Towards Plug&amp;Play Smart Thermostats for Building’s Heating/Cooling Control

Marantos, Charalampos; Lamprakos, Christos P.; Siozios, Kostas; Soudris, Dimitrios

doi:10.1007/978-3-030-03640-9_10

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…For district level energy systems, centralized scheduling may give the best coordination but decentralization of DR efforts can give improved scalability and more options (Harb et al, 2015). In this regards, IoT thermostats may be expected to play a major role (Marantos et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Demand response events in district heating: Results from field tests in a university building

Mishra

Jokisalo

Kosonen

et al. 2019

Sustainable Cities and Society

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Demand side management will play a major role in future energy systems. However, while they have been explored in some depth for electricity grids, a similar progress has not been made for district heating networks (DHN). To this end, the current work field-tested the effect of demand side management, in the form of price based, demand response (DR) events, in the DHN catering to a university building. Responding to variations in a pricing model, the temperature of inlet water was varied from the heating water substation. Using combinations of parameters, 11 different DR scenarios were executed. To gauge the effect of the DR interventions, inlet water temperature, room air temperature, and occupant satisfaction were monitored. Depending on the constraints imposed, significant variations in the inlet water temperature and peaks and drops in the room air temperature were noted. The different DR scenarios did not greatly alter occupant satisfaction levels. The study was able to provide useful data from field tests of DR events in a DHN. The data also showed that price based DR events may be triggered and executed without significantly impacting occupant satisfaction with thermal comfort of the premises.

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Section: Discussionmentioning

confidence: 99%

Demand response events in district heating: Results from field tests in a university building

Mishra

Jokisalo

Kosonen

et al. 2019

Sustainable Cities and Society

View full text Add to dashboard Cite

show abstract

“…Today, more and more research is related to the development and scope of thermostat operation. Marantos et al [29] in their research apply a smart thermostat concept which focuses mainly on cost reduction and deployment flexibility so they can be adopted on a big scale in many buildings and regions. Its idea integrates supervised and reinforcement learning to address the challenge of meeting occupants' thermal comfort requirements while minimizing energy usage.…”

Section: Heating and Coolingmentioning

confidence: 99%

AI-Driven Urban Energy Solutions—From Individuals to Society: A Review

Stecuła,

Wolniak,

Grebski

2023

Energies

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This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI’s role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption.

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Towards Plug&Play Smart Thermostats for Building’s Heating/Cooling Control

Cited by 2 publications

References 37 publications

Demand response events in district heating: Results from field tests in a university building

Demand response events in district heating: Results from field tests in a university building

AI-Driven Urban Energy Solutions—From Individuals to Society: A Review

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