Enhancing Solar Power Forecasting with Data Imputation and Machine Learning: A Comparative Study
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2025
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| _version_ | 1866901255953055744 |
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| author | Usha.N P.S. Manoharan D.Sneha Charis P.M. Devie |
| author_facet | Usha.N P.S. Manoharan D.Sneha Charis P.M. Devie |
| contents | <p><span><span>Abstract</span></span><span>—The surge in energy demand necessitates an integration of renewable sources into power grids, particularly solar energy. It focuses on handling solar yield datasets, which are subject to problems of intermittent data due to sensor failure and variability in weather conditions, using advanced imputation techniques—K-Nearest Neighbors, Linear Interpolation, and Multivariate Imputation by Chained Equations. Feature selection and dimensionality reduction methods such as the Pearson Correlation Coefficient, Principal Component Analysis, and Mutual Information add predictive capability to the optimal datasets using machine learning models like XGBoost, LSTM, CatBoost, Random Forest, and Decision Trees. CatBoost was shown to be the best at training, achieving a very high accuracy of 85.6%. This work forms a sound methodology for tackling issues in data quality and dimension reduction as far as renewable energy forecasting is concerned, taking it a step further in establishing standards in prediction for solar power.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15056491 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhancing Solar Power Forecasting with Data Imputation and Machine Learning: A Comparative Study Usha.N P.S. Manoharan D.Sneha Charis P.M. Devie Feature Selection Imputation Machine Learning Performance metrics Solar Power Prediction <p><span><span>Abstract</span></span><span>—The surge in energy demand necessitates an integration of renewable sources into power grids, particularly solar energy. It focuses on handling solar yield datasets, which are subject to problems of intermittent data due to sensor failure and variability in weather conditions, using advanced imputation techniques—K-Nearest Neighbors, Linear Interpolation, and Multivariate Imputation by Chained Equations. Feature selection and dimensionality reduction methods such as the Pearson Correlation Coefficient, Principal Component Analysis, and Mutual Information add predictive capability to the optimal datasets using machine learning models like XGBoost, LSTM, CatBoost, Random Forest, and Decision Trees. CatBoost was shown to be the best at training, achieving a very high accuracy of 85.6%. This work forms a sound methodology for tackling issues in data quality and dimension reduction as far as renewable energy forecasting is concerned, taking it a step further in establishing standards in prediction for solar power.</span></p> |
| title | Enhancing Solar Power Forecasting with Data Imputation and Machine Learning: A Comparative Study |
| topic | Feature Selection Imputation Machine Learning Performance metrics Solar Power Prediction |
| url | https://doi.org/10.5281/zenodo.15056491 |