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DC Field | Value | Language |
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dc.contributor.author | Chen, R | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Zhong, C | - |
dc.contributor.author | Pan, K | - |
dc.contributor.author | Ng, WWY | - |
dc.contributor.author | Li, Z | - |
dc.contributor.author | Lai, LL | - |
dc.date.accessioned | 2021-11-09T11:29:33Z | - |
dc.date.available | 2021-11-09T11:29:33Z | - |
dc.date.issued | 2021-11-04 | - |
dc.identifier | ORCID iD: Chun Sing Lai chunsing.lai@brunel.ac.uk | - |
dc.identifier | 103484 | - |
dc.identifier.citation | Chen, R. et al. (2021) 'MultiCycleNet: Multiple Cycles Self-Boosted Neural Network for Short-term Electric Household Load Forecasting', Sustainable Cities and Society, 76, 103484, pp. 1 - 13.,doi: 10.1016/j.scs.2021.103484. | en_US |
dc.identifier.issn | 2210-6707 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23470 | - |
dc.description.abstract | Household load forecasting plays an important role in future grid planning and operation. However, compared with aggregated load forecasting, household load forecasting faces the challenge of the uncertainty of prolific load profiles. This paper presents a novel multiple cycles self-boosted neural network (MultiCycleNet) framework for household load forecasting, which aims to solve the uncertainty problem of household load profiles through the correlation analysis of electricity consumption patterns in multiple cycles. The basic idea of the proposed framework is that the predictor can learn customers’ power consumption patterns better by learning the features and contextual information of relevant load profiles in multiple historical cycles. We use two real-life datasets: 1. the household load consumption dataset from Low Carbon London project led by United Kingdom (UK) Power Networks and 2. the UK Domestic Appliance-Level Electricity (UK-DALE) dataset to evaluate the effectiveness of the proposed framework. Compared with the state-of-the-art methods, experimental results show that the proposed framework is effective and outperforms the state-of-the-art methods by 19.83%, 10.46%, 11.14% and 9.02% in terms of mean squared error, root mean squared error, mean absolute error and mean absolute percent error, respectively. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China under Grants 61876066 and 61572201, Guangzhou Science and Technology Plan Project 201804010245, Department of Finance and Education of Guangdong Province 2016 [202]: Key Discipline Construction Program, China; the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; Brunel University London BRIEF Funding, UK. | en_US |
dc.format.extent | 1 - 13 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2021 Elsevier. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.scs.2021.103484, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | load forecasting | en_US |
dc.subject | recurrent neural network | en_US |
dc.subject | time-series forecasting | en_US |
dc.subject | multiple historical cycles | en_US |
dc.title | MultiCycleNet: Multiple Cycles Self-Boosted Neural Network for Short-term Electric Household Load Forecasting | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 21 October 2021 | - |
dc.identifier.doi | https://doi.org/10.1016/j.scs.2021.103484 | - |
dc.relation.isPartOf | Sustainable Cities and Society | - |
pubs.publication-status | Published | - |
pubs.volume | 76 | - |
dc.identifier.eissn | 2210-6715 | - |
dc.rights.holder | Elsevier | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2021 Elsevier. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.scs.2021.103484, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 4.06 MB | Adobe PDF | View/Open |
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