Optimizing building comfort temperature regulation via model predictive control

Energy efficiency and energy saving are important concepts bearing in mind by governments and population during the last years. There exist a widespread concern about fossil fuel depletions and the consequent sharp rise in their value. In particular, building is an area highly influenced by those issues. Studies indicate that 40% of the energy generated worldwide is consumed inside buildings, and then, measures to reduce energy consumption are required. In this work, an optimal controller for distributing the energy consumption rate inside a building and preserving, at the same time, the user welfare is proposed. More precisely, the paper presents a predictive control approach that obtains a high thermal comfort level optimizing the use of an HVAC (Heating, Ventilation and Air Conditioning) system by means of a cost function. The optimization procedure is based on the Lagrangian dual method, which allows the use of parallel programming paradigms in an easy way. This may reduce the computational effort proportionally to the number of processing elements when the problem to solve is large.

► We implemented a model predictive controller approach in order to control thermal comfort.
► For buildings with a large number of rooms, we use an optimization procedure.
► This optimization procedure is based on the Lagrangian dual method.
► The pair controller–optimizer allows to maintain the user's welfare inside the rooms.
► At the same time, the energy consumption rate inside a building is distributed and optimized.