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Title: Deep learning based densely connected network for load forecasting
Authors: Li, Zhuoling
Li, Yuanzheng
Liu, Yun
Wang, Ping
Lu, Renzhi
Gooi, Hoay Beng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Li, Z., Li, Y., Liu, Y., Wang, P., Lu, R. & Gooi, H. B. (2021). Deep learning based densely connected network for load forecasting. IEEE Transactions On Power Systems, 36(4), 2829-2840.
Journal: IEEE Transactions on Power Systems
Abstract: Load forecasting is of crucial importance for operations of electric power systems. In recent years, deep learning based methods are emerging for load forecasting because their strong nonlinear approximation capabilities can provide more forecasting precision than conventional statistical methods. However, they usually suffer from some problems, e.g., the gradient vanishment and over-fitting. In order to address these problems, an unshared convolution based deep learning model with densely connected network is proposed. In this model, the backbone is the unshared convolutional neural network and a densely connected structure is adopted, which could alleviate the gradient vanishment. What is more, we use a regularization method named clipped $L_2$-norm to overcome over-fitting, and design a trend decomposition strategy to address the possible distribution differences between the training and validation data. Finally, we conduct five case studies to verify the outperformance of our proposed deep learning model for deterministic and interval load forecasting. Two high-voltage and an medium-voltage real load datasets from Australia, Germany and America are used for model training and validation, respectively. Results show that the proposed model can achieve higher load forecasting accuracy, compared with other existing methods including the popular conventional methods such as naive forecast and generalized additive model, and deep learning methods, e.g., long short-term memory network, convolutional neural network, fully connected network, etc.
ISSN: 0885-8950
DOI: 10.1109/TPWRS.2020.3048359
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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