Location-aware multi-objective optimization for energy cost management in semi-public buildings using thermal discomfort information

Owing to the large size and dynamic environment in semi-public buildings, reducing energy consumption while providing desirable comfort to users, is challenging. Setting air conditioning systems to a fixed temperature, based on user demands and limited experience, leads to steep increase in costs for building operators. Seasonal changes in local energy tariff further impact costs, eventually affecting the comfort rendered to users. Moreover, indoor environments in buildings vary over regions and a one-size-fits-all approach for cost management may no longer be applicable. In this regard, we propose an adaptive multi-objective optimization model, capable of delivering customized thermal settings in buildings based on geographical location and its influence on user preferences, government regulations and energy pricing. Energy cost is based on the non-linear relation of cost with energy consumed, which is consecutively modelled in terms of the building-specific heat load. A customizable user comfort model developed from the well-known Fanger's model, considers personal parameters of users, further adding to the adaptability of the solution. The model is envisioned to enable building operators in determining the indoor thermal setting while fixing the highest acceptable (dis)comfort level or energy cost as per the set budget.