Day-ahead holiday load forecast based on pattern sequence similarity and random forest-Reference-Cited by-同舟云学术

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Day-ahead holiday load forecast based on pattern sequence similarity and random forest

Published:2022-07-29 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS)
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Author:

Zhu Kedong¹,Li Yaping¹,Guo Xiaorui¹,Liu Jiantao¹,Wang Gang¹

Affiliation:

1. China Electric Power Research Institute,Power Automation Department,Nanjing,China

Funder

Natural Science Foundation of Jiangsu Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9872525/9873533/09873588.pdf?arnumber=9873588

Reference12 articles.

1. Deep belief network based electricity load forecasting: An analysis of Macedonian case

2. Short-Term Load Forecasting Based on a Semi-Parametric Additive Model

3. Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods

4. Next Day Load Curve Forecasting Using Hybrid Correction Method

5. Holiday short-term load forecasting based on fractal characteristic modified meteorological similar day;li;Power System Technology,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Short-term load forecasting for holidays based on similar days selecting and XGBoost model;2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS);2023-05-08

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