Assessment of the energy performance of buildings

The problems of the heat engineering condition of buildings during operation diagnos are considered. A significant problem is the difficulty of obtaining measurement and other information for complete decision making. As a result of this, it is often necessary to make technical decisions regarding the long-term operation of facilities in conditions of incompleteness and fuzzy information about their functioning and their condition The use the methods of the theory of fuzzy sets. is proposed when making technical decisions to increase the energy efficiency of objects.

Currently, the energy efficiency survey of a large number of buildings and structures is being carried out as part of the energy audit of organizations in accordance with Federal Law No. 261 "On Energy Saving and Energy Efficiency Improvement and Amending Certain Legislative Acts of the Russian Federation", as well as Programs to promote housing and communal services reform after major repairs or reconstruction of residential buildings.
An important component of the energy inspection of buildings is the diagnosis of their energy performance, which consists of the condition of the building envelope, the condition of the internal heating pipes, ventilation and hot water supply, the availability and quality of work of regulation systems [1,2]. Operational diagnostics of the energy performance of buildings and structures is also necessary for the organization of their high-quality operation, which is especially important for areas with a long heating period and high tariffs for electricity and thermal energy [3][4][5][6][7][8].
In the ideal case, for a full-fledged diagnosis, it is necessary to have, along with technical documentation, the ability to take air temperature measurements in all rooms of the building while simultaneously recording the outside temperature; thermograms of all internal and external surfaces obtained simultaneously; heat carrier temperatures in all sections of the internal heating pipes. It is important to obtain the results of such measurements at different outdoor temperatures. Obviously, taking measurements in such a volume requires significant costs and is advisable in exceptional cases [8][9][10][11]. In most cases, when examining buildings and structures, a very limited scope of measurements is available. At the same time, it is important to maximize the use of additional information obtained from surveys of operating personnel and users of the premises. Information of this kind usually falls into the category of fuzzy information and is presented in the form of intuitive or expert judgments. For example, "hot", "warm", "cool", "cold" -if this relates to indoor air temperature. Regarding the quality of regulation of indoor microclimate parameters, one can obtain estimates of this kind: "good", "normal", "satisfactory", "unsatisfactory". In terms of assessing the condition of building envelopes, one can also obtain judgments of the type: "normal", "satisfactory", "unsatisfactory". Some of the information is fuzzy in nature. For example, heat carrier flow in open heat supply networks depends on many factors, while constantly changing over time. The imbalance of heating networks often leads to the fact that in peripheral objects the available pressure drop between the supply and return pipelines is insufficient to ensure normal circulation of the heat carrier. In such cases, users often resort to draining the coolant into the sewer [4,6]. This, in turn, affects the distribution of flows in the heating networks and the hydraulic modes of their operation. Obviously, user behavior can only be described on the basis of fuzzy set theory approaches. Usually, qualitative indicators of the thermal conductivity characteristics of building envelopes obtained as a result of surveys are taken into account on an intuitive level, or based on expert judgment [1][2][3]. At the same time, modern methods of the theory of fuzzy sets are widely and effectively used for decision-making in a wide variety of fields. [2][3][4][5].
Given the above factors, it is advisable to use an integrated approach based on the combined use of measurement results, analysis of balance relations of objects of different hierarchical levels and methods of the theory of fuzzy sets [1,6] to diagnose the state of engineering systems of buildings. At the same time, it seems important to develop a formalized decision-making algorithm taking into account fuzzy information. The aim of the present work is to develop an algorithm for diagnosing the heat engineering state of a residential building based on the approaches of the theory of fuzzy sets [12][13][14][15].
Usually, when examining heat consumption objects, their design characteristics and contractual loads are known, the results of flow measurements and the parameters of the heat carrier at the entrance to the building are available, the results of measurements of the temperature of the heat carrier along the risers are known. However, the available information is insufficient to determine the actual thermal and hydraulic modes of the heating system, to assess the energy performance of the building envelope and the actual loss of thermal energy. Additional sources of information may be the results of thermography of the building, as well as conclusions obtained on the basis of a rule base based on fuzzy information [12,13].
Based on the results of a survey of experts, qualitative scales are formed for three characteristics: the indoor air temperature, the deviation of the indoor climate parameters from the normative and the deviation of the state of the building envelope from the design. The indoor air temperature is evaluated according to the following quality scale: "very cold", "cold", "cool", "normal", "warm", "hot". Membership functions are formed for each indicator according to the experts judgments. The membership functions are linear for the extreme indicators "very cold" and "hot":  "very cold" For intermediate indicators "cold", "cool", "normal", "warm" the membership functions are nonlinear and are determined by the following relation:  The control of the indoor climate parameters according to the results of a survey of residents is evaluated on a scale of "good", "normal", "satisfactory", "unsatisfactory". When forming membership functions for the indicators "good" and "unsatisfactory", formulas (1) and (2) are used, respectively, and for the indicators "normal" and "satisfactory", the formula (3)   To diagnose the energy performance of buildings, we define the following rule base: 1. Poor quality of service by the operating organization if  the indoor temperature is rated as very cold, cold or hot;  regulation of the water temperature in the heat network is rated as very good or normal;  building heat transfer coefficient is rated as satisfactory or unsatisfactory.

Unsatisfactory quality of heat carrier supply by heating network company if^
 the indoor temperature is rated as very cold, cold or hot;  regulation of the water temperature in the heat network is rated as satisfactory or unsatisfactory;  building heat transfer coefficient is rated as good, normal or satisfactory.
3. Good quality of service by the operating organization if  the indoor temperature is rated as normal or warm;  regulation of the water temperature in the heat network is rated as satisfactory or unsatisfactory;  building heat transfer coefficient is rated as normal or satisfactory. 4. All services work fine  the indoor temperature is rated as normal or warm;  regulation of the water temperature in the heat network is rated as very good or normal;  building heat transfer coefficient is rated as normal.
The algorithm for obtaining a conclusion about the energy performance of buildings and the quality of service, based on the results of the observation and the rule base, is as follows.
At the current time, the indicators of the indoor temperature , the characteristics of regulating the temperature of the network water and the actual heat transfer coefficient are estimated. We call such a set of parameters "a situation". Further, for each situation, the following steps of the algorithm are performed. ( 1 , 2 , 3 ).
We illustrate the operation of the algorithm in the following example. Suppose that during monitoring of heat consumption objects 10 different situations are recorded (table 1, figure. 4).  A graphical representation of the values of the membership function for each rule is given in Figure  5. Based on the graph, for each situation, not only the value of the membership function of the dominant rule, but also the ratio of the values of the membership functions of each rule to each other can be evaluated. The result of the algorithm execution is the conclusion based on the rule base on the quality of the heating network company and the quality of servicing heat consumption objects from the operating organization and the values of the resulting membership function for the situations under consideration (table 3).
The results obtained make it possible to take into account the oral information obtained by interviewing operating personnel and/or residents when making decisions. Further development of the proposed approach is associated with the development of a model that takes into account the full range of information obtained during measurements and during surveys.