Forecasting monthly gas field production based on the CNN-LSTM model

Zha, Wenshu; Liu, Yuping; Wan, Yong; Luo, Ruilan; Li, Daolun; Yang, Sen; Xu, Yan

doi:10.1016/j.energy.2022.124889

Cited by 154 publications

(34 citation statements)

References 26 publications

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“…Long short-term memory is an example of recurrent neural network (RNN) architecture, which was developed in order to solve the issue of disappearing gradients ( Zha et al, 2022 ). This issue was the motivation for its creation.…”

Section: Methodsmentioning

confidence: 99%

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids

Khalid,

Mustafa,

Rana

et al. 2024

PeerJ Computer Science

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Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

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Section: Methodsmentioning

confidence: 99%

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids

Khalid,

Mustafa,

Rana

et al. 2024

PeerJ Computer Science

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“…CNN-LSTM was also applied in different fields such as sentiment Analysis [45], predicting residential energy consumption [46], gold price time-series forecasting [47], speech emotion recognition using deep 1D & 2D CNN LSTM [48], human Activity Recognition [49], detection of diabetes using CNN and CNN-LSTM network and heart rate signals [50], and forecasting monthly gas field production [51].…”

Section: Related Workmentioning

confidence: 99%

A new hybrid model of convolutional neural networks and hidden Markov chains for image classification

Goumiri

Benboudjema

Pieczynski

2023

Neural Comput & Applic

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Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.

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“…When we consider the stability assessment of power systems, it is not only because it is directly related to the production operations of forestry plants, but also because it has a profound impact on the entire social and economic system. As a key [3] link in resource utilization and production, the power demand of forestry factories is not only related to the interests of the factory itself, but also related to the suppliers and consumers who cooperate with the factory, as well as the livelihood and development of the entire community [4].…”

Section: Introductionmentioning

confidence: 99%

Stability assessment of forestry plant power system based on improved long short-term memory network

Zhang,

Meng,

Tian

et al. 2024

J. Phys.: Conf. Ser.

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The stability assessment of forestry power system plays a vital role in many aspects. First of all, the stability of the power system is directly related to the production efficiency, employee safety and environmental sustainability of the factory. If the power system is unstable, it may cause power outage or power fluctuations, which will interrupt the production and turn the production line, which will lead to a reduction in production capacity and the increase in production costs. In addition, the stability of the power system is directly related to the security of employees. Sudden power failure or voltage fluctuations may cause improper equipment operation, and may even cause accidents and damage. Therefore, by evaluating the stability of the power system, potential security risks can be reduced. In addition, unstable power systems may cause waste and increase environmental burden. In order to improve the stability of the power system, this article proposes an improved LSTM (long and short memory) model of improved stability prediction. This model uses the power system to operate data, including current, voltage, frequency, and other parameters, and external factors related to weather conditions and load changes to predict the stability of the power system. Compared with traditional methods, this method performs well in predicting the failure and abnormalities of the power system, and has the ability to evaluate high accuracy and stability. Therefore, the improved LSTM model method can be regarded as a powerful tool for power system management and maintenance.

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Forecasting monthly gas field production based on the CNN-LSTM model

Cited by 154 publications

References 26 publications

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids

A new hybrid model of convolutional neural networks and hidden Markov chains for image classification

Stability assessment of forestry plant power system based on improved long short-term memory network

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