Coordinated energy management for a cluster of buildings through deep reinforcement learning

Pinto, Giuseppe; Piscitelli, Marco Savino; Vázquez-Canteli, José R.; Nagy, Zoltán; Capozzoli, Alfonso

doi:10.1016/j.energy.2021.120725

Cited by 70 publications

(22 citation statements)

References 27 publications

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“…The application of meta-heuristic methods is still in its infancy as a research field, and the possibilities are immense. Machine learning and data-science have seen very significant advances in recent years, and although the computational needs for training deep models are extreme, cloud resources are making these much more accessible (Dong et al, 2021;Kou et al, 2021;Krizhevsky et al, 2012;Pinto et al, 2021). In addition, the requirements for inference are considerably more modest, meaning pre-trained models can be used on accessible and affordable devices.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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“…The system benefits from the replacement of internal heating and cooling devices of the respective domestic appliances and the simultaneous utilization of both energy flows -warm and coldof the heat pump. The paper by Pinto et al [125] explores the opportunity to enhance energy flexibility of a cluster of buildings, taking advantage from the mutual collaboration between single buildings by pursuing a coordinated approach in energy management. Results shows a reduction of operational costs of about 4%, together with a decrease of peak demand up to 12%.…”

Section: Energy Performance Improvements Of the Existing Buildingsmentioning

confidence: 99%

Paving the way for the Paris Agreement: Contributions of SDEWES science

Guzović

Duić

Piacentino

et al. 2023

Energy

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“…With the development and mature application of AI technology, the prediction model was improved with much higher accuracy, efficiency and reliability for dynamic management. Pinto et al [60] investigated the energy-management strategy of single buildings at building cluster level. An energy management controller based on Deep Reinforcement Learning (DRL) was adopted for optimizing the energy consumption and coordinating the behavior of the clusters.…”

Section: Energy Saving and Emission Reductionmentioning

confidence: 99%

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

et al. 2022

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The COVID-19 pandemic has had a significant impact on the supply chains of traditional fossil fuels. According to a report by the International Energy Agency (IEA) from 2020, oil-refining activity fell by more than the IEA had anticipated. It was also assumed that the demand in 2021 would likely be 2.6 million bpd below the 2019 levels. However, renewable markets have shown strong resilience during the crisis. It was determined that renewables are on track to meet 80% of the growth in electricity demand over the next 10 years and that sustainable energy will act as the primary source of electricity production instead of coal. On the other hand, the report also emphasized that measures for reducing environmental pollution and CO2 emissions are still insufficient and that significant current investments should be further expanded. The Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series is dedicated to the advancement and dissemination of knowledge on methods, policies and technologies for improving the sustainability of development by decoupling growth from the use of natural resources. The 15th SDEWES conference was held online from 1–5 September 2020; more than 300 reports with 7 special sections were organized on the virtual conference platform. This paper presents the major achievements of the recommended papers in the Special Issue of Energies. Additionally, related studies connected to the above papers published in the SDEWES series are also introduced, including the four main research fields of energy saving and emission reduction, renewable energy applications, the development of district heating systems, and the economic assessment of sustainable energy.

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Coordinated energy management for a cluster of buildings through deep reinforcement learning

Cited by 70 publications

References 27 publications

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Paving the way for the Paris Agreement: Contributions of SDEWES science

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

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