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SEA: A Combined Model for Heat Demand Prediction
Beijing University of Posts and Telecommunications, Beijing, China.
Beijing University of Posts and Telecommunications, Beijing, China.
Beijing University of Posts and Telecommunications, Beijing, China.
University College London, London, United Kingdom.
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2018 (English)In: Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 71-75Conference paper, Published paper (Refereed)
Abstract [en]

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 71-75
Keywords [en]
ARIMA model, combined model, Elman neural network, Heat demand prediction, STL decomposition, Digital integrated circuits, ARIMA modeling, Autoregressive integrated moving average models, Elman neural networks (ENN), Intelligent energies, Research topics, Forecasting
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-41775DOI: 10.1109/ICNIDC.2018.8525632Scopus ID: 2-s2.0-85058338764ISBN: 9781538660669 (print)OAI: oai:DiVA.org:mdh-41775DiVA, id: diva2:1272913
Conference
6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018, 22 August 2018 through 24 August 2018
Available from: 2018-12-20 Created: 2018-12-20 Last updated: 2018-12-20Bibliographically approved

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Li, Hailong

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf