Efficient Scheduling of Smart Building Energy Systems using AI Planning

Abstract

Buildings account for a significant share of global energy consumption, with Heating, Ventilation, and Air Conditioning (HVAC) systems being responsible for up to 60% of a building’s energy usage. For this purpose, existing scheduling and control solutions can be used to design more sustainable energy systems and limit their environmental impact. However, these approaches mainly consider HVAC, ignoring other energy systems in buildings such as lighting control and plug loads. In addition, these solutions have to be customized for a specific building instance, hindering portability across different application domains. This paper presents a holistic approach for efficiently scheduling smart building energy systems through AI planning methodologies. AI planning enables decoupling domain knowledge from problem representations, enhancing portability and allowing for straightforward runtime adaptation when needed. We evaluate our approach in a smart office setting and show how AI planning enables reducing energy consumption by up to 30%.