The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction
Open accessAbstract
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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“It expresses average deviations as a percentage of observed values, which aids interpretability for stakeholders less comfortable with unit-specific metrics [54].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [54]
In the citing paper
“In large-scale CNN-BiLSTM energy consumption forecasting studies, combined evaluation using RMSE, MAE, MAPE, and R 2 confirms alignment between predicted curves and real usage profiles under diverse temporal conditions [54].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [54]
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Metadata
- 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
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2024-02-26
- Volume
- 16
- Issue
- 5
- Pages
- 1925
- ISSN
- 2071-1050 (ISSN-L)
- 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
- 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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