Demand-Response in Smart Buildings
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Demand-Response Smart Buildings in • Denia Kolokotsa, Gloria Pignatta and Kostas Gobakis Demand-Response in Smart Buildings Edited by Denia Kolokotsa, Gloria Pignatta and Kostas Gobakis Printed Edition of the Special Issue Published in Energies www.mdpi.com/journal/energies Demand-Response in Smart Buildings Demand-Response in Smart Buildings Special Issue Editors Denia Kolokotsa Gloria Pignatta Kostas Gobakis MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Special Issue Editors Denia Kolokotsa Gloria Pignatta Kostas Gobakis Technical University of Crete UNSW Sydney Technical University of Crete Greece Australia Greece Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Energies (ISSN 1996-1073) (available at: https://www.mdpi.com/journal/energies/special issues/ smart buildings). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Article Number, Page Range. ISBN 978-3-03928-266-1 (Pbk) ISBN 978-3-03928-267-8 (PDF) c 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Special Issue Editors ..................................... vii Preface to ”Demand-Response in Smart Buildings” ......................... ix Sakeena Javaid, Nadeem Javaid, Tanzila Saba, Zahid Wadud, Amjad Rehman and Abdul Haseeb Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework Reprinted from: Energies 2019, 12, 815, doi:10.3390/en12050815 .................... 1 Francesco Mancini and Benedetto Nastasi Energy Retrofitting Effects on the Energy Flexibility of Dwellings Reprinted from: Energies 2019, 12, 2788, doi:10.3390/en12142788 ................... 25 Nikos Kampelis, Elisavet Tsekeri, Dionysia Kolokotsa, Kostas Kalaitzakis, Daniela Isidori and Cristina Cristalli Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions Reprinted from: Energies 2018, 11, 3012, doi:10.3390/en11113012 ................... 45 Martin Lieskovsky,´ Marek Trenˇciansky, Andrea Majlingov´aand J ´uliusJankovsky´ Energy Resources, Load Coverage of the Electricity System and Environmental Consequences of the Energy Sources Operation in the Slovak Republic—An Overview Reprinted from: Energies 2019, 12, 1701, doi:10.3390/en12091701 ................... 67 Komali Yenneti, Riya Rahiman, Adishree Panda and Gloria Pignatta Smart Energy Management Policy in India—A Review Reprinted from: Energies 2019, 12, 3214, doi:10.3390/en12173214 ................... 85 v About the Special Issue Editors Denia Kolokotsa is Associate Professor at the School of Environmental Engineering of the Technical University of Crete, Greece. Her research interests include energy management for the built environment, energy efficiency and renewables, integration of advanced energy technologies, indoor environmental quality, energy efficiency in buildings, cool materials, distributed energy management systems, artificial intelligence, building automation as well as optimisation of microgrids and smart grids. She is Editor of the Energy and Buildings journal of Elsevier and has served as Editor in Chief of Advances in Building Energy Research, Taylor and Francis. She is a member of the editorial board of 3 journals and Scientific Coordinator of 8 international research projects. She has participated in more than 30 international research projects as team leader or researcher. She is the author of 95 articles published in peer-reviewed journals. She is also President and Founding Member of the European Cool Roofs Council and member of numerous scientific committees and international organisations. Gloria Pignatta is an academic of the Faculty of Built Environment at the University of New South Wales (UNSW Sydney), Australia. Her research touches on the global problem of climate adaptation and mitigation by investigating the relationship between extreme weather events and the built environment. She is a Civil Engineer with a Ph.D. in Energy Engineering from the University of Perugia, Italy. In 2016, she was Postdoctoral Fellow at the Inter-University Research Center on Pollution and Environment “Mauro Felli” (CIRIAF), University of Perugia, Italy. In 2017, she was Postdoctoral Associate at Singapore-MIT Alliance for Research and Technology (SMART), in Singapore. She is the author of scientific papers in the field of building energy efficiency and sustainability, and has participated in 2 European and several international research projects as team leader or researcher. Kostas Gobakis is Research Associate at the School of Environmental Engineering of the Technical University of Crete, Greece. His research interests include prediction using artificial neural networks, energy simulation energy, management for the built environment, energy efficiency and renewables, energy efficiency in buildings, and cool materials. He is a physicist with a Ph.D. in Environmental Engineering from Technical University of Crete. He has served as the main technical coordinator of more than 5 European funded projects since 2012. vii Preface to ”Demand-Response in Smart Buildings” Smart buildings and communities are currently in the spotlight of the scientific community due mainly to their potential for creating a more sustainable and livable future through the application of smart energy management. Demand response (DR) offers the capability of applying changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of Demand response is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs, leading to reduced environmental impact. This Special Issue entitled “Demand-Response in Smart Buildings” was published in Energies, and includes 5 international scientific contributions comprising 3 research papers and 2 review papers. Studies on energy retrofitting, consumer to fog to cloud framework, genetic algorithm optimisation models, energy policy, and energy legislations are collected and presented in this book. We would like to express our gratitude to all the authors and reviewers who have significantly contributed to this Special Issue. A special thanks also goes to the editorial team of Energies (MDPI) for offering us the opportunity to publish this book, especially Mr. Mark Guo, Senior Assistant Editor of Energies. This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact. Denia Kolokotsa, Gloria Pignatta, Kostas Gobakis Special Issue Editors ix energies Article Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework† Sakeena Javaid 1, Nadeem Javaid 1,*, Tanzila Saba 2, Zahid Wadud 3, Amjad Rehman 4 and Abdul Haseeb 5 1 Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan; [email protected] 2 College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; [email protected] 3 Department of Computer System Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; [email protected] 4 College of Computer and Information Systems, Al Yamamah