Solar energy forecasting using liquid time-constant networks

Manu, Abdurrahman Mohammed and Haja Mohideen, Ahmad Jazlan and Mohd Ibrahim, Azhar and Mohd Zaki, Hasan Firdaus (2026) Solar energy forecasting using liquid time-constant networks. In: 14th International Conference on Renewable Power Generation (RPG 2025), 24 – 26 October 2025, Shanghai, China.

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Abstract

This paper investigates the application of Liquid Time-Constant (LTC) networks for solar energy forecasting. Traditional models, such as ARIMA and conventional machine learning techniques, often fail to effectively capture nonlinear relationships and process continuous-time data. We implement LTCs, a continuous-time recurrent neural network, as a novel solution and benchmark their performance against other deep learning models, namely LSTM, GRU and a vanilla ANN. Utilizing a dataset comprising weather variables and solar energy readings, we assess model performance using R-Square, Mean Square Error (MSE), and Mean Absolute Error (MAE). The LTC model excels with an R-Square of 0.9820 and MSE of 0.5264, surpassing other models in these metrics, while the LSTM model achieves a slightly better MAE of 0.5543 compared to LTCN’s 0.5937. These results highlight the LTC's superior ability to model complex temporal patterns, positioning them as a promising tool for solar energy forecasting with enhanced accuracy over existing approaches.

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Solar energy, Liquid Time-Constant Network, Forecasting, Regression, Recurrent neural network
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 > Department of Mechatronics Engineering
Depositing User:	Dr Ahmad Jazlan Haja Mohideen
Date Deposited:	07 May 2026 11:31
Last Modified:	07 May 2026 11:31
Queue Number:	2026-04-Q3088
URI:	http://irep.iium.edu.my/id/eprint/128677

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