E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||7|
|Published online||07 October 2021|
Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques
1 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 MTech Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
3 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
4 Asst.Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
5 Asst.Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
* Corresponding author: email@example.com
Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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