**4. An Exemplary Application of the TEAC Software**

It is possible to analyze the actual residential neighborhood using the TEAC software. In this example, the part of the *Smulsko* neighborhood, located in Lodz (Poland), is examined. The neighborhood is analyzed as an Energy Cluster (EC), and due to the fact, that the analysis is focused on the buildings, the area might be considered as a BEC [21]. The area is defined based on the satellite image shown in Figure 6. That image was adjusted (rotated by 45 degrees counter-clockwise) to the predefined grid used by the TEAC software, where each cell represents a parcel for only one house. The built environment is defined by overlapping the grid with the satellite image; whenever a building image fits within a cell, thus the parcel was considered as occupied. The building placement is done following the statistical data (assuming a share of the Polish RSFH) [64] and rotating each house randomly. The schema of the examined BEC is shown in Figure 7; it is a square-based zone, 23 by 23 parcels, consisting of 202 houses (each color represents a different RSFH).

**Figure 6.** The satellite image of the *Smulsko* cluster (on **left**, source: [65]) and its adjustment schema (on **right**).

In this case, deep buildings thermal modernization is analyzed, concerning their full electrification. The refurbishment assumes building enclosures retrofitting to the actual energy-efficiency standards, following the Polish regulations. The modernization also includes heating system upgrades, from standard stoves to highly efficient heat pumps. Moreover, in all buildings, the lighting systems are modernized with LEDs. Also, Renewable Energy Sources (RES) are applied by means of photovoltaic (PV) systems mounted on the appropriate roof slopes, avoiding surfaces oriented North, North-East, and North-West. The Isometric Radiation Model (IRM) was used in order to calculate the solar outputs; it is comprehensively described in [66]. Finally, some smart-metering techniques are applied for the examined BEC. All the proposed modernizations follow the smart-city concept; thus, the examined neighborhood might be considered as the Energy Flexible Building Cluster (EFBC) [20]. The TEAC software is capable to analyze

a single-family house neighborhood as an EFBC, considering various energy-, economicand environmental-related issues; some of the available outputs are presented.

**Figure 7.** The final schema of the *Smulsko* cluster for the analysis in the TEAC software.

The default outputs using the TEAC software are maps, which might present various results, i.e., heating and electrical energy demands, greenhouse gasses (GHG) emission, RES potential or economic indexes (presenting the modernization or operation costs). It should be repeated, that all the results are obtained based on the predicted heating demand (out of the ANN usage), as well as the electricity consumption for the predefined scenarios. As an example, a comparison of CO2 emissions before and after modernization is shown in Figure 8. Maps allow us to validate the proposed modernizations for the whole area, as well as for some smaller parts of the neighborhood. On the other hand, maps can be also used to select the most appropriate region for modernization.

**Figure 8.** CO2 emission maps for the *Smulsko* cluster before (on **left**) and after (on **right**) modernization.

A short summary of the analysis results is shown in Table 2, where both examined variants, before (V0) and after (V1) deep retrofitting, are compared. All of the results are obtained out of the TEAC software. In the modernized scenario, only electricity is used, supported by renewables. The heating demand is significantly reduced (by approx. 95%), same as the electricity consumption (63%). The peak demands are also significantly lower, accordingly by 94.6% and 40.5%. Out of the obtained results ones for heating purposes are more important, due to the fact, that heating, in general, is a dominant demand for Polish

residential buildings (especially single-family houses). The total amount of 640.40 MWh electricity is produced out of 4443.45 m2 of PVs, allowing for energy independence (zeroth demand) for more than a quarter of the year (approx. 2450 h). The proposed modernization is also highly pro-ecological—the GHG emissions are significantly reduced (in the range of 82–92%) in the examined cluster. Finally, some economic aspects are presented. The building's deep retrofitting costs slightly over 15 M PLN, and those actions will be profitable after 14–18 years, depending on the calculation method, where the longest time is the most likely, calculated using the Life Cycle Cost (LCC) approach. The whole PV installation costs approx. 3.72 M PLN and due to electricity consumption savings, its payoffs after approx. 9.3 years. Furthermore, using the EC concept it is possible to recon the whole neighborhood as a unity, not as numerous singular buildings. That approach allows to generate some additional savings out of the cluster operating, without (or with minimal) initial costs. Those annual savings, for the modernized scenario, are as high as 62 k PLN, which is a 24% reduction. In Figure 9 a comparison of electricity Load Duration Curves (LDC) is presented. It is evident, that the proposed modernization improved the safety of the local grid: the electricity demand is more uniform, peak demands are lower, as well as some temporal energy-independency (marked as a green box) is observed. The analysis performed using the LDC is an extremely valuable approach in terms of verifying the modernizations validity.

**Table 2.** An energy-related summary for the *Smulsko* cluster.


Symbols used: V0—base scenario; V1—modernized scenario; HCA—annual heating demand; ECA—annual electricity demand.

**Figure 9.** Comparison of the electricity LDCs for the *Smulsko* cluster.

#### **5. Conclusions**

This paper presented an application of the ANN trained using the L-M method, for analyzing various issues referring to UBEM. The analysis was performed by means of the TEAC software—a home-developed research computational tool for UBEM of the Polish residential sector. The L-M method was used for the ANN training process. The purpose of the AI application was to define a network, capable to predict the heating demand of the neighborhood of a single-family house in Poland, with sufficiently high accuracy, compared with the *Energy Plus* software outputs. The L-M method was the most accurate (for the examined purpose) out of all examined ones, which was discussed in this paper. The high accuracy was obtained, not only for the total values, i.e., annual heating demand but also for much more detailed results, i.e., peak demands. Thus, the applied method

allows not only predicting the accurate values of heating demand (see Table 1), but also its time evolution and characteristics (see Figure 5). Moreover, due to the ANN application, the total computational time of the performed analysis was much shorter comparing with the traditional approaches. The computing time of annual energy demand for a single house is reduced out of approx. 20 s (using the *Energy Plus* software) to approx. 2 s only (by means of the TEAC software). Additionally, the required computational resources are also significantly lesser.

The TEAC software is a useful tool for UBEM of the Polish residential sector. It focuses on the single-family houses sector, due to its impact on the national energy profile. The Polish single-family houses sector is characterized by a huge potential for the overall improvement of energy efficiency, which might be validated using the TEAC software. The tool is using the EC concept, in order to maximize the energy-, economic- and environmental-related profits in the examined region. The TEAC software can be used for various analyses of actual residential areas, as shown in the example discussed in this paper. The developed tool might be used by the local authorities to perform urban-scale management, as well as academics for various UBEM analyses. Further development of the TEAC software is planned (e.g., the addition of GUI), in order to make the TEAC software available as a user-friendly web application.

**Author Contributions:** M.Z. and D.G. were involved in defining the aim of the paper. M.Z. developed the TEAC software. M.Z performed numerical analyses and prepared results. M.Z. and D.G. analyzed the results and performed writing and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received funding of the Faculty of Civil Engineering, Architecture and Environmental Engineering at the Lodz University of Technology—scholarship for young scientists in years 2019 and 2020.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

