research-article

Variation Characteristics Analysis and Short-Term Forecasting of Load Based on CEEMDAN

Authors:

Min WangAuthors Info & Claims

ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering

Pages 493 - 500

https://doi.org/10.1145/3459104.3459185

Published: 20 July 2021 Publication History

Abstract

With the development of the economy and the improvement of living standards of people, electricity consumption around the world has increased dramatically. However, the load that contains many components with different characteristics is affected by many factors. How to classify and extract load characteristics and improve the accuracy of load forecasting has become a focus of attention. Based on this, this paper proposes using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the load and get multiple components of different time scales. The hidden characteristics of each components of load are analyzed with the four characteristic indicators. Then, the Pearson coefficient is used to analyze the multi-scale correlation between the load components and the temperature, and at the same time decompose the temperature to dig deep relationship between the load components and the temperature components. Finally, we use the Least Squares Support Vector Machine Optimized by Particle Swarm Optimization (PSO-LSSVM) to forecasting each component of the load, and select the decomposed temperature as part of the input data of the load forecasting model. The forecasting results verify the advantages of the proposed method in the aspects of load characteristic analysis and improvement of load forecasting accuracy.

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Cited By

Zhang QWang QLyu WYu C(2024)DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber SensingSensors10.3390/s2409267224:9(2672)Online publication date: 23-Apr-2024
https://doi.org/10.3390/s24092672
Lv RWu ELan LFu CGuo MChen FWang MZou J(2024)Research on Virtual Energy Storage Scheduling Strategy for Air Conditioning Based on Adaptive Thermal Comfort ModelEnergies10.3390/en1711267017:11(2670)Online publication date: 30-May-2024
https://doi.org/10.3390/en17112670
Zhang QLyu WWang QYu C(2023)A Deep Learning-Based Model for Human Non-Invasive Vital Sign Signal Monitoring with Optical Fiber Sensor2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM)10.1109/ACP/POEM59049.2023.10369647(1-4)Online publication date: 4-Nov-2023
https://doi.org/10.1109/ACP/POEM59049.2023.10369647

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ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering

February 2021

644 pages

ISBN:9781450389839

DOI:10.1145/3459104

Copyright © 2021 ACM.

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Publication History

Published: 20 July 2021

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ISEEIE 2021

ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering

February 19 - 21, 2021

Seoul, Republic of Korea

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Cited By

Zhang QWang QLyu WYu C(2024)DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber SensingSensors10.3390/s2409267224:9(2672)Online publication date: 23-Apr-2024
https://doi.org/10.3390/s24092672
Lv RWu ELan LFu CGuo MChen FWang MZou J(2024)Research on Virtual Energy Storage Scheduling Strategy for Air Conditioning Based on Adaptive Thermal Comfort ModelEnergies10.3390/en1711267017:11(2670)Online publication date: 30-May-2024
https://doi.org/10.3390/en17112670
Zhang QLyu WWang QYu C(2023)A Deep Learning-Based Model for Human Non-Invasive Vital Sign Signal Monitoring with Optical Fiber Sensor2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM)10.1109/ACP/POEM59049.2023.10369647(1-4)Online publication date: 4-Nov-2023
https://doi.org/10.1109/ACP/POEM59049.2023.10369647

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