Solar thermal process parameters forecasting for evacuated tube collectors (ETC) based on RNN-LSTM

Akbar, Muhammad Ali and Haja Mohideen, Ahmad Jazlan and Rashid, Muhammad Mahbubur and Mohd Zaki, Hasan Firdaus and Akhter, Muhammad Naveed and Embong, Abd Halim (2023) Solar thermal process parameters forecasting for evacuated tube collectors (ETC) based on RNN-LSTM. IIUM Engineering Journal, 24 (1). pp. 256-268. ISSN 1511-788X E-ISSN 2289-7860

	PDF (Journal) - Published Version Restricted to Registered users only Download (1MB) | Request a copy
Preview	PDF (WOS) - Supplemental Material Download (281kB) | Preview

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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	evacuated tube collectors; solar irradiance; flux calorimeter; recurrent neural networks; long short term memory
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Ahmad Jazlan Haja Mohideen
Date Deposited:	09 Jan 2023 16:13
Last Modified:	14 Feb 2024 17:04
URI:	http://irep.iium.edu.my/id/eprint/103059

Actions (login required)

View Item