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

Mariano-Hernández, Deyslen; Hernández-Callejo, Luís; Zorita-Lamadrid, Ángel L.; Duque-Pérez, Óscar; García, Félix Santos

doi:10.1016/j.jobe.2020.101692

Cited by 322 publications

(163 citation statements)

References 126 publications

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“…Several notable articles have been published on BEMS. Mariano-Hernandez [5] surveyed the literature related to different management strategies for BEMS highlighting demand-side management, model predictive control and optimization in residential and non-residential buildings. However, the authors did not highlight the different intelligent controls and optimization algorithms in detail.…”

Section: Comfort Index Optimizationmentioning

confidence: 99%

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Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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Section: Comfort Index Optimizationmentioning

confidence: 99%

“…building's energy economy [5]. Hence, nowadays, the efficient utilization of energy in buildings and their impacts on global warming and climate change are the most challenging issues [6], [7].…”

mentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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“…Minoli et al [7] discussed some Internet of Things (IoT) requirements and considerations for BEMS and energy optimization. Mariano et al [8] focused on various control strategies but only for BEMS. Bayram et al [2] reviewed the behind-the-meter EMS in the smart grid by addressing the system's three-layer classification approach.…”

Section: Contributionsmentioning

confidence: 99%

“…They integrate digital sensing and communication devices that make it possible to track energy usage in real-time, control smart appliances, and communicate with the utilities and grid. There has recently been growing attention in smart energy management systems (EMS) for smart homes (home energy management systems, HEMS), smart buildings (building energy management systems, BEMS), and smart grids [3,[6][7][8]. Optimization-based methods are commonly used in these energy management systems.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

Nguyen

Jung

et al. 2020

Sustainability

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Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

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“…The well‐known model predictive control (MPC) has been proposed as a suitable solution for smart grid 31 and the next generation of the EMSs within economic dispatch scopes. In Reference 32, a two‐layer MPC strategy for charging electric vehicles (EVs) connected to the MG was proposed, taking into consideration the feedback from aggregated EVs uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical smart energy management strategy based on cooperative distributed economic model predictive control for multi‐microgrids systems

Brahmia

Wang

Oliveira

et al. 2020

Int Trans Electr Energ Syst

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Summary With the new edge of smart grids technology, the concept of efficient energy management among cooperative microgrids (MGs) distribution networks for the next generation of energy trading applications has become more and more challenging. This article presents smart energy dispatch for multi‐MGs (MMGs) systems based on the cooperative distributed economic model predictive control (DEMPC) within a hierarchical structure. The optimization framework consists of two levels. First, a DEMPC coordinator computes the optimal energy dispatch solution, taking into account the energy surplus with a power‐sharing policy, the retail electricity price based on the time of use (TOU), and the distributed generators units. For the second level, each local controller executes the energy scheduling reference from the first level. Thus, the MMGs system achieves a global supply‐demand balance. Moreover, the proposed approach improves the utilization of renewable power and demonstrates optimal energy dispatch while reducing operating costs. Finally, to validate the efficiency and resiliency of our proposed approach, two cases of study are presented based on three interconnected MGs system under a cooperative and non‐cooperative optimization. Simulation results show the advantage of the cooperative strategy with 28% for MG 1, and 20% for MG 2 in terms of daily operating costs savings.

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

Cited by 322 publications

References 126 publications

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

Hierarchical smart energy management strategy based on cooperative distributed economic model predictive control for multi‐microgrids systems

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