Presentation for crate power presentation -2025.pptx

EV PPT

1. Two Day’s National Level Virtual Conference on Recent Advances in Technology & Engineering ( CRATE-2025 ) Department of EEE Vemu Institute of Technology D.Balaji Dept. of EEE Sri Venkateshwara College of engineering Tirupati. [email protected]

2. Outline of the Research Objective Introduction Methodology System Architecture System Implementation Results and Discussion Conclusion Reference

3. To analyse the impact of environmental factors on solar power generation. . Investigate how variables such as solar irradiance, temperature, humidity, wind speed, and cloud cover influence solar energy production. To apply machine learning techniques, specifically the Random Forest algorithm, for solar power prediction Assess the effectiveness of the Random Forest model in capturing complex, non-linear relationships between weather parameters and solar power output. To pre-process and optimize historical data for accurate forecasting . Implement data cleaning, feature engineering, and hyperparameter tuning to improve model performance . To evaluate the model’s performance using key statistical metrics . Measure prediction accuracy using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. To deploy the trained model for real-time solar power prediction . Develop a scalable and automated system that integrates real-time weather data for continuous forecasting . To contribute to the advancement of renewable energy forecasting . Provide a data-driven approach to improving solar power integration into the energy grid, enhancing efficiency and grid stability. Objectives

4. Introduction Overview of the importance of solar power in sustainable energy strategies.
Need for accurate solar power output prediction to ensure efficient energy management and grid stability.
Introduction to machine learning as a solution, focusing on the Random Forest algorithm.
Research objectives: improving prediction accuracy using historical weather data and machine learning techniques.

5. Methodology 1.Problem Definition: Framing solar power prediction as a regression task. Data Collection :
. Historical weather data (temperature, humidity, wind speed, solar irradiance, cloud cover).
. Solar power output data.
2. Data Pre-processing :
. Handling missing values and outliers.
. Feature engineering (time-based features, encoding categorical variables).
. Splitting dataset into training and testing sets.
3. Model Selection and Training: . Use of Random Forest algorithm due to its ability to model non-linear relationships.
. Model evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score.
. Hyperparameter tuning (number of trees, tree depth, minimum sample requirements).
4. Deployment :
Integration with real-time weather data.
Continuous monitoring and retraining for improved accuracy.

6. System Architecture Components of the Solar Power prediction System:
1.Data Collection: Weather data sources and solar power production records.
2.Data Pre-processing: Cleaning, normalization, transformation.
3.Feature Engineering: Extracting relevant statistical patterns.
4.Machine Learning Model: Training, evaluation, and selection.
5.Model Deployment: Live prediction system and continuous updates.

7. System implementation Technical Overview: . Centralized database for data storage.
. ETL (Extract, Transform, Load) pipeline for automated data processing.
. Model development in Python with hyperparameter tuning.
. Deployment through RESTful API for real-time prediction. Containerization and Cloud Deployment: . Use of Docker for system consistency.
. Hosting on cloud platforms like AWS or Google Cloud. Monitoring and Continuous Learning: . Real-time tracking of model performance.
. Automated retraining pipeline for model updates. User Interface: . Dashboard for visualization and analytics.
. Reports for stakeholders.

8. Results and Distribution Performance Evaluation :
. Statistical metrics (MAE, RMSE, R² score).
. Comparison with other models like Linear Regression. Data Analysis: . Weather parameters vs. solar power production correlation.
. Time-series analysis and 2D distributions. Findings :
. Effectiveness of Random Forest in improving prediction accuracy.
. Limitations and potential improvements.

9. index Date-Hour (NMT) Windspeed Sunshine Air Pressure Radiation Air Temperature RelativeAirHumidity System Production 01.01.2024-00:00 0.6 1003.8 -7.4 0.1 97 0.0 1 01.01.2024-01:00 1.7 1003.5 -7.4 -0.2 98 0.0 2 01.01.2024-02:00 0.6 1003.4 -6.7 -1.2 99 0.0 3 01.01.2024-03:00 2.4 1003.3 -7.2 -1.3 99 0.0 4 01.01.2024-04:00 4.0 1003.1 -6.3 3.6 67 0.0 5 01.01.2024-05:00 1.4 1003.1 -6.8 1.5 74 0.0 6 01.01.2024-06:00 1.4 1003.7 -7.0 0.4 79 0.0 7 01.01.2024-07:00 1.3 1003.9 -7.0 -0.9 81 0.0 8 01.01.2024-08:00 0.6 1004.3 -6.6 -1.0 77 0.0 9 01.01.2024-09:00 0.6 1004.8 -6.5 -2.0 81 0.0 10 01.01.2024-10:00 0.4 1005.2 -0.8 -1.9 80 0.0 11 01.01.2024-11:00 1.1 46 1005.8 49.7 -0.2 74 215.8333 12 01.01.2024-12:00 1.2 60 1006.1 138.7 1.8 72 831.6667 13 01.01.2024-13:00 0.4 59 1006.1 121.6 2.7 66 349.5 14 01.01.2024-14:00 4.0 55 1006.5 77.0 5.0 49 272.0 15 01.01.2024-15:00 2.7 26 1007.6 33.1 4.8 48 130.2083 Previous data for analysis

10. Distributions

11. 2-d distributions

12. Time Series

13. Values

14. Conclusion The implementation of a system to predict solar power output using the Random Forest algorithm demonstrates a comprehensive and sophisticated approach to harnessing machine learning for renewable energy forecasting. This process begins with meticulous data collection and pre-processing, where weather data is transformed into a format suitable for model training, ensuring that the model captures the nuanced relationships between weather conditions and solar power generation. By leveraging the Random Forest algorithm, known for its ability to model complex, non-linear relationships, the system achieves high accuracy in predicting solar power output, which is further enhanced through hyperparameter tuning and the continuous retraining of the model with new data.

15. Reference Elsaraiti , M., & Merabet , A. (2022). Solar power forecasting using deep learning techniques. IEEE access.
Kim, S. G., Jung, J. Y., & Sim, M. K. (2019). A two-step approach to solar power generation prediction based on weather data using machine learning. Sustainability.
Lee, C. H., Yang, H. C., & Ye, G. B. (2021). Predicting the performance of solar power generation using deep learning methods. Applied Sciences. Sedai , A., Dhakal , R., Gautam , S., Dhamala , A., Bilbao, A., Wang, Q., … & Pol, S. (2023). Performance analysis of statistical, machine learning and deep learning models in long-term forecasting of solar power production. Forecasting.
Chang, R., Bai, L., & Hsu, C. H. (2021). Solar power generation prediction based on deep learning. Sustainable energy technologies and assessments. AlKandari , M., & Ahmad, I. (2024). Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Applied Computing and Informatics. Anuradha , K., Erlapally , D., Karuna , G., Srilakshmi , V., & Adilakshmi , K. (2021). Analysis of solar power generation forecasting using machine learning techniques.
Phan, Q. T., Wu, Y. K., Phan, Q. D., & Lo, H. Y. (2022). A novel forecasting model for solar power generation by a deep learning framework with data preprocessing and postprocessing . IEEE Transactions on Industry Applications. Moosa , A., Shabir , H., Ali, H., Darwade , R., & Gite , B. (2018, June). Predicting solar radiation using machine learning techniques. In 2018 second international conference on intelligent computing and control systems (ICICCS).

16. Thank you