Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network

Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network Zhao Zhang, Dezhi Dong, Lili Lv, Liyuan Peng, Bing Li, Miao Peng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3940604/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Electrical Engineering → Version 1 posted 14 You are reading this latest preprint version Abstract Oil and gas fields have a large amount of distributed new energy. In order to improve the utilization rate of new energy and respond to the dispatching needs of China's State Grid, it is necessary to study the use of ultra-short-term load forecasting algorithms to improve the load forecasting accuracy of oil and gas fields and support the coordinated interaction of source, grid and load in the integrated energy system of oil and gas fields. This paper proposes an ultra-short-term load forecasting algorithm based on a hybrid neural network called Convolutional-Bidirectional Long Short Term Memory-Skip (CNN-BiLSTM-Skip). Using the operating load data of an oil and gas field in Northeast China as a data set, we first constructed a cooling, heating and power system architecture model with wind turbines, photovoltaics, power grids and natural gas as “source and grid loads”; Secondly, we used an improved hybrid multi-time scale algorithm and unit A prediction model was constructed based on the operating load data, and the prediction results of the nonlinear part and linear part of the model were output and integrated to obtain the final prediction result; Finally, the prediction error evaluation index of the algorithm proposed in this article was compared with algorithms such as BP, LSTM, and CNN-LSTM. The results show that the algorithm proposed in this article has stronger robustness and higher accuracy. The proposed CNN-BiLSTM-SKIP algorithm improves the prediction accuracy. Compared with the BP neural network algorithm, the MAPE evaluation index has an average accuracy increase of 3.78%, compared with the LSTM prediction algorithm, the accuracy has increased by 1.63% on average, and compared with the CNN-LSTM prediction algorithm, the accuracy has increased by 0.74% on average; and the proposed prediction algorithm is compared with the BP neural network algorithm, LSTM prediction algorithm and CNN-LSTM algorithm, the RMSE and MAE evaluation index values are both the smallest, which can support the collaborative interaction of oil and gas field source, network and load and realize the planning and dispatching needs. Ultra short term load forecasting CNN-BiLSTM-Skip Hybrid neural network Prediction accuracy Comprehensive energy system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Electrical Engineering → Version 1 posted Editorial decision: Revision requested 16 Aug, 2024 Reviews received at journal 15 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviews received at journal 09 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviews received at journal 09 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviewers invited by journal 11 Jun, 2024 Editor assigned by journal 09 Feb, 2024 Submission checks completed at journal 09 Feb, 2024 First submitted to journal 08 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3940604","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271938594,"identity":"dbb1bbe6-f5f7-4351-a18f-fb55f9ef14b3","order_by":0,"name":"Zhao 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