Day-ahead Photovoltaic Power Production Forecasting Using a Hybrid Artificial Neural Network Model Integrated with Metaheuristic Algorithms

Oguz Tasdemir, Mehmet Yesilbudak, Erdal Irmak

Abstract


The escalating global energy demands and the environmental repercussions of fossil fuel utilization have given rise to a marked increase in the level of interest in renewable energy sources. Solar energy, in particular, is distinguished by its abundance and minimal environmental impact. This study sets out to compare three distinct hybrid models that are designed to enhance the forecasting accuracy of daily photovoltaic power prediction: JAYA-ANN, GA-ANN and PSO-ANN. The models were developed and tested using historical data on PV power output, including air temperature, PM10 levels, and solar irradiance. The study’s findings indicated that the JAYA-ANN hybrid model exhibited superior performance, with a Mean Absolute Percentage Error (MAPE) of 7.38% and a Root Mean Squared Error (RMSE) of 681.71 kW for the test subset. The JAYA-ANN model demonstrated superior performance in comparison to both GA-ANN and PSO-ANN models. On the basis of the entire dataset, the JAYA-ANN model exhibited the highest level of prediction accuracy, with an MAPE of 11.59% and an RMSE of 413.91 kW. The study confirms that the JAYA-ANN hybrid model serves as an effective tool for photovoltaic power estimation. Beyond this, it offers noteworthy opportunities to advance the integration of solar resources into the energy sector while maintaining grid stability through enhanced forecasting accuracy.

Keywords


Forecast; photovoltaic power; jaya optimization algorithm; particle swarm optimization; genetic algorithm; artificial neural networks

Full Text:

PDF

References


A. Ebrahimi, B. Ghorbani and M. Taghavi, “Novel integrated structure consisting of CO2 capture cycle, heat pump unit, Kalina power, and ejector refrigeration systems for liquid CO2 storage using renewable energies”, Energy Science and Engineering, vol. 10, no. 8, pp. 3167-3188, 2022. DOI: 10.1002/ese3.1211.

B. Ghorbani, G. Salehi, A. Ebrahimi and M. Taghavi, “Energy, exergy and pinch analyses of a novel energy storage structure using post-combustion CO2 separation unit, dual pressure Linde-Hampson liquefaction system, two-stage organic Rankine cycle and geothermal energy”, Energy, vol. 233, 121051, 2021. DOI: 10.1016/j.energy.2021.121051.

H.N. Durmus Senyapar and R. Bayindir, “AI-driven smart grid solutions for energy justice: Integrating technical efficiency with inclusive social welfare policy design”, International Journal of Smart Grid, vol. 9, no. 3, pp. 105-115, 2025. DOI: 10.20508/ijsmartgrid.v9i3.426.g400.

E. Irmak, M. Yesilbudak and O. Tasdemir, “Daily prediction of PV power output using particulate matter parameter with artificial neural networks”, 11th International Conference on Smart Grid, pp. 499-502, 04-07 June 2023, Paris, France. DOI: 10.1109/icSmartGrid58556.2023.10171103.

A. Ziane, A. Necaibia, N. Sahouane, R. Dabou, M. Mostefaoui, A. Bouraiou, S. Khelifi, A. Rouabhia and M. Blal, “Photovoltaic output power performance assessment and forecasting: Impact of meteorological variables”, Solar Energy, vol. 220, pp. 745-757, 2021. DOI: 10.1016/j.solener.2021.04.004.

N. Li, L. Li, F. Zhang, T. Jiao, S. Wang, X. Liu and X. Wu, “Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model”, Energy, vol. 277, 127557, 2023. DOI: 10.1016/j.energy.2023.127557.

M.O. Moreira, P.P. Balestrassi, A.P. Paiva, P.F. Ribeiro and B.D. Bonatto, “Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting”, Renewable and Sustainable Energy Reviews, vol. 135, 110450, 2021. DOI: 10.1016/j.rser.2020.110450.

Y. Jung, J. Jung, B. Kim and S. Han, “Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea”, Journal of Cleaner Production, vol. 250, 119476, 2020. DOI: 10.1016/j.jclepro.2019.119476.

Ü. A?bulut, A.E. Gürel, A. Ergün and I. Ceylan, “Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms”, Journal of Cleaner Production, vol. 268, 122269, 2020. DOI: 10.1016/j.jclepro.2020.122269.

D. Korkmaz, “SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting”, Applied Energy, vol. 300, 117410, 2021. DOI: 10.1016/j.apenergy.2021.117410.

