A Bottom-Up Building Stock Model for Tracking Regional Energy Targets—A Case Study of Kočevje

Abstract

Abstract:The paper addresses the development of a bottom-up building stock energy model (BuilS) for identification of the building stock renovation potential by considering energy performance of individual buildings through cross-linked data from various public available databases. The model enables integration of various EE and RES measures on the building stock to demonstrate long-term economic and environmental effects of different building stock refurbishment strategies. In the presented case study, the BuilS model was applied in the Kočevje city area and validated using the measured energy consumption of the buildings connected to the city district heating system. Three strategies for improving the building stock in Kočevje towards a more sustainable one are presented with their impact on energy use and CO 2 emission projections up to 2030. It is demonstrated that the BuilS bottom-up model enables the setting of a correct baseline regarding energy use of the existing building stock and that such a model is a powerful tool for design and validation of the building stock renovation strategies. It is also shown that the accuracy of the model depends on available information on local resources and local needs, therefore acceleration of the building stock monitoring on the level of each building and continually upgrading of databases with building renovation information is of the utmost importance.

Energy Performance Simulation for a Residential Building

“…Dynamic simulation is used in determining construction properties, occupant comfort, HVAC system energy demands, energy conservation techniques etc. [2][3][4][5][6]. In one of our previous researches we used multi-criteria optimization methodology to determine an optimal energy retrofit solution in case of adequate envelope glazing selection [7].…”

Evaluation of a Regional Retrofit Programme to Upgrade Existing Housing Stock to Reduce Carbon Emissions, Fuel Poverty and Support the Local Supply Chain

“…However, identifying the most appropriate combination of retrofit solutions required to meet targets can be extremely complex [8]. Many studies have been undertaken to provide methodologies to support the selection of appropriate retrofit solutions for domestic properties [9][10][11][12][13] but in practice it is proving difficult to implement these on a scale that will achieve the targets set due to the large number of barriers. Properties constructed during different time periods pose different retrofit challenges due to the materials available, construction methods and needs of the population at the time of construction and this is also true for dwelling in different locations.…”

Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach

“…The building sector is a major contributor to energy consumption, accounting for around 20% of total energy consumption in China [1] and 40% in the U.S. and the U.K [2,3]. With the trend of facilitating sustainable development globally, building energy efficiency has been recognized as a critical issue in many countries and regions [4][5][6]. In China, it is reported that buildings are of great potential to contribute up to 50% energy saving [7], and more and more building energy efficiency policies have been and will be implemented to promote a sustainable environment [8].…”

“…Little attention has been paid to geospatial patterns of building energy consumption from a regional perspective, e.g., investigation of the factors impacting regional building energy consumption. As a result, the revealed relationships between influential features and energy consumption or suggestions to improve energy efficiency are more applicable to the building-level intervention but less to the regional level, which is essential to optimal policymaking and better city planning [6,16,19]. Regional building energy models help understand the relationships between potential influential features and regional or city level energy consumption [20], resulting in generating a more holistic view to inform policy formulation.…”