Abstract
This article presents a bottom-up approach for calculation of the useful heat demand for space heating and hot water preparation using geo-referenced datasets for buildings at the city level. This geographic information system (GIS) based approach was applied in the case study for the city of Krakow, where on the one hand the district heat network is well developed, while on the other hand there are still substantial number of buildings burning solid fuels in individual boilers and stoves, causing air pollution. The calculated heat demand was aggregated in the grid with 100 m × 100 m spatial resolution to deliver the heat map depicting the current situation for 21 buildings types. The results show that the residential buildings, in particular one- and multi-family buildings, have the highest share in overall demand for heat. By combining the results with location of the district heat (DH) network, the potential areas in its close vicinity that have sufficient heat demand density for developing the net were pointed out. Future evolution in heat demand for space heating in one-family houses was evaluated with the use of deterministic method employing building stock model. The study lays a foundation for planning the development of the heating system at the city level.
Highlights
Energy system planning plays a key role in urban development to ensure a reliable supply of clean energy
M spatial resolution and each cell contains the information which viewed by building type, type of heat demand in GWh per can be viewed by building type, type of heat demand in GWh per year and build up area in m2
This study demonstrated the use of geospatial building data to map heat demand in a city
Summary
Energy system planning plays a key role in urban development to ensure a reliable supply of clean energy. Broad use of coal and wood as fuel in low efficiency and obsolete boilers results in pollutant emissions from low stacks and very high air pollution [1]. Like fuel and/or technologies switches with thermo-modernization of buildings, contributes to emission reduction of both air pollutants and greenhouse gases (GHG). It is important to propose efficient tools for assisting heat system planning in urban areas. [2] used MATLAB and Modelica models to assist building energy management systems to enhance efficiency in heating [3,4,5] recreated specific buildings in TRNSYS energy simulation model to evaluate the potential of retrofit strategies
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