Bibliographic Details
| Title: |
Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network |
| Authors: |
Yaolong Hou, Xueting Wang, Han Chang, Yanan Dong, Di Zhang, Chenlin Wei, Inhee Lee, Yijun Yang, Yuanzhao Liu, Jipeng Zhang |
| Source: |
Buildings, Vol 14, Iss 3, p 627 (2024) |
| Publisher Information: |
MDPI AG, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Building construction |
| Subject Terms: |
energy shortage, instrument monitoring, natural gas consumption, KNN, BP neural network, Building construction, TH1-9745 |
| More Details: |
With increasing consumption of primary energy and deterioration of the global environment, clean energy sources with large reserves, such as natural gas, have gradually gained a higher proportion of the global energy consumption structure. Monitoring and predicting consumption data play a crucial role in reducing energy waste and improving energy supply efficiency. However, owing to factors such as high monitoring device costs, safety risks associated with device installation, and low efficiency of manual meter reading, monitoring natural gas consumption data at the household level is challenging. Moreover, there is a lack of methods for predicting natural gas consumption at the household level in residential areas, which hinders the provision of accurate services to households and gas companies. Therefore, this study proposes a gas consumption monitoring method based on the K-nearest neighbours (KNN) algorithm. Using households in a residential area in Xi’an as research subjects, the feasibility of this monitoring method was validated, achieving a model recognition accuracy of 100%, indicating the applicability of the KNN algorithm for monitoring natural gas consumption data. In addition, this study proposes a framework for a natural gas consumption prediction system based on a backpropagation (BP) neural network. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2075-5309 |
| Relation: |
https://www.mdpi.com/2075-5309/14/3/627; https://doaj.org/toc/2075-5309 |
| DOI: |
10.3390/buildings14030627 |
| Access URL: |
https://doaj.org/article/eb5d671efd49462e8465b8ebbaa0c5ae |
| Accession Number: |
edsdoj.b5d671efd49462e8465b8ebbaa0c5ae |
| Database: |
Directory of Open Access Journals |
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