IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2054-d531949.html
   My bibliography  Save this article

Energy Efficiency in Smart Homes and Smart Grids

Author

Listed:
  • Anna Fensel

    (Semantic Technology Institute (STI) Innsbruck, Department of Computer Science, University of Innsbruck, Technikerstr. 21a, 6020 Innsbruck, Austria)

  • Juan Miguel Gómez Berbís

    (Software Architect Group, Department of Computer Science, Carlos III University of Madrid, Av. de la Universidad 30, Leganés, 28911 Madrid, Spain)

Abstract

Here, we overview the Energies journal special issue that is dedicated to the topic of “Energy Efficiency in Smart Homes and Smart Grids” (https://www [...]

Suggested Citation

  • Anna Fensel & Juan Miguel Gómez Berbís, 2021. "Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(8), pages 1-2, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2054-:d:531949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2054/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2054/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghulam Hafeez & Khurram Saleem Alimgeer & Zahid Wadud & Zeeshan Shafiq & Mohammad Usman Ali Khan & Imran Khan & Farrukh Aslam Khan & Abdelouahid Derhab, 2020. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid," Energies, MDPI, vol. 13(9), pages 1-25, May.
    2. Alessandro Cannavale & Ubaldo Ayr & Francesco Fiorito & Francesco Martellotta, 2020. "Smart Electrochromic Windows to Enhance Building Energy Efficiency and Visual Comfort," Energies, MDPI, vol. 13(6), pages 1-17, March.
    3. Ah-Yun Yoon & Hyun-Koo Kang & Seung-II Moon, 2020. "Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction," Energies, MDPI, vol. 13(4), pages 1-20, February.
    4. Olga Orynycz & Karol Tucki, 2020. "Technology Management Leading to a Smart System Solution Assuring a Decrease of Energy Consumption in Recreational Facilities," Energies, MDPI, vol. 13(13), pages 1-22, July.
    5. Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lisardo Prieto González & Anna Fensel & Juan Miguel Gómez Berbís & Angela Popa & Antonio de Amescua Seco, 2021. "A Survey on Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(21), pages 1-16, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    2. Josimar Reyes-Campos & Giner Alor-Hernández & Isaac Machorro-Cano & José Oscar Olmedo-Aguirre & José Luis Sánchez-Cervantes & Lisbeth Rodríguez-Mazahua, 2021. "Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm," Mathematics, MDPI, vol. 9(3), pages 1-25, January.
    3. Syrrokostas, George & Leftheriotis, George & Yannopoulos, Spyros N., 2022. "Lessons learned from 25 years of development of photoelectrochromic devices: A technical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Rosaria E.C. Amaral & Joel Brito & Matt Buckman & Elicia Drake & Esther Ilatova & Paige Rice & Carlos Sabbagh & Sergei Voronkin & Yewande S. Abraham, 2020. "Waste Management and Operational Energy for Sustainable Buildings: A Review," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    5. Sajawal ur Rehman Khan & Israa Adil Hayder & Muhammad Asif Habib & Mudassar Ahmad & Syed Muhammad Mohsin & Farrukh Aslam Khan & Kainat Mustafa, 2022. "Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids," Energies, MDPI, vol. 16(1), pages 1-16, December.
    6. Chiang, Yu-Jou & Chang, Ling-Yu & Cheng, Chao-Yuan & Chang, Ching-Cheng & Yeh, Chia-Lin & Huang, Chen-Jui & Jiang, Shi-Kai & Ho, Kuo-Chuan & Hwang, Bing-Joe & Yeh, Min-Hsin, 2022. "Designing highly transparent electropolymerized PANI/rGO nanocomposite as a Pt-free electrocatalytic layer in photoelectrochromic device for self-powered green building," Renewable Energy, Elsevier, vol. 199(C), pages 103-111.
    7. Liu, Lucy & Workman, Mark & Hayes, Sarah, 2022. "Net Zero and the potential of consumer data - United Kingdom energy sector case study: The need for cross-sectoral best data practice principles," Energy Policy, Elsevier, vol. 163(C).
    8. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.
    9. Ghasem Ansari & Reza Keypour, 2023. "Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory," Sustainability, MDPI, vol. 15(7), pages 1-31, April.
    10. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
    11. Shamim Akhtar & Muhamad Zahim Bin Sujod & Syed Sajjad Hussain Rizvi, 2022. "An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
    12. Aneta Sapińska-Śliwa & Tomasz Sliwa & Kazimierz Twardowski & Krzysztof Szymski & Andrzej Gonet & Paweł Żuk, 2020. "Method of Averaging the Effective Thermal Conductivity Based on Thermal Response Tests of Borehole Heat Exchangers," Energies, MDPI, vol. 13(14), pages 1-20, July.
    13. Elissaios Sarmas & Vangelis Marinakis & Haris Doukas, 2022. "A data-driven multicriteria decision making tool for assessing investments in energy efficiency," Operational Research, Springer, vol. 22(5), pages 5597-5616, November.
    14. Sabina Kordana-Obuch & Mariusz Starzec & Daniel Słyś, 2021. "Assessment of the Feasibility of Implementing Shower Heat Exchangers in Residential Buildings Based on Users’ Energy Saving Preferences," Energies, MDPI, vol. 14(17), pages 1-30, September.
    15. Jae-Hyang Kim & Jongin Hong & Seung-Hoon Han, 2021. "Optimized Physical Properties of Electrochromic Smart Windows to Reduce Cooling and Heating Loads of Office Buildings," Sustainability, MDPI, vol. 13(4), pages 1-30, February.
    16. Zishan Guo & Zhenya Ji & Qi Wang, 2020. "Blockchain-Enabled Demand Response Scheme with Individualized Incentive Pricing Mode," Energies, MDPI, vol. 13(19), pages 1-17, October.
    17. Neilson Luniere Vilaça & Marly Guimarães Fernandes Costa & Cicero Ferreira Fernandes Costa Filho, 2023. "A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm," Energies, MDPI, vol. 16(8), pages 1-14, April.
    18. Lantonio, Nicole A. & Krarti, Moncef, 2022. "Simultaneous design and control optimization of smart glazed windows," Applied Energy, Elsevier, vol. 328(C).
    19. Wulfran Fendzi Mbasso & Reagan Jean Jacques Molu & Serge Raoul Dzonde Naoussi & Saatong Kenfack, 2022. "Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 99-103, July.
    20. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.

    More about this item

    Keywords

    n/a;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2054-:d:531949. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.