E. Irmak, M. Yesilbudak and O. Tasdemir, “Enhanced PV power prediction considering PM10 parameter by hybrid JAYA-ANN model”, Electric Power Components and Systems, vol. 52, no. 11, pp. 1-10, 2024. DOI: 10.1080/15325008.2024.2322668.

D. Lee and K. Kim, “PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information”, Renewable Energy, vol. 173, pp. 1098-1110, 2021. DOI: 10.1016/j.renene.2020.12.021.

H. Zhen, D. Niu, K. Wang, Y. Shi, Z. Ji and X. Xu, “Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information”, Energy, vol. 231, 120908, 2021. DOI: 10.1016/j.energy.2021.120908.

A. Agga, A. Abbou, M. Labbadi and Y.E. Houm, “Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models”, Renewable Energy, vol. 177, pp. 101-112, 2021. DOI: 10.1016/j.renene.2021.05.095.

C. Scott, M. Ahsan and A. Albarbar, “Machine learning for forecasting a photovoltaic (PV) generation system”, Energy, vol. 278, 2023. DOI: 10.1016/j.energy.2023.127807.

Y.K. Semero, J. Zhang and D. Zheng, “PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy”, CSEE Journal of Power and Energy Systems, vol. 4, pp. 210-218, 2018. DOI: 10.17775/CSEEJPES.2016.01920.

A. Mellit and S.A. Kalogirou, “Artificial intelligence techniques for photovoltaic applications: A review”, Progress in Energy and Combustion Science, vol. 34, pp. 574-632, 2008. DOI: 10.1016/j.pecs.2008.01.001.

S. Zarei, O. Bozorg-Haddad and M.R. Nikoo, “The basis of artificial neural network (ANN): Structures, algorithms, and functions”, Computational Intelligence for Water and Environmental Sciences, vol. 1043, pp. 225-250, 2022. DOI: 10.1007/978-981-19-2519-1.

R.V. Rao and A. Saroj, “Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm”, Energy, vol. 128, pp. 785-800, 2017. DOI: 10.1016/j.energy.2017.04.059.

R.V. Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems”, International Journal of Industrial Engineering Computations, vol. 7, pp. 19-34, 2016. DOI: 10.5267/j.ijiec.2015.8.004

T.V. Mathew, “Genetic Algorithm”, Report Submitted at IIT Bombay, 2012.

O. Engin and A. Figlali, “Reproduction operator optimization of genetic algorithms in flowshop scheduling problems”, ?TÜ Dergisi D: Mühendislik, vol. 1, no.1, 2002.

J.C. Bansal, P.K. Singh, M. Saraswat, A. Verma, S.S. Jadon and A. Abraham, “Inertia weight strategies in particle swarm optimization”, 3rd World Congress on Nature and Biologically Inspired Computing, pp. 633-640, 2011, Salamanca, Spain. DOI: 10.1109/NaBIC.2011.6089659.

A.M. Eltamaly and A.Y. Abdelaziz, “Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems”, Springer Cham, 2020.

G. Kavuran, “SEM-Net: Deep features selections with binary particle swarm optimization method for classification of scanning electron microscope images”, Materialstoday Communications, vol. 27, 102198, 2021. DOI: 10.1016/j.mtcomm.2021.102198.

O. Tasdemir, “Photovoltaic power prediction with teaching learning based optimization algorithm”, Gazi University Journal of Science Part A: Engineering and Innovation”, vol. 11, no. 4, pp. 780-791, 2024. DOI: 10.54287/gujsa.1581828.

M. Colak, M. Yesilbudak and R. Bayindir, “Very-short term estimation of global horizontal irradiance using data mining methods”, 7th International Conference on Renewable Energy Research and Applications, pp. 1472-1476, 14-17 October 2018, Paris, France. DOI: 10.1109/ICRERA.2018.8566747.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v9i4.550.g416

Refbacks

  • There are currently no refbacks.


www.ijsmartgrid.com; www.ijsmartgrid.org

ilhcol@gmail.com; icolak@gazi.edu.tr

Online ISSN: 2602-439X

Publisher: ilhami COLAK

https://www.ilhamicolak.org/english.htm

Cited in SCOPUS Q1, SCIMAGO, Google Scholar and CrossRef

 

 

LinkedIn Logo Follow us on LinkedIn

Google Scholar Statistics of ijSmartGrid

https://scholar.google.com/citations?user=qP9ZUKAAAAAJ&hl=tr&authuser=1 

 

SCIMAGO information:

https://www.scimagojr.com/journalsearch.php?q=21101271431&tip=sid&clean=0#google_vignette

Q2, h-index:13