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Multi-scene design analysis of integrated energy system based on feature extraction algorithm

The specific analysis of a region’s energy needs to model and simulate various types of energy, quantify energy information, and clearly and intuitively reflect the energy situation and energy potential of a region. In this paper, according to the input attributes of various energy load forecasting...

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Main Authors: Huang, Sihua, Mohd Ali, Noor Azizi, Shaari, Nazlina, Mat Noor, Mohd Sallehuddin
Format: Article
Published: Elsevier 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102264/
https://www.sciencedirect.com/science/article/pii/S2352484722007168
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spelling my.upm.eprints.1022642023-06-15T21:27:35Z http://psasir.upm.edu.my/id/eprint/102264/ Multi-scene design analysis of integrated energy system based on feature extraction algorithm Huang, Sihua Mohd Ali, Noor Azizi Shaari, Nazlina Mat Noor, Mohd Sallehuddin The specific analysis of a region’s energy needs to model and simulate various types of energy, quantify energy information, and clearly and intuitively reflect the energy situation and energy potential of a region. In this paper, according to the input attributes of various energy load forecasting models, the correlation degree of main control factors is analyzed, and the influence degrees of environmental factors on electric power, gas, heating and cooling loads are obtained respectively. Then, convolution neural network is used to extract the feature vectors of comprehensive environmental factors. Finally, according to the given feature vectors, the feature clustering models of various energy loads are established by using K-means clustering algorithm, and the load forecasting results of multi-energy systems are obtained. The errors between the predicted results of various energy loads and the actual load records in the study area are 1.105%, 1.876%, 3.102% and 2.834%, respectively. The load forecasting method based on feature clustering proposed in this paper can effectively extract the influence of different environmental factors on the load forecasting results, and get more accurate load forecasting results. Elsevier 2022 Article PeerReviewed Huang, Sihua and Mohd Ali, Noor Azizi and Shaari, Nazlina and Mat Noor, Mohd Sallehuddin (2022) Multi-scene design analysis of integrated energy system based on feature extraction algorithm. Energy Reports, 8 (supp.6). 466 - 476. ISSN 2352-4847 https://www.sciencedirect.com/science/article/pii/S2352484722007168 10.1016/j.egyr.2022.03.161
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The specific analysis of a region’s energy needs to model and simulate various types of energy, quantify energy information, and clearly and intuitively reflect the energy situation and energy potential of a region. In this paper, according to the input attributes of various energy load forecasting models, the correlation degree of main control factors is analyzed, and the influence degrees of environmental factors on electric power, gas, heating and cooling loads are obtained respectively. Then, convolution neural network is used to extract the feature vectors of comprehensive environmental factors. Finally, according to the given feature vectors, the feature clustering models of various energy loads are established by using K-means clustering algorithm, and the load forecasting results of multi-energy systems are obtained. The errors between the predicted results of various energy loads and the actual load records in the study area are 1.105%, 1.876%, 3.102% and 2.834%, respectively. The load forecasting method based on feature clustering proposed in this paper can effectively extract the influence of different environmental factors on the load forecasting results, and get more accurate load forecasting results.
format Article
author Huang, Sihua
Mohd Ali, Noor Azizi
Shaari, Nazlina
Mat Noor, Mohd Sallehuddin
spellingShingle Huang, Sihua
Mohd Ali, Noor Azizi
Shaari, Nazlina
Mat Noor, Mohd Sallehuddin
Multi-scene design analysis of integrated energy system based on feature extraction algorithm
author_facet Huang, Sihua
Mohd Ali, Noor Azizi
Shaari, Nazlina
Mat Noor, Mohd Sallehuddin
author_sort Huang, Sihua
title Multi-scene design analysis of integrated energy system based on feature extraction algorithm
title_short Multi-scene design analysis of integrated energy system based on feature extraction algorithm
title_full Multi-scene design analysis of integrated energy system based on feature extraction algorithm
title_fullStr Multi-scene design analysis of integrated energy system based on feature extraction algorithm
title_full_unstemmed Multi-scene design analysis of integrated energy system based on feature extraction algorithm
title_sort multi-scene design analysis of integrated energy system based on feature extraction algorithm
publisher Elsevier
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102264/
https://www.sciencedirect.com/science/article/pii/S2352484722007168
_version_ 1769844439612653568
score 13.211869

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