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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/14/3/627 |
| _version_ | 1797241832024834048 |
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| author | Yaolong Hou Xueting Wang Han Chang Yanan Dong Di Zhang Chenlin Wei Inhee Lee Yijun Yang Yuanzhao Liu Jipeng Zhang |
| author_facet | Yaolong Hou Xueting Wang Han Chang Yanan Dong Di Zhang Chenlin Wei Inhee Lee Yijun Yang Yuanzhao Liu Jipeng Zhang |
| author_sort | Yaolong Hou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:29:35Z |
| format | Article |
| id | doaj.art-eb5d671efd49462e8465b8ebbaa0c5ae |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-04-24T18:29:35Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-eb5d671efd49462e8465b8ebbaa0c5ae2024-03-27T13:29:05ZengMDPI AGBuildings2075-53092024-02-0114362710.3390/buildings14030627Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural NetworkYaolong Hou0Xueting Wang1Han Chang2Yanan Dong3Di Zhang4Chenlin Wei5Inhee Lee6Yijun Yang7Yuanzhao Liu8Jipeng Zhang9Department of Railway Engineering, Zhengzhou Railway Vocational and Technical College, Zhengzhou 451460, ChinaDepartment of Architecture, School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Architecture, School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Architecture, School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Architecture, School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Humanities and Social Science, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Architecture, Pusan National University, Busan 46241, Republic of KoreaSchool of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Architecture, Pusan National University, Busan 46241, Republic of KoreaCollege of Computing, Beijing Institute of Technology, Zhuhai 519088, ChinaWith 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.https://www.mdpi.com/2075-5309/14/3/627energy shortageinstrument monitoringnatural gas consumptionKNNBP neural network |
| spellingShingle | Yaolong Hou Xueting Wang Han Chang Yanan Dong Di Zhang Chenlin Wei Inhee Lee Yijun Yang Yuanzhao Liu Jipeng Zhang Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network Buildings energy shortage instrument monitoring natural gas consumption KNN BP neural network |
| title | Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network |
| title_full | Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network |
| title_fullStr | Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network |
| title_full_unstemmed | Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network |
| title_short | Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network |
| title_sort | natural gas consumption monitoring based on k nn algorithm and consumption prediction framework based on backpropagation neural network |
| topic | energy shortage instrument monitoring natural gas consumption KNN BP neural network |
| url | https://www.mdpi.com/2075-5309/14/3/627 |
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