An approach for improving energy efficiency in a commercial building with learning consideration and availability constraints

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Industrial Engineering, Turkey

Approval Date: 2014




Energy efficiency applications are increasingly becoming more popular as available natural resources such as natural gas and fossils have been running out. In this study, we address the scheduling problem of the heating, ventilation and air conditioning (HVAC) system of a commercial building with a supporting automation system that controls the energy resources so as to increase energy efficiency. We formulate the problem as a multilevel generalized assignment model to obtain the schedule for the HVAC system and to determine the units of the system that should work in different periods of a day. The objective is to minimize the electricity consumption subject to the user requirements. In our formulation we consider the dynamic nature of weather conditions, multi-level structures, availability conditions and learning effects of the resources, and the circulation of people in the building as well. Moreover, we propose a tabu search algorithm with ejection chains to solve such a complex and large model in reasonable times, and present the computational results. Our tabu search algorithm provides satisfactory results with a significant amount of reduction in electricity consumption, without remarkably increasing the computational effort required.