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Medium-term heat load prediction for an existing residential building based on a wireless on-off control system

Gu, Jihao; Wang, Jin LU ; Qi, Chengying; Min, Chunhua and Sundén, Bengt LU (2018) In Energy 152. p.709-718
Abstract

For district heating systems, prediction of the heat load is a very important topic for energy storage and optimized operation. For large and complex heating systems, most prediction models in previous publications only considered the influence of outdoor temperature, whereas the indoor temperature and thermal inertia of buildings were not included. For an energy-efficient residential building in Shijiazhuang (China), the heat load prediction is investigated using various prediction models, including a wavelet neural network (WNN), extreme learning machine (ELM), support vector machine (SVM) and back propagation neural network optimized by a genetic algorithm (GA-BP). In these models, the indoor temperature and historical loads are... (More)

For district heating systems, prediction of the heat load is a very important topic for energy storage and optimized operation. For large and complex heating systems, most prediction models in previous publications only considered the influence of outdoor temperature, whereas the indoor temperature and thermal inertia of buildings were not included. For an energy-efficient residential building in Shijiazhuang (China), the heat load prediction is investigated using various prediction models, including a wavelet neural network (WNN), extreme learning machine (ELM), support vector machine (SVM) and back propagation neural network optimized by a genetic algorithm (GA-BP). In these models, the indoor temperature and historical loads are considered as influencing factors. It is found that the prediction accuracies of the ELM and GA-BP are slightly higher than that of WNN, so the ELM and GA-BP models provide feasible methods for the heat load prediction. The SVM shows smaller relative errors in the model prediction compared with three neural network algorithms.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
District heating system, Heat load, Neural network, Prediction, Support vector machine
in
Energy
volume
152
pages
10 pages
publisher
Elsevier
external identifiers
  • scopus:85047457581
ISSN
0360-5442
DOI
10.1016/j.energy.2018.03.179
language
English
LU publication?
yes
id
336d93db-e7b9-44b8-8c55-6539886edbcf
date added to LUP
2018-06-05 09:56:40
date last changed
2019-09-11 03:58:15
@article{336d93db-e7b9-44b8-8c55-6539886edbcf,
  abstract     = {<p>For district heating systems, prediction of the heat load is a very important topic for energy storage and optimized operation. For large and complex heating systems, most prediction models in previous publications only considered the influence of outdoor temperature, whereas the indoor temperature and thermal inertia of buildings were not included. For an energy-efficient residential building in Shijiazhuang (China), the heat load prediction is investigated using various prediction models, including a wavelet neural network (WNN), extreme learning machine (ELM), support vector machine (SVM) and back propagation neural network optimized by a genetic algorithm (GA-BP). In these models, the indoor temperature and historical loads are considered as influencing factors. It is found that the prediction accuracies of the ELM and GA-BP are slightly higher than that of WNN, so the ELM and GA-BP models provide feasible methods for the heat load prediction. The SVM shows smaller relative errors in the model prediction compared with three neural network algorithms.</p>},
  author       = {Gu, Jihao and Wang, Jin and Qi, Chengying and Min, Chunhua and Sundén, Bengt},
  issn         = {0360-5442},
  keyword      = {District heating system,Heat load,Neural network,Prediction,Support vector machine},
  language     = {eng},
  month        = {06},
  pages        = {709--718},
  publisher    = {Elsevier},
  series       = {Energy},
  title        = {Medium-term heat load prediction for an existing residential building based on a wireless on-off control system},
  url          = {http://dx.doi.org/10.1016/j.energy.2018.03.179},
  volume       = {152},
  year         = {2018},
}