Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings

Korkidis, Panagiotis; Dounis, Anastasios I.; Kofinas, Panagiotis

doi:10.3390/en14164971

Cited by 8 publications

(5 citation statements)

References 35 publications

(67 reference statements)

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“…The European Union has already mandated the possibility of buying only electric cars from 2035 [16]. It involves building an entirely new infrastructure and modernizing the entire energy sector dedicated to the electromobility [140]. Charging stations for electric cars as an essential product for EVs are starting to become a new reality.…”

Section: Discussionmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues.

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Section: Discussionmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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“…The references have been categorized in terms of the application class, objective function, and building type that were described in the immediately preceding section. Cost & Comfort [103], [104] Other Academic [105] Comfort Mixed/NA [106] Other [107], [98] P2P Trading Cost [108], [109] Residential [110] EV, ES, and RG [111], [112] Mixed/NA [113] Other Residential [114], [115] Other/Mixed Cost & Comfort Commercial [116] Academic [117], [96], [118], [119], [120], [121] Residential [122] Other [123], [124] Cost [125] Mixed/NA [126] Cost & Comfort [127], [128] Cost & Load Balance [129] Other [130] P2P Trading Cost Distributed RL [131], [132] Other [138] Cost & Comfort Commercial Model Based RL [139] HVAC, Fans, WH Cost Residential Other (CARLA) [140] Cost & Comfort Commercial Other (Context. RL)…”

Section: Reinforcement Learning Algorithms In Home Energy Management ...mentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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“…References that used either a combination of two or more approaches, or any other approach not commonly used in RL literature, are shown in Table 5. Cost and Comfort [114,115] Other Academic [116] Comfort Mixed/NA [117] Other [109,118] P2P Trading Cost [119,120] Residential [121] EV, ES, and RG [122,123] Mixed/NA [124] Other Residential [125,126] Other/Mixed Cost and Comfort Commercial [127] Academic [107,[128][129][130][131][132] Residential [133] Other [134,135] Cost [136] Mixed/NA [137] Cost and Comfort [138,139] Cost and Load Balance [140] Other [141] P2P…”

Section: Reinforcement Learning Algorithms In Home Energy Management ...mentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

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Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings

Cited by 8 publications

References 35 publications

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

Reinforcement Learning: Theory and Applications in HEMS

Reinforcement Learning: Theory and Applications in HEMS

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