Prediction of Solar Power Using Linear Regression-Reference-Cited by-同舟云学术

Prediction of Solar Power Using Linear Regression

Published:2022 Issue: Volume: Page:709-718
ISSN:2662-6829
Container-title:Advances in Sustainability Science and Technology
language:
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Author:

Thombare Shubham,Pande V. N.,Kulkarni R. S.,Kakade S. M.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-9033-4_53

Reference11 articles.

1. Wang Z (2019) Solar power forecasting. Thesis submitted for the degree of Doctor of Philosophy in the School of Computer Science at The University of Sydney

2. Khan A, Bhatnagar R, Masrani V, Lobo VB (2020) A comparative study on solar power forecasting using ensemble learning. In: 2020 4th international conference on trends in electronics and informatics (ICOEI)(48184), 2020, pp 224–231. https://doi.org/10.1109/ICOEI48184.2020.9142884

3. Carrera B, Kim K (2020) Comparison analysis of machine learning techniques for photovoltaic prediction using weather sensor data. 1 June 2020

4. Prakash A, Singh SK (2014) Towards an efficient regression model for solar energy prediction. In: 2014 innovative applications of computational intelligence on power, energy and controls with their impact on humanity (CIPECH), 2014, pp 18–23. doi: https://doi.org/10.1109/CIPECH.2014.7019040

5. Xu Z, Yang G, Geng H (2019) Power forecasting of photovoltaic generation based on multiple linear regression method with real-time correction term. In: 2019 22nd international conference on electrical machines and systems (ICEMS), 2019, pp 1–4. https://doi.org/10.1109/ICEMS.2019.8922096

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