期刊
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
卷 9, 期 1, 页码 160-169出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.35833/MPCE.2020.000321
关键词
Load modeling; Forecasting; Load forecasting; Buildings; Predictive models; Feature extraction; Energy consumption; Load forecasting; deep learning; convolutional neural network; feature fusion; ResNet
The energy consumption of buildings is rising steadily, and deep learning techniques offer advanced forecasting capabilities to manage electricity demand efficiently.
The energy consumption of buildings has risen steadily in recent years. It is vital for the managers and owners of the building to manage the electric energy demand of the buildings. Forecasting electric energy consumption of the buildings will bring great profits, which is influenced by many factors that make it very difficult to provide an advanced forecasting. Recently, deep learning techniques are widely adopted to solve this problem. Deep neural network offers an excellent capability in handling complex non-linear relationships and competence in exploring regular patterns and uncertainties of consumption behaviors at the building level. In this paper, we propose a deep convolutional neural network based on ResNet for hour-ahead building load forecasting. In addition, we design a branch that integrates the temperature per hour into the forecasting branch. To enhance the learning capability of the model, an innovative feature fusion is presented. At last, sufficient ablation studies are conducted on the point forecasting, probabilistic forecasting, fusion method, and computation efficiency. The results show that the proposed model has the state-of-the-art performance, which reflects a promising prospect in application of the electricity market.
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