research-article

Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland

Authors:

Qingguo ZhouAuthors Info & Claims

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 47, Pages 1 - 7

https://doi.org/10.1145/3014812.3014861

Published: 31 January 2017 Publication History

Abstract

With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland's electricity demand forecasting.

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https://doi.org/10.4018/979-8-3693-4107-0.ch015
Atanane ABenabbou LEl Ouafi A(2023)Electricity demand Forecasting: A systematic literature review2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)10.1109/SITA60746.2023.10373741(1-8)Online publication date: 22-Nov-2023
https://doi.org/10.1109/SITA60746.2023.10373741
Ghimire SDeo RCasillas-Pérez DSalcedo-Sanz S(2023)Efficient daily electricity demand prediction with hybrid deep-learning multi-algorithm approachEnergy Conversion and Management10.1016/j.enconman.2023.117707297(117707)Online publication date: Dec-2023
https://doi.org/10.1016/j.enconman.2023.117707
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Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland
1. Computing methodologies
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cover image ACM Other conferences

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

615 pages

ISBN:9781450347686

DOI:10.1145/3014812

Copyright © 2017 ACM.

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Published: 31 January 2017

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ACSW 2017

ACSW 2017: Australasian Computer Science Week 2017

January 30 - February 3, 2017

Geelong, Australia

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ACSW '17 Paper Acceptance Rate 78 of 156 submissions, 50%;

Overall Acceptance Rate 204 of 424 submissions, 48%

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Cited By

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https://doi.org/10.4018/979-8-3693-4107-0.ch015
Atanane ABenabbou LEl Ouafi A(2023)Electricity demand Forecasting: A systematic literature review2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)10.1109/SITA60746.2023.10373741(1-8)Online publication date: 22-Nov-2023
https://doi.org/10.1109/SITA60746.2023.10373741
Ghimire SDeo RCasillas-Pérez DSalcedo-Sanz S(2023)Efficient daily electricity demand prediction with hybrid deep-learning multi-algorithm approachEnergy Conversion and Management10.1016/j.enconman.2023.117707297(117707)Online publication date: Dec-2023
https://doi.org/10.1016/j.enconman.2023.117707
Teng H(2021)Improved grey‐level correlation feature and neural network model for medical resource requirement predictionExpert Systems10.1111/exsy.1292739:6Online publication date: 16-Dec-2021
https://doi.org/10.1111/exsy.12927
Qian WGu CZhu CJiang ZHan BYu M(2021)Short-term Load Forecasting Based on Multi-model Fusion of CNN-LSTM-LGBM2021 International Conference on Power System Technology (POWERCON)10.1109/POWERCON53785.2021.9697619(934-939)Online publication date: 8-Dec-2021
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Lyu BWang NChen J(2021)Monte Carlo Modeling of Employment Distribution of College Graduates based on Data Mining Technology2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC51422.2021.9532975(1537-1540)Online publication date: 4-Aug-2021
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Abdulrahman MIbrahim KGital AZambuk FJa’afaru BYakubu ZIbrahim A(2021)A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential BuildingProcedia Computer Science10.1016/j.procs.2021.10.014193(141-154)Online publication date: 2021
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Feng FLi KShen JZhou QYang X(2020)Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced ClassificationIEEE Access10.1109/ACCESS.2020.29873648(69979-69996)Online publication date: 2020
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Yong BWei YShen JLi FJiang XZhou Q(2020)Combined General Vector Machine for Single Point Electricity Load ForecastFrontier Computing10.1007/978-981-15-3250-4_33(283-291)Online publication date: 26-Feb-2020
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Yong BShen ZWei YShen JZhou Q(2020)Short-Term Electricity Demand Forecasting Based on Multiple LSTMsAdvances in Brain Inspired Cognitive Systems10.1007/978-3-030-39431-8_18(192-200)Online publication date: 1-Feb-2020
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