Open Access

Prediction of Solar Radiation using Deep LSTM-based Machine Learning Algorithm

K. C. Jayasankar, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, TN, India G. Anandhakumar, anandhakumar@saveetha.com
Department of Electrical and Electronics Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, TN, India
A. Kalaimurugan Department of Electrical and Electronics Engineering, Agni College of Technology, Chennai, TN, India


J. Environ. Nanotechnol., Volume 13, No 3 (2024) pp. 01-08

https://doi.org/10.13074/jent.2024.09.242585

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Abstract

Solar radiation, a critical parameter for various applications such as solar energy systems and weather forecasting, exhibits complex temporal patterns influenced by numerous environmental factors. In the quest to enhance the accuracy of solar radiation predictions, this study introduces a novel approach utilizing Deep Long Short-Term Memory (Deep LSTM) networks, a type of recurrent neural network (RNN) known for its capability to model sequential data. Traditional prediction methods often struggle to capture these intricacies, leading to sub-optimal performance. This investigation is based on a Deep LSTM-based machine learning algorithm designed to predict solar radiation with elevated exactitude. The model is meticulously trained on copious datasets encompassing historical meteorological data, including temperature, humidity, wind velocity, and antecedent solar radiation metrics. Pivotal stages encompass data preprocessing, judicious feature selection, and hyperparameter optimization to enhance the model’s predictive efficacy. Empirical results clearly illustrate that the Deep LSTM model surpasses traditional methodologies, attaining superior accuracy and resilience across diverse meteorological scenarios. The ramifications of this endeavor portend significant advancements in the strategic planning and administration of solar energy resources, thereby contributing to more dependable and efficacious renewable energy systems.

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