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Development and comparison of two computational intelligence algorithms for electrical load forecasts with multiple time scales
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Liu, Jinqi, Zhang, Sizhe and Wang, Jihong (2022) Development and comparison of two computational intelligence algorithms for electrical load forecasts with multiple time scales. In: 2022 Power System and Green Energy Conference (PSGEC), Shanghai, China, 25-27 Aug 2022. Published in: 2022 Power System and Green Energy Conference (PSGEC) pp. 637-643. ISBN 9781665499941. doi:10.1109/PSGEC54663.2022.9881169
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WRAP-Development-comparison-two-computational-intelligence-algorithms-electrical-multiple-time-scales-22.pdf - Accepted Version - Requires a PDF viewer. Download (1282Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/PSGEC54663.2022.9881169
Abstract
Electricity load forecasting provides the critical information required for power institutions and authorities to develop rational, effective, and economic dispatch plans. The load forecasting at the regional power system is important for optimal management and accommodating local renewable energy sources, which is a challenging task as the demand variations are more sensitive to local weather changes (such as temperature, humidity, precipitation, and wind speed) and consumers' activities and behaviours. The paper aims to develop a new prediction method using intelligent computational algorithms. Long Short-Term Memory (LSTM), a deep recurrent neural network, explores the long-term dependency of network memory sequence data to identify intrinsic variations in both horizontals (time series) and vertical (network depth) dimensions over a longer historical period. Support Vector Machine (SVM) is a typical learning method that has been successfully implemented to solve nonlinear regression and time series problems. This paper studies the two methods and adapts the two methods to become suitable algorithms for load prediction. The paper presents the algorithms, their applications and prediction results. The prediction performance is compared for using LSTM and SVM at ultra-short, short-term, medium-term, and long-term forecasting. The results show that LSTM has higher prediction accuracy than SVM in both ultra-short and short-term forecasts, but SVM is more capable of medium-term and long-term forecasting. Finally, the epoch time for LSTM and SVM is also calculated and compared.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Computer algorithms, Neural networks (Computer science) , Support vector machines, Electric power-plants -- Load -- Mathematical models, Electric power-plants -- Load | ||||||
Journal or Publication Title: | 2022 Power System and Green Energy Conference (PSGEC) | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781665499941 | ||||||
Book Title: | 2022 Power System and Green Energy Conference (PSGEC) | ||||||
Official Date: | 20 September 2022 | ||||||
Dates: |
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Page Range: | pp. 637-643 | ||||||
DOI: | 10.1109/PSGEC54663.2022.9881169 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | IEEE | ||||||
Date of first compliant deposit: | 3 November 2022 | ||||||
Date of first compliant Open Access: | 3 November 2022 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 2022 Power System and Green Energy Conference (PSGEC) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Shanghai, China | ||||||
Date(s) of Event: | 25-27 Aug 2022 | ||||||
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