Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches
- Title
- Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches
- Creator
- Sucharitha M.M.; Sowjanya S.; Sumalatha K.N.; Ayesha S.; Basha M.S.A.
- Description
- In this study, we dive into the world of renewable energy, specifically focusing on predicting solar energy output, which is a crucial part of managing renewable energy resources. We recognize that solar energy production is heavily influenced by a range of environmental factors. To effectively manage energy usage and the power grid, it's vital to have accurate forecasting methods. Our main goal here is to delve into various predictive modeling techniques, encompassing both machine learning and time series analysis, and evaluate their effectiveness in forecasting solar energy production. Our study seeks to address this by developing robust models capable of capturing these complex dynamics and providing dependable forecasts. We took a comparative route in this research, putting three different models to the test: Random Forest Regressor, a streamlined version of XGBoost, and ARIMA. Our findings revealed that both the Random Forest and XGBoost models showed similar levels of performance, with XGBoost having a slight edge in terms of RMSE.. By providing a comprehensive comparison of these different modeling techniques, our research makes a significant contribution to the field of renewable energy forecasting. We believe this study will be immensely helpful for professionals and researchers in picking the most suitable models for solar energy prediction, given their unique strengths and limitations. 2024 IEEE.
- Source
- Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 235-241.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Machine Learning approaches; Solar Energy; Time Series and ARIMA
- Coverage
- Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management Studies, Nandyal, India; Sumalatha K.N., Maris Stella College, Vijayawada, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038328-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sucharitha M.M.; Sowjanya S.; Sumalatha K.N.; Ayesha S.; Basha M.S.A., “Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19459.