Xin Liu^1,*, Meng Sun¹, Bo Lin², Shibo Gu¹

Energy Engineering, Vol.122, No.3, pp. 1053-1072, 2025, DOI:10.32604/ee.2025.060489 - 07 March 2025

Abstract Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures… More >

Nataliya Shakhovska^1,2,*, Mykola Medykovskyi¹, Oleksandr Gurbych^1,3, Mykhailo Mamchur^1,3, Mykhailo Melnyk¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024

Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

Xiao Ya Ma^1,2,*, Jin Tong^1,2, Fei Jiang³, Min Xu⁴, Li Mei Sun¹, Qiu Yan Chen¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6145-6159, 2023, DOI:10.32604/cmc.2023.034833 - 28 December 2022

Abstract Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long… More >

Shaohua Gu^1,2, Jiabao Wang³, Liang Xue^3,*, Bin Tu³, Mingjin Yang³, Yuetian Liu³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1579-1599, 2022, DOI:10.32604/cmes.2022.019435 - 19 April 2022

Abstract Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the… More >