Abstract
The development of energy-efficient buildings and sustainable energy supply systems is an obligatory undertaking towards a more sustainable future. To protect the natural environment, the modernization of urban infrastructure is indisputably important, possible to achieve considering numerous buildings as a group, i.e., Building Energy Cluster (BEC). The urban planning process evaluates multiple complex criteria to select the most profitable scenario in terms of energy consumption, environmental protection, or financial profitability. Thus, Urban Building Energy Modelling (UBEM) is presently a popular approach applied for studies towards the development of sustainable cities. Today’s UBEM tools use various calculation methods and approaches, as well as include different assumptions and limitations. While there are several popular and valuable software for UBEM, there is still no such tool for analyses of the Polish residential stock. In this work an overview on the home-developed tool called TEAC, focusing on its’ mathematical model and use of Artificial Neural Networks (ANN). An exemplary application of the TEAC software is also presented.
Highlights
Jaroslaw Krzywanski, Cities around the globe are growing rapidly, following the rising population
This paper presented an application of the Artificial Neural Network (ANN) trained using the L-M method, for
This paper presented an application of the trained the L-M
Summary
Jaroslaw Krzywanski, Cities around the globe are growing rapidly, following the rising population. A study focused on the energy behavior of a single building is called Building Energy Modelling (BEM) It is a well-known issue, already performed by academics all over the world; the overview of some popular BEM codes can be found in [8]. The UBEM allows aggregating the energyrelated results of singular buildings to the urban scale, including some complex phenomena. The UBEM allows aggregating the energy-related results of singular buildings to the urban scale, including some complex phenomena occurring in urban environments. Energy-related of city districts should [9], those tools are the most appropriate approach for analyzing building stocks at a large be performed using specialized UBEM software.
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