A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Aguilar, José; Garcés-Jiménez, Alberto; R-Moreno, María D.; García, Ricardo Hernández

doi:10.1016/j.rser.2021.111530

Cited by 131 publications

(73 citation statements)

References 70 publications

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“…Another open problem worth mentioning is the application of artificial intelligence techniques to model the different components in Figure 7. One may notice that recent surveys dedicated to this topic either focus on specific equipment [212,213] or they have a more general scope [214,215] but do not explain how to use mathematical models to decrease the complexity of the calculation with datadriven methods. To conclude, this work has reviewed the mathematical models of building physics and popular technologies, and has presented assemblable models.…”

Section: Discussionmentioning

confidence: 99%

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

2022

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The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first presented. Then, it is shown how the collected mathematical models can be linked. Merging with two frequently used EMS strategies, namely rule-based and model predictive controls, is discussed. This work provides an extendable map to model and control buildings and intends to be a foundation for building researchers, designers and engineers.

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Section: Discussionmentioning

confidence: 99%

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

2022

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“…Artificial intelligence (AI) is undoubtedly one of the most epoch-making innovations in contemporary history; it has probably contributed more than expected and accelerated the development of other industries and fields [6,7,21]. Artificial intelligence (AI) is intelligence that is performed by machines or software by thinking and behaving intelligently like humans [3,4,22]. Furthermore, when distinguishing between "humanity" and "rationality", artificial intelligence is differentiated into human performance and idea performance [1,23].…”

Section: What Is Artificial Intelligence (Ai)?mentioning

confidence: 99%

“…More concretely, artificial intelligence (AI) systems are designed to resemble the human brain, leading to the ability of deep learning. As a result, the system is capable of tasks such as self-driving, medical diagnosis, program coding, financial problems, fraud detection, market prediction, product recommendations, image classification, and language translation [3][4][5]. In recent years, with the further development of artificial intelligence, we have also witnessed and been surprised at how AI has become part of our popular culture.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Determinants for Adoption of Intelligent Personal Assistant: An Empirical Study

Sun

2021

Applied Sciences

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With the accelerating advances in artificial intelligence (AI) technologies, the rising demand for the intelligent personal assistant is expected to rapidly increase in the following decades. As customer loyalty has become a critical element in gaining a competitive advantage and many marketers pay great attention to it, this research aims to explore the factors that directly affect customer loyalty toward the intelligent personal assistant. The main motivation is to investigate and identify the direct effects that variables such as customer innovativeness and perceived value have on customer loyalty. Additionally, both product knowledge and corporate image will be examined as moderators. The results obtained demonstrate that perceived value has a positive influence on customer loyalty. Furthermore, product knowledge moderates the influence of functional and hedonic innovativeness on customer satisfaction. The study’s main contributions provide a better understanding of customer loyalty. This research aims to be viewed as a specific guide to provide effective practices that companies should implement to help them reallocate resources appropriately and enhance customer value creation. The results indicate that the higher the level of customer perceived value, the better the customer loyalty will be. The findings represent that corporate image has a positive moderating effect on the influence of customer innovativeness on perceived value. This study draws on the outcomes of administrative suggestions and recommends a few observational strategies to improve the monetary administration in the intelligent personal assistant industry.

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“…Fanger, which integrates heat-balance equations and empirical studies of skin temperature to determine comfort [28]. The three indicators derived from Fanger's comfort equation concern: (1) predicted mean vote (PMV); (2) predicted percentage dissatisfied (PPD); and (3) lowest possible percentage dissatisfied (LPPD). The first parameter estimates the average thermal comfort selection and ranges from cold (−3) to very hot (3), with desired limits in [−0.5 + 0.5] field.…”

Section: Measuring Comfort Costmentioning

confidence: 99%

“…Energy saving in buildings represents a substantial scientific area for curbing energy consumption and decreasing energy wastage. Recent scientific studies have exposed numerous methodologies of and applications for mitigating the environmental impact of buildings' operation through intelligent energy management [1,2].…”

Section: Introductionmentioning

confidence: 99%

Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study

et al. 2021

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The existing literature on energy saving focuses on large-scale buildings, wherein the energy-saving potential is substantially larger than smaller-scale buildings. However, the research intensity is significantly less for small-scale deployments and their capacities to regulate energy use individually, directly and without depreciating users’ comfort and needs. The current research effort focused on energy saving and user satisfaction, concerning a low-cost—yet technically sophisticated—methodology for controlling conventional residential HVAC units through cheap yet reliable actuation and sensing and auxiliary IoT equipment. The basic ingredients of the proposed experimental methodology involve a conventional A/C unit, an Arduino microcontroller, typical wireless IoT sensors and actuators, a configured graphical environment and a sophisticated, model-free, optimization-and-control algorithm (PCAO) that portrays the ground basis for achieving improved performance results in comparison with conventional methods. The main goal of this study was to produce a system that would adequately and expeditiously achieve energy savings by utilizing minimal hardware/equipment (affordability). The system was designed to be easily expandable in terms of new units or thermal equipment (expandability) and also to be autonomous, requiring zero user interventions at the experimental site (automation). The real-life measurements were collected over two different seasonal periods of the year (winter, summer) and concerned a conventional apartment in the city of Xanthi, Northern Greece, where summers and winters exhibit quite diverse climate characteristics. The final results revealed the increased efficiency of PCAO’s optimization in comparison with a conventional rule-based control strategy (RBC), as concerns energy savings and user satisfaction.

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A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Cited by 131 publications

References 70 publications

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

Analyzing Determinants for Adoption of Intelligent Personal Assistant: An Empirical Study

Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study

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