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Medium-term heat load prediction for an existing residential building based on a wireless on-off control system
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- Gu, Jihao (author)
- Hebei University of Technology
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- Wang, Jin (author)
- Hebei University of Technology
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- Qi, Chengying (author)
- Hebei University of Technology
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- (creator_code:org_t)
- Elsevier BV, 2018
- 2018
- English 10 s.
- In: Energy. - : Elsevier BV. - 0360-5442. ; 152, s. 709-718
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Keyword
- District heating system
- Heat load
- Neural network
- Prediction
- Support vector machine
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
- Gu, Jihao
- Wang, Jin
- Qi, Chengying
- Min, Chunhua
- Sundén, Bengt
- About the subject
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- ENGINEERING AND TECHNOLOGY
- ENGINEERING AND ...
- and Mechanical Engin ...
- and Energy Engineeri ...
- Articles in the publication
- Energy
- By the university
- Lund University
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