## 2 Literature Review and Justification of Topic Selection  
In the literature can find several approaches related to the control of heating systems in buildings, which are connected to the municipal heating network. Detailed approaches can be divided into [14, 15]:  
* control of night-time temperature reduction in the heating system [9],
* control based on outside temperature [15],
* energy control with indoor temperature sensors [16, 17, 18],
* control based on weather forecasts [16, 19, 20, 21, 22, 23, 24, 25],
* forecast control by using a barometer [15, 16],
* energy limitation by installing an accumulator tank [26, 27],
* complex object control models- predictive models based on data analysis using methods such as artificial neural networks and others [28, 21, 29, 30, 31, 32]. Some of the above-mentioned ways of regulating the operation of the heating system have been commonly used in real residential buildings as commercial systems. These are among others: EnReduce, Egain Edge oraz Kabona (these systems are discussed in detail in the paper [16]). Therefore, in this research we want to focus on the evaluation of one of the presented methods, which has been implemented in buildings in Poland. Previous research carried out mainly in Sweden has shown that depending on the applied method of regulation, measurable savings in energy consumption can be achieved. The level of savings for selected regulation systems [16, 17, 20] is presented in Table 1.  
The presented research results indicate that the average energy savings regardless of the applied regulation system is about 13%, with the biggest differences in the achieved savings (0-26%) being observed in the case of the Egain Edge method [16]. In addition to temperature indicators (and others), one of the key values for energy storage in buildings is the time constant \(\tau\). The time constant describes how quickly a building will react to changes in the weather and additional heat. The constant differs between buildings, the heavier the structure, the greater the time constant (temperature inertia) of the building. It is estimated that a \"heavy\" type building has a time constant of about 160 h, an \"average\" building has about 66 h and a \"light\" building has about 25 h [33]. In the literature [16, 17] there is no precise information on what type of buildings were tested, looking at the range of energy savings (0 to 26%) in the case of the method based on the Egain Edge weather forecast, it can be assumed that these buildings may have differed from each other by the type of construction and therefore by the time constant. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Heating Control System in Objects**} \\ \hline
**Specification** & **Egain Edge** & **EnReduce** & **Kabona** \\ \hline number of buildings tested & 19; 2 & 7 & 4 \\ \hline range of energy savings achieved \% & 0–26; 13,7–14 & 7–20 & 9–16 \\ \hline average energy saving \% & 11; 13,85 & 13 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of energy saving levels for different control systems for central heating in residential buildings. In Poland, from the sixties to the eighties of the last century, the technology of large plate (prefabricated elements) dominated in the residential construction. The issue of energy efficiency of large panel buildings is discussed in many scientific works [34; 35]. None of the buildings erected at that time in the large panel technology, by assumption, is able to meet the thermal and energy requirements set today. The research carried out on the analyses of thermal insulation of external walls of buildings made in the large panel technology, confirmed the deterioration of their thermal insulation, mainly due to the use of concretes with increased density. The defects and errors resulting from the execution or damage of thermal insulation layers as well as the location of thermal bridges also had an impact.