**2. TEAC Software Concept**

A brief concept of the TEAC software is described in this section. The TEAC software is a tool for various UBEM analyses of Polish single-family stocks. It is based on a hybrid approach of UBEM: both top-down and bottom-up methods are included. As a basis for the TEAC software some economic and market-derived drivers were used (econometric model), the ANN was implemented for energy demand predictions (neural network model) as well as a data of representative single-family houses of Poland was used (archetype model). A sequence chart of the TEAC software development is shown in Figure 2. The whole process can be divided into two stages, where the first one includes the software development, while the second one responds to the application phase (marked with a blue outline).

**Figure 2.** The schematic concept of the TEAC software.

The procedure is developed for the Representative Single-Family Houses (RSFH) of Poland, and those seven buildings were defined by means of the *Design Builder* software [47]. The process was performed to obtain input files (IDF-format), which then are used for

parametric simulations performed by means of the *Energy Plus* software. The simulations were automatized by the code written in *Python* programming language and all their results were collected in the csv-files. The performed parametric simulations included the following variables: weather data (air temperature and total solar radiation), building geometry data (area, volume, windows area, U-values of building enclosure), ventilation and infiltration rates, heating system data (type and efficiency), as well as built environment information (building orientation and its' closest surrounding). In total, 358,400 parametric simulations were performed, and their gathered results (expressing the heating demand) were used in the ANN training process. The application of ANN was performed using the *MATLAB* software [48]; it is furtherly described in Section 3. The obtained code was rewritten in the *Phyton* language, allowing to predict the heating demand of an examined neighborhood. Also, some additional modules were defined using the *Python* language, allowing for various energy-, environmental-, and economic-related analyses of the built area. In general, the TEAC software consists of four main modules, while the 3rd module consists of seven submodules. The final analyses of the considered area are performed based on the predicted heating demands, built environment data, as well as some precalculated energy-related data. The results can be presented by means of Urban Energy Maps (UEM) [49], various types of graphs, e.g., Load Duration Curves (LDC), or tabular summaries. The developed software can be used by various authorities in order to improve the local energy efficiency. The comprehensive description of the TEAC software can be found in [43].
