AI temporal planning for energy smart buildings

Buildings are responsible for about one-third of industrialised countries’ overall energy consumption and greenhouse gas emissions. As if this was not enough, recently, energy prices significantly increased and affected all economic areas. Making buildings more efficient and effective is the step needed toward cost reductions. Key enablers of cost-effectiveness are leveraging batteries, awareness of and adaptability to energy prices, and integrating powerful reasoning techniques to optimally and flexibly operate buildings. Researchers have tackled many of these aspects using a variety of approaches. Whereas a less investigated one is that of AI planning to coordinate actions and save energy in buildings. However, generating plans based on signals of energy prices and leveraging batteries is still an open research problem. To address this high-potential aspect, we engineer an AI planning system for improving the energy-cost effectiveness in buildings by coordinating the building’s operation based on day-ahead prices and the use of a battery, all without sacrificing the comfort of building occupants. We propose to exploit temporal planning due to its powerful modelling and reasoning features, especially in explicitly addressing time. We evaluate the effectiveness of the system in several scenarios with varying building environmental conditions. We compare the energy cost from using our planning system to a baseline cost, where we record a reduction of 43rage in favour of our system.