DOI:10.35833/MPCE.2020.000321 Building Load Forecasting Using Deep Neural Network with Efficient Feature Fusion Page view: 0        Net amount: 104 Author: Jinsong Wang1,Xuhui Chen2,Fan Zhang1,Fangxi Chen3,Yi Xin3 Author Affiliation: 1.Department of Electrical, Computer, and System Engineering at Case Western Reserve University, Cleveland, USA;2.College of Aeronautics and Engineering, Kent State University, Kent, USA;3.Software College, Northeastern University, Shenyang, China Foundation: Abstract: 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. Keywords: Load forecasting ; deep learning ; convolutional neural network ; feature fusion ; ResNet. Received:May 21, 2020               Online Time:2021/01/22 View Full Text       Download reader