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Processes
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Data-Based Prediction Models in Energy Systems: From Principles to Applications
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Data-Based Prediction Models in Energy Systems: From Principles to Applications

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 2323

Special Issue Editors

Prof. Dr. Chao Min

E-Mail Website
Guest Editor
School of Sciences, Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China
Interests: optimization; data analysis; interpretable machine learning in petroleum engineering
Dr. Xin Ma

E-Mail Website
Guest Editor
School of Science, Southwest University of Science and Technology, Mianyang 621010, China
Interests: grey system; machine learning; intelligent optimization; energy forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaogang Li

E-Mail Website
Guest Editor
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Interests: reservior stimulation; intelligent hydraulic fracturing
Dr. Huohai Yang

E-Mail Website
Guest Editor
Petroleum Engineering School, Southwest Petroleum University, Chengdu 610500, China
Interests: oil-gas field development engineering; artificial intelligence in petroleum engineering

Special Issue Information

Dear Colleagues,

Data science has become an independent discipline, and numerous industries have benefited from data science and technology in recent years. As one of the lifelines of industrial society, energy is undergoing an unprecedented global revolution. In addition to advancements in traditional energy technologies, the introduction of data science technology is profoundly influencing the progress of the energy revolution. However, the rapid growth of energy demand and the diversity of energy sources are making energy systems more complex, presenting significant challenges for the industry. Among the numerous successful applications of data-based prediction models in energy systems, we believe that such research will be very impactful in the near future.

This Special Issue, "Data-based Prediction Models in Energy Systems: From Principles to Applications," will feature high-quality works on data-based prediction models, including innovations in methodology and comprehensive applications. Potential topics include (but are not limited to):

  • Grey system models for energy forecasting;
  • AI-based models for energy forecasting;
  • Hybrid data-based prediction models with intelligent algorithms;
  • Applications in fossil fuels/renewable energy.

Prof. Dr. Chao Min
Dr. Xin Ma
Prof. Dr. Xiaogang Li
Dr. Huohai Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • grey system models
  • machine learning/deep learning models
  • data-driven prediction models
  • intelligent optimization
  • petroleum engineering
  • renewable energy
 

Published Papers (4 papers)

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Research

17 pages, 3277 KiB  
Article
Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model
by Su Yang, Jinxuan Han, Lin Liu, Xingwen Wang, Lang Yin and Jianfa Ci
Processes 2024, 12(5), 918; https://doi.org/10.3390/pr12050918 (registering DOI) - 30 Apr 2024
Viewed by 114
Abstract
Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based [...] Read more.
Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty. Full article
(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)
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19 pages, 4023 KiB  
Article
Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model
by Chunlan Zhao, Ying Jia, Yao Qu, Wenjuan Zheng, Shaodan Hou and Bing Wang
Processes 2024, 12(5), 878; https://doi.org/10.3390/pr12050878 - 26 Apr 2024
Viewed by 257
Abstract
In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural [...] Read more.
In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs. Full article
(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)
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19 pages, 17550 KiB  
Article
A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction
by Ling Xiao, Ruofan An and Xue Zhang
Processes 2024, 12(4), 793; https://doi.org/10.3390/pr12040793 - 15 Apr 2024
Viewed by 339
Abstract
Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid [...] Read more.
Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management. Full article
(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)
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28 pages, 5694 KiB  
Article
A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process
by Zhenglang Wang, Zao Feng, Zhaojun Ma and Jubo Peng
Processes 2024, 12(1), 32; https://doi.org/10.3390/pr12010032 - 22 Dec 2023
Viewed by 925
Abstract
Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to [...] Read more.
Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions. Full article
(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)
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