{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:07:10Z","timestamp":1775095630198,"version":"3.50.1"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,12,14]]},"abstract":"<jats:p>Oil and gas will remain essential to global economic development and prosperity for decades to come, and the oil and gas industry is an energy-intensive industry. Thus, enhancing energy efficiency for producing oil and gas in oil and gas companies is an important issue. The intelligent energy consumption prediction method with the ability to analyze energy consumption patterns and to identify targets for energy saving proved itself as an effective approach for energy efficiency in many industrial domains. Moreover, prediction of energy consumption enables managers to scientifically plan out the energy usage of energy production and to shift energy usage to off-peak periods. However, it still remains a challenging issue to some degree with the unpredictability and uncertainty caused by various energy consumption behaviors, and this phenomenon is becoming more obvious in the oil and gas company. To this end, in our work, we primarily discussed the forecasting of the energy consumption in the oil and gas company. Firstly, four different forecasting models, support vector machine, linear regression, extreme learning machine, and artificial neural network, were trained on the training dataset and then evaluated by the test dataset. Secondly, in order to enhance the energy consumption prediction accuracy, the combinations of all these four models were examined with the RMSE value by taking the average of two models\u2019 outputs. The outcomes show that these four different models are able to predict energy consumption with good accuracy, but the hybrid model\u2014artificial neural network and extreme learning machine\u2014would present higher accuracy. In addition, the hybrid model is installed in the energy management system of the oil and gas industry to manage oil field energy consumption and improve the efficiency.<\/jats:p>","DOI":"10.1155\/2021\/5729630","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T19:35:15Z","timestamp":1639510515000},"page":"1-7","source":"Crossref","is-referenced-by-count":9,"title":["Using Hybrid Machine Learning Methods to Predict and Improve the Energy Consumption Efficiency in Oil and Gas Fields"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9116-1701","authenticated-orcid":true,"given":"Jun","family":"Li","sequence":"first","affiliation":[{"name":"Northwest Branch of Research Institute of Petroleum Exploration and Development of CNPC, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yidong","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwest Branch of Research Institute of Petroleum Exploration and Development of CNPC, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northwest Branch of Research Institute of Petroleum Exploration and Development of CNPC, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanbao","family":"Fu","sequence":"additional","affiliation":[{"name":"Northwest Branch of Research Institute of Petroleum Exploration and Development of CNPC, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1002\/ese3.161"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2014.12.036"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.12.072"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.3390\/en11020452"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2015.2397003"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2015.03.038"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.08.011"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2014.08.023"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.03.125"},{"key":"11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.enbuild.2016.05.028","article-title":"Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique","volume":"126","author":"F. 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