Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning

Xiaowan Zeng, Liangbin Dong, Xiaoxi Fu, Jingyi Xie, Xinyan Wang, Jianing Liu

2025

Abstract

At present, the power demand fluctuates greatly, and the demand analysis and processing is relatively complex, and the real-time forecast demand is high, and these problems need to be solved. The purpose of this paper is to study the analysis and demand forecasting of power marketing metering demand analysis and demand forecasting based on deep learning, so as to solve the problems of inaccurate power demand forecasting and low operational efficiency. In this paper, the initial research is carried out through the design of system power marketing and related steps. Subsequently, the system adopts the hybrid structure of CNN and LSTM, two deep learning algorithms, combined with microservice architecture technology, to achieve efficient integration and deployment of the system. After the completion of the system, in order to verify the effectiveness, stability and prediction accuracy of the system, this paper also applies the system in practice. The results show that the system has multiple advantages, such as high accuracy, real-time, and effective decision support. The research in this paper will provide a guarantee for the power price strategy formulation and power dispatching optimization of power companies, ensure the high utilization efficiency of power resources, and maintain the stable operation of the power grid. At the same time, the research in this paper will also lay a good foundation for the further development and construction of smart grids in the future.

Paper Citation

in Harvard Style

Zeng X., Dong L., Fu X., Xie J., Wang X. and Liu J. (2025). Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 548-554. DOI: 10.5220/0013550800004664

in Bibtex Style

@conference{incoft25,
author={Xiaowan Zeng and Liangbin Dong and Xiaoxi Fu and Jingyi Xie and Xinyan Wang and Jianing Liu},
title={Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={548-554},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013550800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning
SN - 978-989-758-763-4
AU - Zeng X.
AU - Dong L.
AU - Fu X.
AU - Xie J.
AU - Wang X.
AU - Liu J.
PY - 2025
SP - 548
EP - 554
DO - 10.5220/0013550800004664
PB - SciTePress