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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: 10 January 2025 | Viewed by 6069

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
 

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Published Papers (6 papers)

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Research

14 pages, 5119 KiB  
Article
A Method for Optimizing Production Layer Regrouping Based on a Genetic Algorithm
by Lining Cui, Jiqun Zhang, Dehai He, Longchuan Pu, Boyang Peng and Xiaolin Ping
Processes 2024, 12(9), 1881; https://doi.org/10.3390/pr12091881 - 2 Sep 2024
Viewed by 498
Abstract
As waterflooding multi-layer reservoirs reach the high-water-cut stage, inter-layer conflicts become increasingly serious, leading to a worsening development effect over time. Production layer regrouping is an effective approach for resolving inter-layer conflicts and improving waterflooding efficiency. At the current stage, there are limitations [...] Read more.
As waterflooding multi-layer reservoirs reach the high-water-cut stage, inter-layer conflicts become increasingly serious, leading to a worsening development effect over time. Production layer regrouping is an effective approach for resolving inter-layer conflicts and improving waterflooding efficiency. At the current stage, there are limitations to most of the methods of production layer regrouping. This article proposes a smart method for optimizing the layer regroup plan based on a genetic algorithm. Comprehensively considering various factors that affect the regroup of layers, such as layer thickness, porosity, permeability, remaining oil saturation, remaining reserves, recovery ratio, water cut, etc., based on the combination principle of “smaller intra-group variance and larger inter-group variance of each influencing factor are expected”, a genetic algorithm is used to calculate the fitness value of the initial combination schemes, and the advantageous schemes with higher fitness values are selected as the basis of the next generation. Then, crossover and mutation operations are performed on those advantageous schemes to generate new schemes. Through continuous selection and evolution, until the global optimal solution with the highest fitness value is found, the optimal combination scheme is determined. Comparative analysis with numerical simulation results demonstrates the reliability of this intelligent method, with an increased oil recovery of 4.34% for the sample reservoir. Unlike selecting a preferable plan from a limited number of predefined combination schemes, this method is an automatic optimization to solve the optimal solution of the problem. It improves both efficiency and accuracy as compared to conventional reservoir engineering methods, numerical simulation methods, and most mathematical methods, thus providing effective guidance for EOR strategies of waterflooding reservoirs in the high-water-cut stage. 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, 1398 KiB  
Article
A Hybrid Grey System Model Based on Stacked Long Short-Term Memory Layers and Its Application in Energy Consumption Forecasting
by Yiwu Hao and Xin Ma
Processes 2024, 12(8), 1749; https://doi.org/10.3390/pr12081749 - 20 Aug 2024
Viewed by 613
Abstract
Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages [...] Read more.
Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model’s performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model’s prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting. Full article
(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)
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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 - 30 Apr 2024
Viewed by 825
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
Cited by 2 | Viewed by 878
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
Cited by 1 | Viewed by 793
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
Cited by 2 | Viewed by 1632
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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