Solar Thermal Process Parameters Forecasting for Evacuated Tubes Collector (ETC) Based on RNN-LSTM

IF 0.6 Q3 ENGINEERING, MULTIDISCIPLINARY
Muhammad Ali Akbar, Ahmad Jazlan, Muhammad Mahbubur Rashid, Hasan Firdaus Mohd Zaki, Muhammad Naveed Akhter, A. H. Embong
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引用次数: 1

Abstract

Solar Heat for Industrial Process (SHIP) systems are a clean source of alternative and renewable energy for industrial processes. A typical SHIP system consists of a solar panel connected with a thermal storage system along with necessary piping. Predictive maintenance and condition monitoring of these SHIP systems are essential to prevent system downtime and ensure a steady supply of heated water for a particular industrial process. This paper proposes the use of recurrent neural network-based predictive models to forecast solar thermal process parameters. Data of five process parameters namely - Solar Irradiance, Solar Collector Inlet & Outlet Temperature, and Flux Calorimeter Readings at two points were collected throughout a four-month period. Two variants of RNN, including LSTM and Gated Recurrent Units, were explored and the performance for this forecasting task was compared. The results show that Root Mean Square Errors (RMSE) between the actual and predicted values were 0.4346 (Solar Irradiance), 61.51 (Heat Meter 1), 23.85 (Heat Meter 2), Inlet Temperature (0.432) and Outlet Temperature (0.805) respectively. These results open up possibilities for employing a deep learning based forecasting method in the application of SHIP systems. ABSTRAK: Penggunaan sumber bersih seperti Tenaga Solar dalam Proses Industri (SHIP) adalah satu kaedah alternatif untuk menhasilkan tenaga yang boleh diperbaharui bagi mengurangkan kesan gas rumah hijau yang terhasil dari proses industri. Sistem SHIP biasanya mengandungi panel solar dan sistem penyimpanan haba yang berhubung melalui paip yang sesuai. Penyelengaraan secara berkala diperlukan bagi memastikan sistem ini sentiasa membekalkan tenaga solar pada kadar bersesuaian dan bekalan tenaga solar yang terhasil berterusan dan tidak menjejaskan sistem pemanasan air bagi sesuatu proses industri. Kajian ini mencadangkan penggunaan model ramalan rangkaian neural berulang bagi meramal parameter proses pemanasan solar. Kelima-lima parameter proses iaitu – Iradiasi Solar, Suhu Saluran Keluar & Masuk Pengumpul Solar dan Bacaan Kalorimeter Fluks pada dua tempat diambil sepanjang empat bulan (dari Julai 2021 sehingga Oktober 2021). Dapatan menunjukkan dua varian RNN termasuk LSTM dan Unit Berulang dapat dibanding prestasinya bagi tugas ramalan ini. Dapatan kajian menunjukkan Ralat Punca Min Kuasa Dua (RMSE) antara bacaan sebenar dan ramalan adalah masing-masing 0.4346 (Iradiasi Solar), 61.51 (Meter Terma 1), 23.85 (Meter Terma 2), Suhu Salur Masuk (0.432) and Suhu Salur Keluar (0.805). Ini membuka peluang kajian mendalam berdasarkan kaedah ramalan dalam aplikasi sistem SHIP.
基于RNN-LSTM的真空管集热器太阳热过程参数预测
工业过程太阳能热(SHIP)系统是一种清洁的工业过程替代能源和可再生能源。典型的SHIP系统由太阳能电池板和蓄热系统以及必要的管道连接而成。这些SHIP系统的预测性维护和状态监测对于防止系统停机和确保特定工业过程的稳定热水供应至关重要。本文提出利用基于递归神经网络的预测模型对太阳能热过程参数进行预测。五个工艺参数的数据,即太阳辐照度,太阳能集热器入口和出口温度,以及两个点的通量量热计读数,在四个月的时间内收集。研究了RNN的两种变体,包括LSTM和门控循环单元,并比较了该预测任务的性能。结果表明,实际值与预测值的均方根误差(RMSE)分别为0.4346(太阳辐照度)、61.51(热计1)、23.85(热计2)、入口温度(0.432)和出口温度(0.805)。这些结果为在SHIP系统的应用中采用基于深度学习的预测方法开辟了可能性。摘要/ abstract摘要:彭家南太阳能发电工业有限公司(SHIP)是我国太阳能发电工业的重要组成部分,也是我国太阳能发电工业的重要组成部分。系统SHIP biasanya mengandungi太阳能板系统penyimpanan haba yang berhubung melalui paip yang sesuai。Penyelengaraan secara berkala diperlukan bagi memastikan系统ini sentiasa成员bebekalkan tenaga solar padar bersesuan和bekalan tenaga solar yang terhasil berterusan和datak menjejaskan系统peemanasan空气bagi sesuatu加工工业。kamjian ini menencadangkan penggunaan模型ramalan rangkaian神经系统berulang bagi meramal参数过程。Kelima-lima参数过程iiitu - Iradiasi Solar, Suhu Saluran Keluar和Masuk Pengumpul Solar和Bacaan热量计Fluks pada dua tempat diambil sepanjang empat bulan (dari Julai 2021 - seinga 2021年10月)。dpatan menunjukkan dua varian RNN termasuk LSTM dan Unit Berulang dpatat distasinya bagi tugas ramalan ini。Dapatan kajian menunjukkan Ralat Punca Min Kuasa Dua (RMSE) antara bacaan sebenar dan ramalan adalah masing-masing 0.4346 (Iradiasi Solar), 61.51 (Meter Terma 1), 23.85 (Meter Terma 2), Suhu Salur Masuk(0.432)和Suhu Salur Keluar(0.805)。应用系统SHIP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IIUM Engineering Journal
IIUM Engineering Journal ENGINEERING, MULTIDISCIPLINARY-
CiteScore
2.10
自引率
20.00%
发文量
57
审稿时长
40 weeks
期刊介绍: The IIUM Engineering Journal, published biannually (June and December), is a peer-reviewed open-access journal of the Faculty of Engineering, International Islamic University Malaysia (IIUM). The IIUM Engineering Journal publishes original research findings as regular papers, review papers (by invitation). The Journal provides a platform for Engineers, Researchers, Academicians, and Practitioners who are highly motivated in contributing to the Engineering disciplines, and Applied Sciences. It also welcomes contributions that address solutions to the specific challenges of the developing world, and address science and technology issues from an Islamic and multidisciplinary perspective. Subject areas suitable for publication are as follows: -Chemical and Biotechnology Engineering -Civil and Environmental Engineering -Computer Science and Information Technology -Electrical, Computer, and Communications Engineering -Engineering Mathematics and Applied Science -Materials and Manufacturing Engineering -Mechanical and Aerospace Engineering -Mechatronics and Automation Engineering
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