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Designs
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Smart Home Design, 2nd Edition
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Smart Home Design, 2nd Edition

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Energy System Design".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 5565

Special Issue Editors

Dr. Saher Javaid

E-Mail Website
Guest Editor
School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
Interests: smart home; home energy management system (HEMS); distributed energy resources; power flow control; power system stability and control; power flow coloring; demand response; energy on demand
Special Issues, Collections and Topics in MDPI journals
Dr. Yuto Lim

E-Mail Website
Guest Editor
School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
Interests: predictive control; network coding; evolutionary multi-objective optimization; game theory; smart energy distribution; smart homes; wireless communications; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is Series II of the Special Issue “Smart Home Design”.

A smart home is a place that is equipped with information technology and computing; it can accept as well as respond to the resident's requests. Its main purpose is to provide the resident with a comfortable and convenient life through the managing of various technologies at home. A smart home system supports the control of several different systems in a household (e.g., heating, air conditioning, security, lighting, and audio/video systems) and is labeled accordingly. As more and more home appliances and consumer electronics are deployed, the power consumption of the home (i) tends to increase and (ii) leads to an increase in the risk of a power blackout. As a result, an intelligent smart home energy management system, which is responsible for observing and handling the working operations of home appliances, is needed for smart homes. This Special Issue focuses on original research and literature reviews from different areas related to their system design, analysis, operation, simulation, and control of power. Manuscript submissions in the areas mentioned are highly encouraged.

Dr. Saher Javaid
Dr. Yuto Lim
Guest Editors

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. Designs 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 1600 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

  • home networks
  • home energy management system (HEMS)
  • modeling, simulation, and optimization
  • power control
  • energy efficiency
  • energy storage
  • distributed energy resources
  • information appliances

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 3559 KiB  
Article
LSTM Networks for Home Energy Efficiency
by Zurisaddai Severiche-Maury, Wilson Arrubla-Hoyos, Raul Ramirez-Velarde, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso and Alejandro Cama-Pinto
Designs 2024, 8(4), 78; https://doi.org/10.3390/designs8040078 - 9 Aug 2024
Viewed by 764
Abstract
This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart [...] Read more.
This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart meters within a Home Energy Management System (HEMS). Additionally, a meter was installed on the distribution board to measure total consumption. Real-time data were collected at 15-min intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre, inhabited by four people. This setup enabled the capture of detailed and specific energy consumption data, facilitating data analysis and validating the system before large-scale implementation. Using the detailed power consumption information of these devices, an LSTM model was trained to identify temporal connections in power usage. Proper data preparation, including normalisation and feature selection, was essential for the success of the model. The results showed that the LSTM model was effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This study emphasises the importance of continued research on preferred predictive models and identifies areas for future research, such as the integration of additional contextual data and the development of practical applications for residential energy management. Additionally, it demonstrates the potential of LSTM models in smart-home energy management and serves as a solid foundation for future research in this field. Full article
(This article belongs to the Special Issue Smart Home Design, 2nd Edition)
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22 pages, 5457 KiB  
Article
Designing Home Automation Routines Using an LLM-Based Chatbot
by Mathyas Giudici, Luca Padalino, Giovanni Paolino, Ilaria Paratici, Alexandru Ionut Pascu and Franca Garzotto
Designs 2024, 8(3), 43; https://doi.org/10.3390/designs8030043 - 13 May 2024
Viewed by 1891
Abstract
Without any more delay, individuals are urged to adopt more sustainable behaviors to fight climate change. New digital systems mixed with engaging and gamification mechanisms could play an important role in achieving such an objective. In particular, Conversational Agents, like Smart Home Assistants, [...] Read more.
Without any more delay, individuals are urged to adopt more sustainable behaviors to fight climate change. New digital systems mixed with engaging and gamification mechanisms could play an important role in achieving such an objective. In particular, Conversational Agents, like Smart Home Assistants, are a promising tool that encourage sustainable behaviors within household settings. In recent years, large language models (LLMs) have shown great potential in enhancing the capabilities of such assistants, making them more effective in interacting with users. We present the design and implementation of GreenIFTTT, an application empowered by GPT4 to create and control home automation routines. The agent helps users understand which energy consumption optimization routines could be created and applied to make their home appliances more environmentally sustainable. We performed an exploratory study (Italy, December 2023) with N = 13 participants to test our application’s usability and UX. The results suggest that GreenIFTTT is a usable, engaging, easy, and supportive tool, providing insight into new perspectives and usage of LLMs to create more environmentally sustainable home automation. Full article
(This article belongs to the Special Issue Smart Home Design, 2nd Edition)
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21 pages, 847 KiB  
Article
Incorporating a Load-Shifting Algorithm for Optimal Energy Storage Capacity Design in Smart Homes
by Ruengwit Khwanrit, Yuto Lim, Saher Javaid, Chalie Charoenlarpnopparut and Yasuo Tan
Designs 2024, 8(1), 11; https://doi.org/10.3390/designs8010011 - 22 Jan 2024
Cited by 2 | Viewed by 2100
Abstract
In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. [...] Read more.
In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity. Full article
(This article belongs to the Special Issue Smart Home Design, 2nd Edition)
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