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Probabilistic Long-term Load Forecasting Based on Stacked LSTM

Authors:

Chunming Yuan,

Tian Xiu,

Tianyue LouAuthors Info & Claims

ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence

Pages 80 - 84

https://doi.org/10.1145/3325730.3325734

Published: 12 April 2019 Publication History

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Abstract

The accurate load forecasting is of great significance to power companies. In this paper, we proposed a probabilistic long-term load forecasting model based on stacked Long Short-Term Memory (LSTM).The data set is the load data of a power plant from 2005 to 2013.Firstly, data preprocessing is aimed to eliminate outliers and missing values, it can improve the accuracy of prediction. Secondly, we obtained the point prediction value with stacked LSTM, and the result shows that the proposed model performs better on prediction accuracy than other models, such as Support Vector Regression and Artificial neural network (BP). Finally, we proposed a probability density prediction method based on error statistics, comparing with point prediction method, it can provide more uncertain information for long-term load forecasting.

References

[1]

P Mukhopadhyay, G. Mitra, S. Banerjee, G. Mukherjee. 2017. Electricity Load Forecasting Using Fuzzy Logic Short Term Load Forecasting Factoring Weather Parameter. International Conference on Power Systems. IEEE.

Google Scholar

[2]

Yi Wang, Qixin Chen. 2018. Conditional Residual Modeling for Probabilistic Load Forecasting. TRANSACTIONS ON POWER SYSTEMS.IEEE.

Google Scholar

[3]

Ren ZhiChao, Ye Qiang. 2017. Power Load Forecasting in the Spring Festival Based on Feedforward Neural Network Model. International Conference on Computer and Communications IEEE.

Google Scholar

[4]

Rong Gao, Liyuan Zhang. 2012. Short-term Load Forecasting Based on Least Square Support Vector Machine Combined with Fuzzy Control. World Congress on Intelligent Control and Automation IEEE.

Google Scholar

[5]

Xuan Wenbo, Song Jia. 2017. The Model Combination Method of Power System Load Forecasting Based on Freshness Availability Index. International Conference on Power and Renewable Energy.IEEE.

Google Scholar

[6]

Peng Xiuyan, Zhang Biao, Cui Yanqing. 2015. The Short-term Load Forecasting of Electric Power System Based on Combination Forecast Model. Chinese Control and Decision Conference. IEEE.

Google Scholar

[7]

Panfeng Chen; Haozhong Cheng. 2018. Research on Medium-Long Term Power Load Forecasting Method Based on Load Decomposition and Big Data Technology. International Conference on Smart Grid and Electrical Automation.IEEE.

Google Scholar

[8]

Melodi A. O, Momoh J. A 2016. Probabilistic long term load forecast for Nigerian bulk power transmission system expansion planning.PES Power Africa Conference. IEEE.

Google Scholar

[9]

Eman Khorsheed. 2018. Long-term Energy Peak Load Forecasting Models: A Hybrid Statistical Approach. Advances in Science and Engineering Technology International Conferences.IEEE.

Google Scholar

[10]

Sreenu Sreekumar; Jatin Verma. 2016. Matrix based univariate and multivariate linear approach towards Long Term electrical Load Forecasting.International Conference on Power Electronics, Intelligent Control and Energy Systems. IEEE.

Google Scholar

[11]

Songlin Zhou. 2011.Short-term Forecasting of Wind Power and Non-parametric Confidence Interval Estimation.

Google Scholar

Cited By

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Zhang MFan ZMiao YYang L(2023)Cloud computing resource load prediction based on improved VMD and attention mechanismJournal of Physics: Conference Series10.1088/1742-6596/2589/1/0120222589:1(012022)Online publication date: 1-Sep-2023
https://doi.org/10.1088/1742-6596/2589/1/012022

Index Terms

Probabilistic Long-term Load Forecasting Based on Stacked LSTM
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence

April 2019

232 pages

ISBN:9781450362580

DOI:10.1145/3325730

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University
Xihua University: Xihua University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2019

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ICMAI 2019

ICMAI 2019: 2019 4th International Conference on Mathematics and Artificial Intelligence

April 12 - 15, 2019

Chegndu, China

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Zhang MFan ZMiao YYang L(2023)Cloud computing resource load prediction based on improved VMD and attention mechanismJournal of Physics: Conference Series10.1088/1742-6596/2589/1/0120222589:1(012022)Online publication date: 1-Sep-2023
https://doi.org/10.1088/1742-6596/2589/1/012022

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Recommendations

Power Load Forecasting Using a Refined LSTM

Neural networks for pattern-based short-term load forecasting

Conventional regression versus artificial neural network in short-term load forecasting