid models that estimate the energy consumption mainly influ enced by occupant behavior, such as domestic hot water (DHW), cooking, lighting and appliances with statistical methods while calculate the energy consumption for space heating and cooling with engineeringbased methods due to a lack of historic data and the application of new technologies
This study presents a Geographic Information System (GIS)-archetype based bottom-up building stock model for energy consumption for space heating
Summary
The building sector is important for climate change mitigation [1], as it is responsible for approximately 40% of final energy consumption and 36% of the greenhouse gas (GHG) emissions in the European Union (EU) [2]. Swan et al [5] further classify the bottom-up models into statistical and engineering-based methods ( known as physical models or white box models [6]). The former performs statistical analysis (mostly regression techniques) on historical data and establishes the relationships between end uses and energy consumption [10] while the latter considers the building elements and HVAC of sample buildings representative of the building stock and simulates the energy demand with the balance of heat transfer in accordance with thermodynamic principles [11]. Kavgic et al [8] add the hybrid models that estimate the energy consumption mainly influ enced by occupant behavior, such as domestic hot water (DHW), cooking, lighting and appliances with statistical methods while calculate the energy consumption for space heating and cooling with engineeringbased methods due to a lack of historic data and the application of new technologies
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