Special Issue "Energy Efficiency in Smart Homes and Grids"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (20 May 2020).

Special Issue Editors

Dr. Anna Fensel
Website
Guest Editor
Department of Computer Science, Semantic Technology Institute (STI) Innsbruck, University of Innsbruck, Innsbruck, Austria
Interests: semantic technology; knowledge graphs; explainable AI; data value chain; social web
Special Issues and Collections in MDPI journals
Dr. Juan Miguel Gómez Berbís
Website
Guest Editor
Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain
Interests: Semantic Technologies; Semantic IoT; Blockchain; Enterprise Knowledge Graphs; Business Formal Methods
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleague,

This Special Issue focuses on advancements in the energy efficiency of smart homes and smart grids. This encompasses smart homes, smart grids, smart cities and villages, ICT solutions for efficient energy consumption, as well as energy production and distribution. This Special Issue is especially interested in receiving high quality, unpublished submissions that focus on advancing the technologies for sensor infrastructures, knowledge management and engineering, as well as user engagement: in particular, energy savings should take place without a decrease in residents’ quality of life. We are interested in back-end technologies, such as for the predictive maintenance and optimization of energy distribution and consumption, as well as in front-end technologies, such as end user interfaces, gamification and techniques facilitating behavior change. Further, data are playing an increasingly important role in energy efficiency, and are gaining a leading role in various domains, such as future building certification, smart grid and meter management, and predictive maintenance. Specifically, we are looking into the combination of machine learning and semantic techniques, for example, combining them in order to create high-quality knowledge graphs in the energy efficiency domain for smart homes and smart grids.

Dr. Anna Fensel
Dr. Juan Miguel Gómez Berbís
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 papers will be 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 1800 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

  • Intelligent Cyber–Physical ICT Infrastructures: Smart Home, Smart Village, Smart City
  • Smart Homes and Energy Efficiency
  • Semantics and Ontologies for Smart Homes and Smart Grids
  • Smart Home and Smart Grid Data Management with Knowledge Graphs
  • Smart Metering
  • Building Information Management and Energy Efficiency
  • Predictive Maintenance
  • Smart Energy Grids
  • Energy Efficient Data Centers
  • RFID and Sensors, Intelligent Sensors
  • Embedded Computing Systems
  • Cloud Computing in Smart Home Technology
  • Machine Learning for Energy Efficiency
  • Energy Data Collection and Processing
  • Energy Data Repository and Management in Cloud
  • Smart Building and City Data Management
  • Semantic Rules, Policies and Context-Awareness in Smart Buildings
  • Behavioral Change in Smart Cities
  • Gaming for Energy Saving
  • Energy Efficiency Education
  • Ambient Intelligence, Human–Computer/ Human–Machine/ Human–Device Interface
  • Physical and Conceptual Modeling of Cyber-Physical Environments
  • Human Factors, Ethics and Usability
  • Ubiquitous and Pervasive Computing Concerns
  • Smart Management of Home Appliances
  • Management of Home Energy Concerns
  • Context Awareness and Autonomous Computing
  • Residential Networks and Services
  • Mobile Services
  • Social, Policy, Privacy, Security Concerns
  • Middleware Design and Support for Smart Home
  • Guidelines and Policy for Future Energy Efficiency Building Certification
  • Innovative Smart Home Applications and Services
  • Smart Environment Monitoring and Control

Published Papers (5 papers)

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Research

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Open AccessArticle
Technology Management Leading to a Smart System Solution Assuring a Decrease of Energy Consumption in Recreational Facilities
Energies 2020, 13(13), 3425; https://doi.org/10.3390/en13133425 - 03 Jul 2020
Abstract
Improvement of the energy efficiency of public buildings appears to be one of the best ways to simultaneously reduce energy consumption as well as the negative impacts on the environment. The work is dedicated to the analysis of modernization process of the energy [...] Read more.
Improvement of the energy efficiency of public buildings appears to be one of the best ways to simultaneously reduce energy consumption as well as the negative impacts on the environment. The work is dedicated to the analysis of modernization process of the energy system in a sports facility in a way leading to design of smart energy system. The proposed solution, being a specific case study, offers optimal use of energy in the facility, significantly reducing the demand for energy derived from fossil fuels (heat providers and conventional power plants). The project, on its first step, consists of recovering energy from sewage that usually is irretrievably lost. This option allows to achieve the assumed goals simultaneously optimizing the investment costs. The proposed solution mitigates air pollution and harmful gas and dust emissions to the atmosphere, and contributes to an increase of both the attractiveness and competitiveness of the area in which the sports facility is located. The next step will be further automation of the system and intelligent synchronization of time-dependencies of the processes. Full article
(This article belongs to the Special Issue Energy Efficiency in Smart Homes and Grids)
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Open AccessArticle
A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid
Energies 2020, 13(9), 2244; https://doi.org/10.3390/en13092244 - 03 May 2020
Cited by 1
Abstract
Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high [...] Read more.
Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate. Full article
(This article belongs to the Special Issue Energy Efficiency in Smart Homes and Grids)
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Open AccessArticle
HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving
Energies 2020, 13(5), 1097; https://doi.org/10.3390/en13051097 - 02 Mar 2020
Cited by 2
Abstract
Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is [...] Read more.
Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption. Full article
(This article belongs to the Special Issue Energy Efficiency in Smart Homes and Grids)
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Open AccessArticle
Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction
Energies 2020, 13(4), 862; https://doi.org/10.3390/en13040862 - 16 Feb 2020
Cited by 1
Abstract
Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in [...] Read more.
Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings. Full article
(This article belongs to the Special Issue Energy Efficiency in Smart Homes and Grids)
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Review

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Open AccessReview
Smart Electrochromic Windows to Enhance Building Energy Efficiency and Visual Comfort
Energies 2020, 13(6), 1449; https://doi.org/10.3390/en13061449 - 20 Mar 2020
Cited by 2
Abstract
Electrochromic systems for smart windows make it possible to enhance energy efficiency in the construction sector, in both residential and tertiary buildings. The dynamic modulation of the spectral properties of a glazing, within the visible and infrared ranges of wavelengths, allows one to [...] Read more.
Electrochromic systems for smart windows make it possible to enhance energy efficiency in the construction sector, in both residential and tertiary buildings. The dynamic modulation of the spectral properties of a glazing, within the visible and infrared ranges of wavelengths, allows one to adapt the thermal and optical behavior of a glazing to the everchanging conditions of the environment in which the building is located. This allows appropriate control of the penetration of solar radiation within the building. The consequent advantages are manifold and are still being explored in the scientific literature. On the one hand, the reduction in energy consumption for summer air conditioning (and artificial lighting, too) becomes significant, especially in "cooling dominated" climates, reaching high percentages of saving, compared to common transparent windows; on the other hand, the continuous adaptation of the optical properties of the glass to the changing external conditions makes it possible to set suitable management strategies for the smart window, in order to offer optimal conditions to take advantage of daylight within the confined space. This review aims at a critical review of the relevant literature concerning the benefits obtainable in terms of energy consumption and visual comfort, starting from a survey of the main architectures of the devices available today. Full article
(This article belongs to the Special Issue Energy Efficiency in Smart Homes and Grids)
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