Authors :
R. Indhuja; Sivakami G.; Syamala Devi S.; Sowndarya A. K.
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/3ya6spac
Scribd :
https://tinyurl.com/yusbeuff
DOI :
https://doi.org/10.38124/ijisrt/25mar1047
Google Scholar
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Abstract :
Solar power prediction using Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) in
Python is a crucial application of deep learning for renewable energy optimization. This study focuses on leveraging time-
series forecasting capabilities of LSTM and RNN to predict solar power generation based on historical data, including
temperature, sunlight intensity, humidity, and other meteorological factors. By preprocessing data, normalizing inputs,
and training models using TensorFlow and Keras, the study enhances prediction accuracy. The comparative analysis of
LSTM and standard RNN highlights the superior performance of LSTM in capturing long-term dependencies and
mitigating vanishing gradient issues. The results demonstrate that deep learning models can effectively forecast solar
energy output, aiding energy grid management and sustainable resource planning.Solar power is one of the most
promising renewable energy sources, playing a crucial role in sustainable energy solutions. However, its efficiency depends
on various meteorological factors, such as sunlight intensity, temperature, humidity, and cloud cover, making accurate
prediction a challenging task.
This study explores the application of LSTM and RNN models for predicting solar power generation using Python-based machine
learning frameworks such as TensorFlow and Keras. By leveraging historical meteorological data, the proposed models aim to improve
forecasting accuracy, aiding energy management systems in optimizing solar energy utilization and grid stability. The research also
includes a comparative analysis of RNN and LSTM to assess their effectiveness in predicting solar power generation.
Keywords :
Solar Power Prediction, Time Series Forecasting, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Renewable Energy, Deep Learning, Energy Management, AI in Forecasting, Flask Web Application.
References :
- U. K. Das, K. S. Tey, M. Seyed mahmoudian, S. Mekhilef, M. Y. I. Idris, W. V. Deventer, B. Horan, and A. Stojcevski, “Forecasting of photovoltaic power generation and model optimization: A review,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 912 – 928, 2023.
- M. Abuella and B. Chowdhury, “Solar power probabilistic forecasting by using multiple linear regression analysis,” in Southeast Con 2021. IEEE,2022, pp. 1–5.
- A. Gensler, J. Henze, B. Sick, and N. Raabe, “Deep learning for solarpowerforecastingâ ˘AˇTan approach using autoencoder and lstmneuralnetworks,” in Systems, Man, and Cybernetics (SMC), 2023 IEEE Inter-national Conference on. IEEE, 2023, pp. 002 858–002 865.
- J. Zeng and W. Qiao, “Short-term solar power prediction using a support vector machine,” Renewable Energy, vol. 52, pp. 118–127, 2023.
- S. Ferrari, M. Lazzaroni, V. Piuri, L. Cristaldi, and M. Faifer, “Statistical models approach for solar radiation prediction,” in Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. IEEE, 2023, pp. 1734–1739
Solar power prediction using Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) in
Python is a crucial application of deep learning for renewable energy optimization. This study focuses on leveraging time-
series forecasting capabilities of LSTM and RNN to predict solar power generation based on historical data, including
temperature, sunlight intensity, humidity, and other meteorological factors. By preprocessing data, normalizing inputs,
and training models using TensorFlow and Keras, the study enhances prediction accuracy. The comparative analysis of
LSTM and standard RNN highlights the superior performance of LSTM in capturing long-term dependencies and
mitigating vanishing gradient issues. The results demonstrate that deep learning models can effectively forecast solar
energy output, aiding energy grid management and sustainable resource planning.Solar power is one of the most
promising renewable energy sources, playing a crucial role in sustainable energy solutions. However, its efficiency depends
on various meteorological factors, such as sunlight intensity, temperature, humidity, and cloud cover, making accurate
prediction a challenging task.
This study explores the application of LSTM and RNN models for predicting solar power generation using Python-based machine
learning frameworks such as TensorFlow and Keras. By leveraging historical meteorological data, the proposed models aim to improve
forecasting accuracy, aiding energy management systems in optimizing solar energy utilization and grid stability. The research also
includes a comparative analysis of RNN and LSTM to assess their effectiveness in predicting solar power generation.
Keywords :
Solar Power Prediction, Time Series Forecasting, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Renewable Energy, Deep Learning, Energy Management, AI in Forecasting, Flask Web Application.
