Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

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 ro...

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Published inBuildings (Basel) Vol. 14; no. 3; p. 627
Main Authors Hou, Yaolong, Wang, Xueting, Chang, Han, Dong, Yanan, Zhang, Di, Wei, Chenlin, Lee, Inhee, Yang, Yijun, Liu, Yuanzhao, Zhang, Jipeng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2024
Subjects
Algorithms
Back propagation networks
BP neural network
Clean energy
Climate change
Emissions
Energy consumption
Energy management systems
energy shortage
Energy shortages
Energy sources
Green technology
Households
instrument monitoring
K-nearest neighbors algorithm
KNN
Monitoring
Monitoring methods
Natural gas
natural gas consumption
Neural networks
Nuclear energy
Residential areas
China
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Summary: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.
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content type line 14
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings14030627