Utilizing a CNN-RNN machine learning approach for forecasting time-series outlet fluid temperature monitoring by long-term operation of BHEs system

IF 3.5 2区 工程技术 Q3 ENERGY & FUELS
Makarakreasey King , Sang Inn Woo , Chan-Young Yune
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引用次数: 0

Abstract

The Borehole Heat Exchanger (BHE) plays a pivotal role in enhancing heat exchange efficiency within Ground Source Heat Pump (GSHP) systems. The accurate prediction of the BHE's outlet fluid temperature is crucial for optimizing GSHP performance, energy storage, and resource conservation. However, conventional machine learning methods encounter challenges in manual feature extraction, learning complex nonlinear relationships, and adapting to real-world scenarios. To address these limitations, this research proposes a crossbreed model integrating Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures to forecast long-term outlet fluid temperature in BHE systems. The model framework encompasses data preprocessing, utilizing refined data in the CNN module for temporal feature extraction, subsequently passed to the RNN module to capture sequential and temporal patterns from each dataset. Specifically, the advanced CNN-RNN architecture is designed to establish a comprehensive input-output mapping, leveraging essential input features such as inlet fluid, ambient air, and subsurface temperatures at varying depths (0, 10, and 20 m). Performance evaluation metrics, including R2, RMSE, MAE, and AARE, are employed to compare and assess prediction accuracy across various models, including LSTM, CNN, and SimpleRNN. The obtained results demonstrate the superior performance of the proposed model, achieving an RSME of 0.818, MAE of 0.642, AARE of 0.0305, and an R2 value of 98.75 %. This surpasses the performance of traditional prediction models (LSTM, CNN, and SimpleRNN) by 3.01 %, 5.80 %, and 19.52 %, respectively. Notably, the remarkably low MAE of 0.642 exhibited by a CNN-RNN model underscores its capability to outperform traditional approaches, especially when handling large datasets. These findings emphasize the significance of the developed model in facilitating efficient operation, positioning it as a valuable tool for advancing the long-term sustainability of BHE systems.

利用 CNN-RNN 机器学习方法预测 BHE 系统长期运行时的出口流体温度监测时间序列
钻孔换热器(BHE)在提高地源热泵(GSHP)系统的热交换效率方面发挥着举足轻重的作用。准确预测 BHE 的出口流体温度对于优化 GSHP 性能、能量存储和资源保护至关重要。然而,传统的机器学习方法在手动特征提取、学习复杂的非线性关系和适应真实世界场景方面遇到了挑战。为了解决这些局限性,本研究提出了一种整合了卷积神经网络(CNN)和循环神经网络(RNN)架构的杂交模型,用于预测 BHE 系统的长期出口流体温度。该模型框架包括数据预处理,利用 CNN 模块中的精炼数据进行时间特征提取,然后传递到 RNN 模块,以捕捉每个数据集的顺序和时间模式。具体来说,先进的 CNN-RNN 架构旨在建立一个全面的输入输出映射,利用重要的输入特征,如入口流体、环境空气和不同深度(0、10 和 20 米)的地下温度。性能评估指标包括 R2、RMSE、MAE 和 AARE,用于比较和评估 LSTM、CNN 和 SimpleRNN 等不同模型的预测精度。结果表明,所提模型性能优越,RSME 为 0.818,MAE 为 0.642,AARE 为 0.0305,R2 为 98.75%。这比传统预测模型(LSTM、CNN 和 SimpleRNN)的性能分别高出 3.01 %、5.80 % 和 19.52 %。值得注意的是,CNN-RNN 模型的 MAE 值低至 0.642,这突显了其超越传统方法的能力,尤其是在处理大型数据集时。这些研究结果强调了所开发模型在促进高效运行方面的重要意义,并将其定位为促进 BHE 系统长期可持续发展的宝贵工具。
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来源期刊
Geothermics
Geothermics 工程技术-地球科学综合
CiteScore
7.70
自引率
15.40%
发文量
237
审稿时长
4.5 months
期刊介绍: Geothermics is an international journal devoted to the research and development of geothermal energy. The International Board of Editors of Geothermics, which comprises specialists in the various aspects of geothermal resources, exploration and development, guarantees the balanced, comprehensive view of scientific and technological developments in this promising energy field. It promulgates the state of the art and science of geothermal energy, its exploration and exploitation through a regular exchange of information from all parts of the world. The journal publishes articles dealing with the theory, exploration techniques and all aspects of the utilization of geothermal resources. Geothermics serves as the scientific house, or exchange medium, through which the growing community of geothermal specialists can provide and receive information.
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