Selective reinforcement graph mining approach for smart building energy and occupant comfort optimization

Optimizing Building energy consumption is a key solution to reducing their environmental impact. In this context, Information Technology can be harnessed by deploying sensors inside buildings, to collect relevant data about both energy consumed and occupant behavior, since occupants influence building appliances, such as HVAC, lights, and hot water tanks. Predicting room occupancy can be a solution to heat/cool rooms for instance. But, as prediction models are not often accurate, we may face situations where HVAC is activated while the rooms are empty or vice-versa, leading to either a waste of energy or a lack of occupants’ comfort. To predict user behavior, detect prediction errors, and correct the model, we introduce a graph mining-based optimization method that combines an occupant behavior prediction model and a selective reinforcement learning method, where error detection relies on sensors that detect real-time occupancy of rooms. We experimented with our approach on simulated data and results showed that, compared to conventional HVAC management, our model can reduce up to 57.8% of HVAC energy consumption, and provide up to 94.3% of occupants’ comfort when using the prediction method only, and up to 80.1% of HVAC energy consumption, and provide up to 97% of occupants’ comfort when using the reinforcement method to correct prediction errors. • The main objective is to ensure building occupants’ thermal comfort while optimizing building energy consumption. • A combined AI-based approaches is proposed: Graph Mining and Reinforcement Learning to optimize building energy usage while ensuring occupant comfort. • Occupancy prediction is used to schedule the functioning of the HVAC inside the rooms. • Room occupancy status is tracked based on a real-time data collected by sensors deployed inside a building. • Occupancy tracking is used to correct the prediction errors by deploying a reward reinforcement strategy. • Selective reinforcement method is also utilized to penalize the rooms that are occupied during a short period of time. • Experiments carried out on real-world building occupancy datasets. • Experiments results showed that our approach allows saving up to 80.1% of HVAC energy consumption and ensuring up to 97% of thermal comfort for building occupants.