%0 Journal Article %1 natarajan2024enhancing %A Natarajan, Yuvaraj %A K. R., Sri Preethaa %A Wadhwa, Gitanjali %A Choi, Young %A Chen, Zengshun %A Lee, Dong-Eun %A Mi, Yirong %D 2024 %J Sustainability %K imported %N 5 %R 10.3390/su16051925 %T Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction %U https://www.mdpi.com/2071-1050/16/5/1925 %V 16 %X 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.

@article{natarajan2024enhancing, 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.}, added-at = {2024-12-11T10:47:14.000+0100}, article-number = {1925}, author = {Natarajan, Yuvaraj and K. R., Sri Preethaa and Wadhwa, Gitanjali and Choi, Young and Chen, Zengshun and Lee, Dong-Eun and Mi, Yirong}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/21490eaf1735b7319860fb468172d12aa/scadsfct}, doi = {10.3390/su16051925}, interhash = {68666ed60b5c9cb1b6b86e17049ba4d0}, intrahash = {1490eaf1735b7319860fb468172d12aa}, issn = {2071-1050}, journal = {Sustainability}, keywords = {imported}, number = 5, timestamp = {2024-12-11T10:47:14.000+0100}, title = {Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction}, url = {https://www.mdpi.com/2071-1050/16/5/1925}, volume = 16, year = 2024 }