Endüstriyel soğuk hava depolamada makine öğrenimi ile oda bazlı enerji tüketimi tahminleme

In this thesis, there are theoretical and practical studies required for room and/or warehouse-based energy consumption prediction studies in industrial cold storage. Thesis studies also cover a part of the Research \& Development Center project studies supported by the Republic of Turkey Ministry of Industry and Technology. In industrial cold storages cooled by central system cooling, energy measurement is generally not made on a room and/or warehouse basis. The absence of room and/or warehouse-based energy consumption data causes many operational works to be done inefficiently. By obtaining room and/or warehouse consumption, it becomes possible to carry out operational works based on data. In this case, as mentioned in detail in the thesis study, a great deal of energy savings can be made. Analysis studies of the Research \& Development Center project initiated in this context were carried out. As a result of the analysis study, it has been understood that the biggest unknown in obtaining the room and/or warehouse consumption of a facility cooled by the central system is the evaporator capacities. Within the scope of the thesis studies, machine learning technology has been researched in depth. With Feature Importance Scoring methods, the capacities of the evaporator units, which are the cooling components of the rooms and/or warehouses in the facilities, were predicted using different algorithms. As a result of the software development studies of the thesis, a software module that makes capacity estimation has been obtained. This module is designed to provide a web service with REST API technology. The position of this module in the Research \& Development Center project is also included as a diagram in the discussion and findings section of the thesis study.