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Forecasting of Energy Demands for Smart Home Applications

Author

Listed:
  • Dhowmya Bhatt

    (Faculty of Information Technology, Delhi-NCR Campus, SRM Institute of Science and Technology, Delhi-Meerut Road, Modinagar, Ghaziabad 201204, India)

  • Danalakshmi D

    (Faculty of Electrical and Electronics Engineering, GMR Institute of Technology, GMR Nagar, Rajam 532127, Andhra Pradesh, India)

  • A. Hariharasudan

    (Faculty of English, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil 626126, India)

  • Marcin Lis

    (Faculty of Applied Sciences, WSB University in Dabrowa Górnicza, Zygmunta Cieplaka 1c, 41-300 Dąbrowa Górnicza, Poland)

  • Marlena Grabowska

    (The Faculty of Management, Czestochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, Poland)

Abstract

The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

Suggested Citation

  • Dhowmya Bhatt & Danalakshmi D & A. Hariharasudan & Marcin Lis & Marlena Grabowska, 2021. "Forecasting of Energy Demands for Smart Home Applications," Energies, MDPI, vol. 14(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1045-:d:500722
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    References listed on IDEAS

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    Cited by:

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    2. Daniela Durand & Jose Aguilar & Maria D. R-Moreno, 2022. "An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    3. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Ismail Aouichak & Sébastien Jacques & Sébastien Bissey & Cédric Reymond & Téo Besson & Jean-Charles Le Bunetel, 2022. "A Bidirectional Grid-Connected DC–AC Converter for Autonomous and Intelligent Electricity Storage in the Residential Sector," Energies, MDPI, vol. 15(3), pages 1-19, February.
    5. Wang, Junke & Jiang, Yilin & Tang, Choon Yik & Song, Li, 2022. "Development and validation of a second-order thermal network model for residential buildings," Applied Energy, Elsevier, vol. 306(PB).

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