Retrofitting Planning Optimization

Abstract

In response to the European Commission's 2020 Renovation Wave strategy aiming to double the annual rate of energy renovation for buildings by 2030, this thesis addresses the challenge of optimizing retrofitting planning using planning optimization algorithms. Despite the economic and practical hurdles identified in existing research, limited attention has been given to optimizing the timing and sequence of retrofitting actions. Recognizing the complexity introduced by uncertainty factors such as material degradation rates,this study draws parallels between retrofitting and predictive maintenance problems. This thesis is divided into two main sections: the first establishes the theoretical foundation, covering retrofitting principles, Markov Decision Processes, and planning algorithms like Q-learning and Value Iteration; the second applies these theories to practical implementation to develop a methodology for retrofitting planning optimization. Detailed methodologies are discussed, emphasizing building energy performance and degradation factors, with conclusions drawn to inform the simulation of building performance over time. This comprehensive approach aims to advance the current understanding and application of planning optimization in the building retrofitting domain.