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SEA: A Combined Model for Heat Demand Prediction

Xie, J; Guo, J; Ma, Z; Xue, JH; Sun, Q; Li, H; Guo, J; (2018) SEA: A Combined Model for Heat Demand Prediction. In: Proceedings of 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). (pp. pp. 71-75). IEEE: Guiyang, China. Green open access

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Abstract

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.

Type: Proceedings paper
Title: SEA: A Combined Model for Heat Demand Prediction
Event: 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC)
ISBN-13: 9781538660669
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICNIDC.2018.8525632
Publisher version: https://doi.org/10.1109/ICNIDC.2018.8525632
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Heat demand prediction; combined model; STL decomposition; Elman neural network; ARIMA model
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/10070884
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