Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement

Abstract

Energy efficiency in the building industry is related to the amount of energy that can be saved through thermal improvement. Therefore, it is important to determine the energy saving potential of the buildings to be thermally upgraded in order to check whether the set targets for the amount of energy saved will be reached after the implementation of corrective measures. In real residential buildings, when starting to make energy calculations, one can often encounter the problem of incomplete architectural documentation and inaccurate data characterizing the object in terms of thermal (thermal resistance of partitions) and usable (number of inhabitants). Therefore, there is a need to search for methods that will be suitable for quick technical analysis of measures taken to improve energy efficiency in existing buildings. The aim of this work was to test the usefulness of the type Takagi-Sugeno fuzzy models of inference model for predicting the energy efficiency of actual residential buildings that have undergone thermal improvement. For the group of 109 buildings a specific set of important variables characterizing the examined objects was identified. The quality of the prediction models developed for various combinations of input variables has been evaluated using, among other things, statistical calibration standards developed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The obtained results were compared with other prediction models (based on the same input data sets) using artificial neural networks and rough sets theory.

The System of Models and Optimization of Operating Modes of a Catalytic Reforming Unit Using Initial Fuzzy Information

“…As a specific technological unit for the production of high-quality mother fuel, this work considers a catalytic reforming unit of the LG-35-11/300-95 model operating at the Atyrau refinery. The issues of increasing the efficiency of the work of the investigated by ensuring the optimal operating mode of the catalytic reforming unit are investigated and solved using modern mathematical methods and methods of fuzzy mathematics [6][7][8][9][10][11][12][13][17][18][19].…”

“…This led to the emergence and development of a promising direction in science and technology-a fuzzy approach to modeling, optimization, and control of complex CES based on the methods of expert assessments and theories of fuzzy sets [7][8][9][10][11][12].…”

“…To conduct a comparative analysis, we analyze the works [6,9,11,17], in which approaches to the development of mathematical models of objects are studied in conditions of fuzzy initial information. In the analyzed works, the issues of production control with fuzzy initial information [6], the application of fuzzy set theories [9], as well as the problems of modeling a catalytic cracking reactor [11] and controlling the reforming process [17] were studied.…”

“…To conduct a comparative analysis, we analyze the works [6,9,11,17], in which approaches to the development of mathematical models of objects are studied in conditions of fuzzy initial information. In the analyzed works, the issues of production control with fuzzy initial information [6], the application of fuzzy set theories [9], as well as the problems of modeling a catalytic cracking reactor [11] and controlling the reforming process [17] were studied. However, in these and other well-known works on modeling complex objects in a fuzzy environment, the issues of developing a system of mathematical models of interconnected technological units have not been sufficiently studied.…”

“…At the same time, to collect fuzzy information around expert opinions in natural language, methods of expert assessments are used, for example, the Delphi method. To formalize, process, and use fuzzy information in the construction of models, methods of fuzzy set theories and combined (hybrid) methods of developing models are used [9][10][11]22,28]. As can be seen from Table 1, the optimal type of model for reactors is a combined model, which is built using available information of a different nature, for example, statistical data and fuzzy information.…”