Cited by 62Energy Load and Power ForecastingBuilding Energy and Comfort OptimizationSmart Grid Energy Management

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Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

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Natarajan, Yuvaraj, K. R., Sri Preethaa, Wadhwa, Gitanjali, Choi, Young, Chen, Zengshun, Lee, Dong-Eun, Mi, Yirong·2024·Sustainability

DOI 10.3390/su16051925

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Abstract

Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

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Authors: Natarajan, Y., K. R., S. P., Wadhwa, G., Choi, Y., Chen, Z., Lee, D.-E., & Mi, Y.
Affiliations: Kyungpook National University (KR)
Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Uk-gu, Daegu 41566, Republic of Korea
Center for Scalable Data Analytics and Artificial Intelligence (DE)
Earth Turbine, 36, Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Republic of Korea
Chongqing University (CN)
Corresponding author: Lee, Dong-Eun
Mi, Yirong
ORCID: Natarajan, Yuvaraj
K. R., Sri Preethaa
Chen, Zengshun
Lee, Dong-Eun
Mi, Yirong
Venue: Sustainability
Publisher: MDPI AG
Published: 2024-02-26
Volume: 16
Issue: 5
Pages: 1925
ISSN: 2071-1050 (ISSN-L)
DOI: 10.3390/su16051925
OpenAlex: W4392166773
Cited by: 61
References: 43
Citations by year: 2026: 23 · 2025: 31 · 2024: 7
FWCI: 6.33
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sustainability (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Research Foundation of Korea (NRF) (2022R1F1A1068374, NRF-2018R1A5A1025137, NRF-2022H1D3A2A02082296)
Ministry of Science and ICT through the National Research Foundation of Korea (2022R1F1A1068374, NRF-2018R1A5A1025137, NRF-2022H1D3A2A02082296)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Keywords: building energy efficiency, load forecasting, Internet of Things, smart meters, hybrid deep learning model, convolutional neural networks, long short-term memory, household energy consumption, commercial buildings, energy management, home automation

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Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

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