A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings

Ceballos-Fuentealba, Irlanda; Álvarez-Miranda, Eduardo; Torres-Fuchslocher, Carlos; Campo-Hitschfeld, Maria Luisa del; Díaz-Guerrero, John

doi:10.1016/j.apenergy.2019.113953

Cited by 32 publications

(10 citation statements)

References 43 publications

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“…There are 15 papers (37.5%) in this subset. With the shift towards the digital era, the role of Machine Learning in attaining sustainability is deemed to receive much attention (Breiman, 2001 ; Ceballos-Fuentealba et al, 2019 ; Chahidi et al, 2021 ; Chu et al, 2019 ; Diaz et al, 2021 ; Duan et al, 2020 ; Fallah et al, 2018 ; Fujimoto et al, 2019 ; Hegedűs et al, 2021 ; Lim et al, 2017 ; Rafique & Jianhua, 2018 ; Schreiber et al, 2021 ; Shaharum et al, 2020 ; Trivedi et al, 2021 ; Tsai et al, 2011 ).…”

Section: Findings From Content Analysismentioning

confidence: 99%

“…To promote sustainable development of non-residential buildings, Ceballos-Fuentealba et al ( 2019 ) proposed a simulation and optimization methodology to predict the energy consumed in any building and the impact various energy conservation measures have on that building. Anther work to monitor energy consumption was done by Tsai et al ( 2011 ) where life cycle assessment is used to analyse the cost of carbon dioxide emission and mathematical programming technique is used to study the distribution of limited resources.…”

Section: Findings From Content Analysismentioning

confidence: 99%

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RETRACTED ARTICLE: Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges

et al. 2023

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In the broad sphere of Analytics, prescriptive analytics is one of the emerging areas of interest for both academicians and practitioners. As prescriptive analytics has transitioned from its inception to an emerging topic, there is a need to review existing literature in order to ascertain development in this area. There are a very few reviews in the related field but not specifically on the applications of prescriptive analytics in sustainable operations research using content analysis. To address this gap, we performed a review of 147 articles published in peer-reviewed academic journals from 2010 to August 2021. Using content analysis, we have identified the five emerging research themes. Through this study, we aim to contribute to the literature on prescriptive analytics by identifying and proposing emerging research themes and future research directions. Based on our literature review, we propose a conceptual framework for studying the impacts of the adoption of prescriptive analytics and its impact on sustainable supply chain resilience, sustainable supply chain performance and competitive advantage. Finally, the paper acknowledges the managerial implications, theoretical contribution and the limitations of this study.

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Section: Findings From Content Analysismentioning

confidence: 99%

Section: Findings From Content Analysismentioning

confidence: 99%

RETRACTED ARTICLE: Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges

et al. 2023

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“…Indeed, an essential part of the literature review is based on classical optimization techniques, which can (i) provide relevant recommendations to both end-users and energy providers; and (ii) reduce wasted energy automatically through controlling energy demand and electrical devices [80,81]. For example, in [82], the authors propose an energy optimization approach, which aims to predict the amount of energy used by the heating and cooling systems in a set of commercial or institutional buildings. Following, the potential impacts of various energy saving measures based on parameter optimization are investigated before recommending tailored actions to optimize energy consumption.…”

Section: Methodologies and Algorithms For Energy Efficiency Recommend...mentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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“…Under this situation, optimal setpoint temperature is determined by referring to a performance indicator, called distance 𝐷, presented in equation ( 1)-( 3). Based on the predictive energy models developed, HVAC energy consumption can be written as a function of HVAC setpoint temperature, shown in equation (1). Similarly, occupants' thermal dissatisfaction, i.e.…”

Section: Setpoint Optimizer and Optimization Rulesmentioning

confidence: 99%

“…1.1 Background Nowadays, it is widely accepted that buildings can account for over 30% of energy use and greenhouse gas emission [1]. In particular, it is realized that heating, ventilation and air-conditioning (HVAC) systems are the main contributors to the high energy consumption of buildings.…”

Section: Introductionmentioning

confidence: 99%

Real-time Building Heat Gains Prediction and HVAC Setpoint Optimization: an Integrated Framework

Wang,

Calautit,

Wei

et al. 2021

Preprint

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This paper proposes an integrated framework to achieve a simultaneous real-time reduction of occupants' thermal dissatisfaction and room HVAC energy consumption. The framework can optimize the HVAC setpoint temperature according to the internal heat gains predicted by a vision-based approach. When there are no occupants found by cameras, this framework will just turn off HVAC systems to reduce energy consumption. When occupants are present, the framework will determine an optimal setpoint temperature to balance occupants' thermal satisfaction and room HVAC energy consumption. During the simulated four days in the winter in a temperature climate, the utilization of this framework can lead to a reduction of heating energy by up to 36.8% and occupants' thermal dissatisfaction by up to 5.26%. During another simulated four days in the summer, the cooling energy savings would range from 3.5% to 33.9%, whilst occupants' thermal dissatisfaction could be decreased by 0.17-2.89%.

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A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings

Cited by 32 publications

References 43 publications

RETRACTED ARTICLE: Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges

RETRACTED ARTICLE: Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Real-time Building Heat Gains Prediction and HVAC Setpoint Optimization: an Integrated Framework

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