A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis

By D. Mariano-Hernández, L. Hernández-Callejo, Á. Zorita-Lamadrid, Ó. Duque-Pérez, F. García. 2021

589 citations recorded by the source corpus.

Abstract Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/7a28b781996634080010185f3edb825ea9629dd3

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In Electrical and Electronic Engineering

1. Dirk Abel
2. Thomas Bergs
3. Max Schwenzer
4. Muzaffer Ay

Citations in the corpus

• AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, background): “2020), coupled input and forget gate (CIFG) (Runge and Zmeureanu), deep feed forward neural networks (DFNN) (Marino et al. 2016), and convolutional neural network (CNN) (Li et al.”
• Review on model predictive control: an engineering perspective (2021, background): “The overwhelming majority of the works addressed nonresidential buildings, where only 4% included residential buildings often as one energy sink among others in a microgrid [74].”