Adaptable energy management system for smart buildings

• Published in: Journal of Building Engineering Vol. 44; p. 102748
• Main Authors: Salerno, Ilaria, Anjos, Miguel F., McKinnon, Kenneth, Gómez-Herrera, Juan A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2021
• Subjects: Building design; Demand response; Nonlinear programming; Storage; Thermal energy; Demand response; Storage; Nonlinear programming; Thermal energy; Building design
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2021.102748

This paper presents a novel adaptable energy management system (EMS) for smart buildings. In this framework we model the energy consumption of a living unit, and its energy exchanges with the surroundings. A unit is a well delimited space inside a building, for which the information on electrical consumption is known. We assume that the unit has no energy exchanges with its neighbors. Examples of units are a household, an office, a restaurant or a gym. We explicitly consider the impact of the outside environment and design features such as building orientation, automatic shading, and double façade. We formulate this problem as a nonlinear optimization model in which the living unit minimizes a performance function subject to the energy flows from and toward the unit as well as the building-specific features. It is solved using off-the-shelf solvers. We present computational experiments to validate the proposed approach, considering different objective functions and several building configurations. The experiments show that our approach enhances the unit's performance and also provides demand flexibility for the grid. We demonstrate that for heating a unit in Montreal, Canada, there are periods where the EMS alone can lower the electricity cost by up to 26% and the energy consumption by up to 14%. If the EMS is combined with smart design features, the electricity cost of heating can be lowered by up to 35%, the cost of cooling by up to 97%, and the energy consumption by up to 49%. •We present a novel adaptable energy management system for smart buildings.•We model the energy consumption of a living unit and its energy exchanges with the surroundings.•We explicitly consider the outside environment and features such as orientation, automatic shading, and double façade.•We use nonlinear optimization to optimize performance subject to the energy flows and building-specific features.•For a unit in Montreal, Canada, for some periods the electricity costs and energy consumption can be dramatically reduced.