Version 1
: Received: 14 May 2024 / Approved: 14 May 2024 / Online: 15 May 2024 (10:22:21 CEST)
How to cite:
Alizadegan, H.; Radmehr, A.; Karimi, H.; Asghari Ilani, M. Solar Energy Production Forecasting: A Comparative Study of Bi-LSTM, LSTM, XGBoost Models with Activation Function Analysis. Preprints2024, 2024050994. https://doi.org/10.20944/preprints202405.0994.v1
Alizadegan, H.; Radmehr, A.; Karimi, H.; Asghari Ilani, M. Solar Energy Production Forecasting: A Comparative Study of Bi-LSTM, LSTM, XGBoost Models with Activation Function Analysis. Preprints 2024, 2024050994. https://doi.org/10.20944/preprints202405.0994.v1
Alizadegan, H.; Radmehr, A.; Karimi, H.; Asghari Ilani, M. Solar Energy Production Forecasting: A Comparative Study of Bi-LSTM, LSTM, XGBoost Models with Activation Function Analysis. Preprints2024, 2024050994. https://doi.org/10.20944/preprints202405.0994.v1
APA Style
Alizadegan, H., Radmehr, A., Karimi, H., & Asghari Ilani, M. (2024). Solar Energy Production Forecasting: A Comparative Study of Bi-LSTM, LSTM, XGBoost Models with Activation Function Analysis. Preprints. https://doi.org/10.20944/preprints202405.0994.v1
Chicago/Turabian Style
Alizadegan, H., Hossein Karimi and Mohsen Asghari Ilani. 2024 "Solar Energy Production Forecasting: A Comparative Study of Bi-LSTM, LSTM, XGBoost Models with Activation Function Analysis" Preprints. https://doi.org/10.20944/preprints202405.0994.v1
Abstract
The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.
Keywords
Time series forecasting; LSTM; Bi-LSTM; Deep learning; ML; XGBoost; Energy; Solar Energy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
