Author:

Keywords:

Building retrofit, clustering, cost-benefit analysis, district heating, fuzzy logics, geoinformation system (GIS), thermal microgrids (TMGs), Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, RENEWABLE ENERGY-SYSTEMS, RESIDENTIAL SECTOR, FUZZY-LOGIC, CONSUMPTION, INFORMATION, MANAGEMENT, AREAS, 0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, 4009 Electronics, sensors and digital hardware

Abstract:

Retrofitting the existing building stock is among the most important objectives and imperative to meet societal goals to reduce primary energy demand and anthropogenic greenhouse gas emissions. District heating systems have proven to supply heat for buildings both energy- and cost-efficiently. Thermal microgrids can be understood as a subcategory of district heating systems: small scale, bi-directional, low temperature and potentially fed by different thermal sources. Given a suitable combination of loads, number of and distance between buildings they can offer economic and environmental advantages compared to the supply by individual heating systems per building. We present a novel method using data analysis techniques on geo-referenced building stock data to identify suitable configurations of buildings that yield a cost-efficient thermal microgrid. For the identification both semantic and spatial data from a database are combined using fuzzy logics and cost-benefit analysis. We apply the method using a case study featuring a database of 306 buildings potentially to be retrofitted. As a result, we can identify 9 groups of in total 25 buildings that would form a microgrid featuring up to 17.4% cost benefits compared to an individual heat supply. This would save approximately 30% of the building induced CO2 emission of the community.