Deep-learning-based demand response optimization and prediction model

Jianfeng Gong; Yi Qi; Wentao Xu; Wei Zheng; Jiaxin Zhang; Siyuan Chen; Hengrui Ma

doi:10.1117/12.3051186

27 September 2024 Deep-learning-based demand response optimization and prediction model

Jianfeng Gong, Yi Qi, Wentao Xu, Wei Zheng, Jiaxin Zhang, Siyuan Chen, Hengrui Ma

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328110 (2024) https://doi.org/10.1117/12.3051186
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

With the continuous increase in electricity demand, power grids face challenges such as peak load regulation, frequency adjustments, and power shortages. Demand-side response (DR) has emerged as an effective tool to balance electricity supply and demand by controlling user-side resources. This study proposes a deep learning-based prediction and optimization model to enhance the accuracy and effectiveness of DR. Using a convolutional neural network (CNN) and long short-term memory network (LSTM), we perform detailed load forecasting and extract resource parameters. Dynamic parameter identification enables online extraction and optimal management of these parameters. Experimental results demonstrate significant improvements in load forecasting accuracy and system reliability. Key contributions include integrating deep learning to model complex factors and resource characteristics, developing a CNN-LSTM-based load forecasting model, and creating a dynamic priority control algorithm for smart appliances based on the comfort index (KApp). This research supports stable power system operation and has significant theoretical and practical value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianfeng Gong, Yi Qi, Wentao Xu, Wei Zheng, Jiaxin Zhang, Siyuan Chen, and Hengrui Ma "Deep-learning-based demand response optimization and prediction model", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328110 (27 September 2024); https://doi.org/10.1117/12.3051186

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