Optimizing Solar Energy Forecasting Using Deep Learning Techniques: A Study on Predictive Models in Forested Environments
DOI:
https://doi.org/10.54228/mjaret0624036Keywords:
Solar Energy Forecasting; Deep Learning; Long Short-Term Memory (LSTM); Renewable Energy; Neural Networks; Energy Prediction; Environmental Data; Forest Ecosystems; Computational Efficiency; Machine Learning Models.Abstract
Precise information about the efficiency of harvesting solar energy is an important
requirement in the application of the renewable energy on practical projects, especially where
climate is characteristically changeable. This paper focuses on the use of deep learning methods
namely LSTM to predict the efficiency of solar power in forested and other regions of the land. This
seems to be more efficient than conventional neural network like Feedforward Neural Network
(FNN) and Convolutional Neural Network (CNN) in terms of accuracy with the MAPE of 22% than
FNN and 15% than CNN. Currently, based on the results of the LSTM model the authors got 7.45%
and 20.35% improvements in terms of computational time and memory respectively when compared
with the CNN models. Based on these results, it can be concluded that deep learning techniques,
especially LSTM, are suitable when it comes to the solar power prediction considering accuracy and
practicability. The further developments of the work will be the inclusion of other environmental
data, as well as enhancing the real time prediction – leading to better sustainable energy solutions.
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2025 MJARET
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License Terms for MJARET
Creative Commons Attributio 4.0 International (CC BY) License:
This license allows for the following:
-
Sharing — Copy and redistribute the material in any medium or format.
-
Adaptation — Remix, transform, and build upon the material.
The license is subject to the following terms:
-
Attribution:
- You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- Attribution should include the citation of the article, the author's name, and a link to the original work published in MJARET.
- This must be done in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial:
- The material cannot be used for commercial purposes.
- Any use of the work intended to provide a commercial advantage or monetary compensation is considered outside the scope of this license.
-
No Additional Restrictions:
- You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
General Provisions:
- Understandability: This license can be revoked if you do not comply with its terms and conditions.
- Public Domain: Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- Other Rights:
- The license does not cover rights such as publicity, privacy, or moral rights that may affect your ability to use the material as contemplated by the license.
- Such rights might need to be considered and respected separately.
Disclaimer:
- MJARET does not provide any warranties with the work. The work is provided "as is" without any representations or warranties, express or implied. MJARET will not be liable for any damages resulting from the use of the work.
How to Cite:
- Proper attribution for use of the licensed work should follow the standard citation format provided by MJARET, which should include the author(s), the title of the work, MJARET, and the DOI link.
