Predicting short-term energy usage in a smart home using hybrid deep learning models

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS Frontiers in Energy Research Pub Date : 2024-09-05 DOI:10.3389/fenrg.2024.1323357
Imane Hammou Ou Ali, Ali Agga, Mohammed Ouassaid, Mohamed Maaroufi, Ali Elrashidi, Hossam Kotb
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Abstract

The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption forecasts for a smart home. Two deep learning models are implemented: ConvLSTM, which combines convolutional operations with Long Short-Term Memory (LSTM), and the CNN-LSTM model, which synergizes Convolutional Neural Networks (CNN) and LSTM networks. Both hybrid models offer a comprehensive approach to modeling complex relationships in spatial and temporal patterns. Additionally, two baseline models—LSTM and CNN—are employed for comparative analysis. Utilizing real data from a smart home in Houston, Texas, the results demonstrate that both the hybrid models deliver highly accurate predictions for energy consumption. However, the ConvLSTM model outperforms all proposed models, improving predictions in terms of mean absolute percentage error by 4.52%, 9.59%, and 10.53% for 1 day, 3 days, and 6 days in advance, respectively.
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利用混合深度学习模型预测智能家居的短期能源使用情况
家庭能源消耗预测是智能电网(SG)中人工智能(AI)增强型能源管理领域的一个重要而又具有挑战性的课题。本研究的主要目标是为智能家居提供准确的能耗预测。本研究采用了两种深度学习模型:ConvLSTM 结合了卷积操作和长短期记忆(LSTM),而 CNN-LSTM 模型则协同了卷积神经网络(CNN)和 LSTM 网络。这两种混合模型都为空间和时间模式中的复杂关系建模提供了一种全面的方法。此外,还采用了 LSTM 和 CNN 两种基线模型进行比较分析。利用德克萨斯州休斯顿一个智能家居的真实数据,结果表明这两种混合模型都能提供高度准确的能耗预测。不过,ConvLSTM 模型的表现优于所有建议的模型,提前 1 天、3 天和 6 天预测的平均绝对百分比误差分别提高了 4.52%、9.59% 和 10.53%。
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来源期刊
Frontiers in Energy Research
Frontiers in Energy Research Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
11.80%
发文量
1727
审稿时长
12 weeks
期刊介绍: Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria
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