Forecasting solar power generation with machine learning techniques

K. Muthuvel; P. Muthukumar; Thomas Thangam

Author(s)

K. Muthuvel Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education,Kumaracoil,India

P. Muthukumar Department of Electrical and Electronics Engineering, Prasad V. Potluri Siddhartha Institute of Technology,Vijayawada,India

Thomas Thangam Department of Process Engineering, International Maritime College of Oman, National University of Science and Technology,Sohar, Sultanate of,Oman

Abstract

This study underscores the economic and environmental advantages of integrating solar energy into power systems. The unpredictable nature of solar power poses challenges to system operation and planning. To ensure the economic sustainability of newly constructed systems, precise forecasting of Photovoltaic (PV) system effectiveness and energy output is crucial. Addressing variations in solar power consumption, this work presents an enhanced Machine Learning (ML) model. Utilizing Python, the study explores Linear Regressor, Random Forest (RF) Regressor, XGBoost Regression, K-Nearest Neighbor (KNN) Regressor, and AdaBoost Regressor approaches, all proving effective in predicting electricity production. Results highlight the superior performance of ML algorithms over traditional time series methods and two baseline models, emphasizing their efficacy in solar power forecasting.

Publication Details

Page(s) 1378-1388

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 22, Year: 2024

Request the full text from PASTIC

Keywords

PV system, Machine learning, time-series, XGBoost regressor, adaboost regressor

References

Dairi A.,Harrou F.,Sun Y.,Khadraoui S. .2022 .Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach. Applied Sciences, 10(23) : 8400.

Park S.,Kim Y.,Ferrier N. J.,Collis S. M.,Sankaran R.,Beckman P. H. .2021 .Prediction of solar irradiance and photovoltaic solar energy product based on cloud coverage estimation using machine learning methods. Atmosphere, 12(3) : 395.

Li Z.,Rahman S. M. M. Z.,Vega R.,Dong B. .2016 .A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting. Energies, 9(1) : 55.

Bosman L. B.,Leon-Salas W. D. L. B.,Hutzel W.,Soto E. A. .2020 .PV system predictive maintenance: Challenges, current approaches, and opportunities. Energies, 13(6) : 1398.

Al-Shetwi A. Q.,Hannan M. A.,Jern K. P.,Alkahtani A. A.,Abas A. E. P. G. .2020 .Power quality assessment of grid-connected PV system in compliance with the recent integration requirements. Electronics, 9(2) : 366.

Dairi A.,Harrou F.,Sun Y.,Khadraoui S. .2020 .Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach. Applied Sciences, 10(23) : 8400.

Sorkun M. C.,Incel O. D. M. C.,Paoli C. .2020 .Time series forecasting on multivariate solar radiation data using deep learning (LSTM). Turkish Journal of Electrical Engineering and Computer Sciences, 28(1) : 211-223.

Lee W.,Kim K.,Park J.,Kim J.,Kim Y. .2018 .Forecasting solar power using long-short term memory and convolutional neural networks. IEEE Access. 6, : 73068-73080.

Suresh V.,Janik P.,Rezmer J.,Z. J. .2020 .Forecasting solar PV output using convolutional neural networks with a sliding window algorithm. Energies, 13(3) : 723.

Polo J.,Martín-Chivelet N.,Alonso-Abella M.,Cuenca SanzSaiz C.,de la Cruz J,M. J .2023 .Exploring the PV Power forecasting at building façades using gradient boosting methods. Energies, 16(3) : 1495.

Huang C. J.,Kuo P. H. .2019 .Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access. 7, : 74822-74834.

Alsharif M. H.,Younes M. K.,Kim J. .2019 .Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry, 11(2) : 240.

Sharadga H.,Hajimirza S.,Balog R. S. .2020 .Time series forecasting of solar power generation for largescale photovoltaic plants. Renewable Energy, 150 : 797-807.

Belmahdi B.,Louzazni M.,Bouardi A. E. .2020 .A hybrid ARIMA-ANN method to forecast daily global solar radiation in three different cities in Morocco. The European Physical Journal Plus. 135, : 1-23.

Louzazni M.,Mosalam H.,Cotfas D. T. .1953 .Forecasting of photovoltaic power using non-linear auto-regressive exogenous artificial neural network and time series analysis. Electronics, 10(16) : .

Wan C.,Zhao J.,Song Y.,Xu Z.,Lin J.,Z. J. .2015 .Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems, 1(4) : 38-46.

Khodayar M.,Mohammadi S.,Khodayar M. E.,Wang J.,Liu G. .2019 .Convolutional graph autoencoder: A generative deep neural network for probabilistic spatiotemporal solar irradiance forecasting. IEEE Trans. on Sustainable Energy, 11(2) : 571-583.

de Santos D ,de Oliveira J. F.,Siqueira H. V.,Barchi T. M.,Lima A. R.,Dantas D. A.,Converti A.,Pereira A. C.,de Melo Filho J. B A. C. .2022 .Solar irradiance forecasting using dynamic ensemble selection. Applied Sciences, 12(7) : 3510.

Huang H.,Karami H.,Ehteram M.,Chau K. W.,Zhang Q. .2022 .Solar radiation prediction using improved soft computing models for semi-arid, slightly arid, and humid climates. Alexandria Engineering Journal, 61(12) : 10631-10657.

Alharbi F. R.,Csala D. .2021 .Wind speed and solar irradiance prediction using a bidirectional long shortterm memory model based on neural networks. Energies, 14(20) : 6501.

Zafar R.,Vu B. H.,Husein M.,Chung I. Y. .2021 .Day-ahead solar irradiance forecasting using a hybrid recurrent neural network with weather classification for power system scheduling. Applied Sciences, 11(15) : 6738.

Hosseini M.,Katragadda S.,Wojtkiewicz J.,Gottumukkala R.,Maida A.,Chambers T. L. .2020 .Direct normal irradiance forecasting using multivariate gated recurrent units. Energies, 13(15) : 3914.

Selvaraj T.,Rengaraj R.,Venkatakrishnan G.,Soundararajan S. G.,Natarajan K.,Balachandran P. K.,Selvarajan David P. W.,S. David P. W. .2022 .Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks. International Transactions on Electrical Energy Systems., : .

Natarajan K.,Sampath V. .2020 .Fault detection of solar PV system using SVM and thermal image processing. International Journal of Renewable Energy Research (IJRER), 10(2) : 967-977.

Abdelhag M. E.,Amin S. T.,Ali A. .2022 .Machine Learning Based Model for Solar Radiation Prediction. Machine Learning, 54(02) : .

Babbar S. M.,Lau C. Y.,Thang K. F. .2021 .LongTerm Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model. International Journal of Advanced Computer Science and Applications, 12(11) : .

Li X.,Chen P.,Xu H.,Xing Q.,Yan J.,Lu S.,Fan H.,Yang L,Cheng Y. .2022 .Probabilistic solar irradiance forecasting based on XGBoost. Energy Reports. 8, : 1087-1095.

Garniwa P. M. P.,Ramadhan R. A. A.,Lee H. J. .2021 .Application of semi-empirical models based on satellite images for estimating solar irradiance in Korea. Applied Sciences, 11(8) : 3445.

Yeom J. M.,Park S.,Chae T.,Kim J. Y.,Lee C. S. .2082 .Spatial assessment of solar radiation by machine learning and deep neural network models using data provided by the COMS MI geostationary satellite: A case study in South Korea. Sensors, 19(9) : .