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Energies
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Smart Thermostats for Energy Saving in Buildings
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Smart Thermostats for Energy Saving in Buildings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (2 March 2022) | Viewed by 10426

Special Issue Editor

Dr. Therese Peffer

E-Mail Website
Guest Editor
California Institute for Energy and Environment, University of California, Berkeley, CA 94720, USA
Interests: thermal energy; thermostat; building; thermal comfort
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editor is delighted to invite submissions to a Special Issue of Energies entitled “Smart Thermostats for Energy Savings in Buildings”. The past decade has seen a paradigm shift in advanced control and user interfaces for thermostats in residential and commercial buildings, which presents both opportunities and challenges for energy savings. Thermostats are now more often networked and embedded devices, as part of the Internet of Things, and thus considered “smart”. These advanced thermostats enable demand response and use of heating ventilation and air conditioning (HVAC) equipment as distributed energy resources (DERs) towards a more resilient and effective utility grid. Different thermostat interfaces may be used for specialized audiences such as seniors, people of low income, or schools. Networked thermostats allow multiple building systems to interoperate, such as whole-building energy metering and ceiling fans, in order to optimize comfort and energy savings. Thermostats may control ventilation as well, which may play a role in improving air quality and reducing potential infectious diseases such as COVID-19.

This Special Issue will deal with novel user interfaces, optimization, and control techniques of advanced thermostats for energy savings in residential and commercial buildings. Topics of interest for publication include, but are not limited to:

  • Advanced or specialized thermostat user interfaces;
  • Usability studies;
  • Feedback for improved energy savings;
  • Advanced thermostat designs for specialized audiences, such as low-income or disadvantaged communities;
  • Advanced control and machine learning algorithms that reduce energy;
  • Data fusion and social data mining for designing advanced connected thermostat functions;
  • Commercial building energy management;
  • Enabling HVAC as DER;
  • Demand response (DR) and grid responsiveness;
  • Networked thermostats integrated with other systems (e.g., ceiling fans) for energy savings;
  • Improved thermal comfort and energy savings optimization;
  • Advanced ventilation strategies.

Dr. Therese Peffer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

energy efficiency; demand response; grid interactive buildings; usability; community microgrid

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Published Papers (2 papers)

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Research

28 pages, 6170 KiB  
Article
Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats
by Adán Medina, Juana Isabel Méndez, Pedro Ponce, Therese Peffer, Alan Meier and Arturo Molina
Energies 2022, 15(5), 1811; https://doi.org/10.3390/en15051811 - 1 Mar 2022
Cited by 25 | Viewed by 5259
Abstract
Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save [...] Read more.
Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting. Full article
(This article belongs to the Special Issue Smart Thermostats for Energy Saving in Buildings)
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27 pages, 27792 KiB  
Article
Energy Management System Based on a Gamified Application for Households
by Manuel Avila, Juana Isabel Méndez, Pedro Ponce, Therese Peffer, Alan Meier and Arturo Molina
Energies 2021, 14(12), 3445; https://doi.org/10.3390/en14123445 - 10 Jun 2021
Cited by 19 | Viewed by 4041
Abstract
Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim [...] Read more.
Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs. Full article
(This article belongs to the Special Issue Smart Thermostats for Energy Saving in Buildings)
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