| Autor: |
Kumar R. Dhilip, K Prakash, Sundari P. Abirama, S. Sathya |
| Jazyk: |
English<br />French |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
E3S Web of Conferences, Vol 387, p 04003 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202338704003 |
| Popis: |
The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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