Analysis of Solar Power Prediction Employs Linear Regression, Random Forest Regression, Decision Tree And Neural Network

Danial Zulfiqar; Marina Artiyasa; Irvan Syah Riadi; Robby Ardiansah

doi:10.2991/978-94-6463-406-8_6

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Analysis of Solar Power Prediction Employs Linear Regression, Random Forest Regression, Decision Tree And Neural Network

Authors

Danial Zulfiqar¹^{, *}, Marina Artiyasa², Irvan Syah Riadi³, Robby Ardiansah⁴

¹Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia

²Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia

³Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia

⁴Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia

^*Corresponding author. Email: danial.zulfiqar_te20@nusaputra.ac.id

Corresponding Author

Danial Zulfiqar

Available Online 13 May 2024.

DOI: 10.2991/978-94-6463-406-8_6 How to use a DOI?
Keywords: —Solar energy; Linear regression; Random forest regression; Decision tree; Neural network
Abstract: Solar energy is increasingly being used and developed because it is easy to build and maintain. In addition, solar energy is also used because fossil energy is increasingly depleted, carbon dioxide emissions are increasing and the greenhouse effect is becoming more and more worrying. Solar energy is a fairly popular alternative to several renewable energies used as energy suppliers for local grid systems. However, when using solar energy, there are still hurdles that need to be taken into account as the voltage value generated from solar energy varies greatly and is difficult to calculate. Therefore, solar panel energy forecast analysis is very important and the choice of method used must also have minimal errors. In this research, we use four supervised machine learning methods to predict solar panel energy. The results of this research show that decision trees can predict solar panel energy data better than random forest regression, linear regression and neural network methods., with an MAE percentage of 0.11%, RMSE 0.86% and MAPE 0.00035%.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2nd International Conference on Consumer Technology and Engineering Innovation (ICONTENTION 2023)
Series: Advances in Engineering Research
Publication Date: 13 May 2024
ISBN: 978-94-6463-406-8
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-406-8_6 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Danial Zulfiqar
AU  - Marina Artiyasa
AU  - Irvan Syah Riadi
AU  - Robby Ardiansah
PY  - 2024
DA  - 2024/05/13
TI  - Analysis of Solar Power Prediction Employs Linear Regression, Random Forest Regression, Decision Tree And Neural Network
BT  - Proceedings of the 2nd International Conference on Consumer Technology and Engineering Innovation (ICONTENTION 2023)
PB  - Atlantis Press
SP  - 23
EP  - 28
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-406-8_6
DO  - 10.2991/978-94-6463-406-8_6
ID  - Zulfiqar2024
ER  -

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