Long-Term Prediction of Solar Panel Power Output with Artificial Intelligence Techniques
DOI:
https://doi.org/10.5281/zenodo.15127720Anahtar Kelimeler:
long term solar pv panel forecasting, deep learning (LSTM), sustainable energy systemsÖzet
The increasing global population and unsustainable energy consumption have led to a growing energy demand, making it imperative to predict future energy requirements and devise proactive strategies. Among renewable energy sources, solar energy stands out as a clean, eco-friendly, and readily accessible option, facilitating the integration of renewable energy into power grids. To ensure successful grid operation, efficient energy management, and economic planning, the development of an optimal solar photovoltaic (PV) power forecasting technique has become critical. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Numerical Weather Prediction (NWP), Artificial Neural Networks (ANN), and hybrid artificial intelligence approaches, are often inadequate for long-term PV power output predictions. While short-term forecasting may suffice for small or standalone PV systems, large-scale PV systems integrated into power grids require reliable long-term predictions for effective management and operation. The increasing complexity of grid-integrated renewable energy systems further emphasizes the need for advanced forecasting methodologies capable of providing accurate and long-term predictions. This study addresses this critical challenge by employing a deep learning-based Long Short-Term Memory (LSTM) artificial intelligence model to forecast long-term PV power outputs. Unlike existing approaches, this research introduces a novel model utilizing the Nadam optimizer, which enhances performance on time-series data. In our study, we utilized single-layer, three-layer, and four-layer LSTM models to predict the power output of solar panels. Additionally, we experimented with ReLU and Leaky ReLU activation functions across all model configurations. To evaluate performance, we employed several metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). By leveraging this innovative approach, the proposed LSTM model delivers improved accuracy and reliability in long-term solar PV power forecasting, offering valuable insights for grid operators and energy planners.
Referanslar
Abdel-Nasser, M., Mahmoud, K., 2019. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications, 31(7): 2727–2740.
Anonymous, 2025a. Our World in Data. (https://ourworldindata.org/), (Access Date: 01.01.2025).
Anonymous, 2025b. Türkiye Electricity Production-Transmission Statistics. (https://www.teias.gov.tr/turkiye-elektrik-uretim-iletim-istatistikleri), (Access Date: 01.01.2025).
Badwawi, R., Al Abusara, M., Mallick, T.K., 2015. A Review of hybrid solar pv and wind energy system. Smart Science, 3(3): 127–138.
Bou-Rabee, M., Sulaiman, S.A., Saleh, M.S., Marafi, S., 2017. Using artificial neural networks to estimate solar radiation in Kuwait. Renewable and Sustainable Energy Reviews, 72: 434–438.
Cai, T., Duan, S., Chen, C., 2010. Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement. 2nd International Symposium on Power Electronics for Distributed Generation Systems PEDG 2010, pp. 773–777.
Chen, C., Duan, S., Cai, T., Liu, B., 2011. Online 24-h solar power forecasting based on weather type classification using artificial neural network. Solar Energy, 85(11): 2856–2870.
Dandil, E., Gürgen, E., 2017. Prediction of photovoltaic panel power output using artificial neural networks learned by heuristic algorithms: A comparative study. 2nd International Conference on Computer Science and Engineering UBMK 2017, pp. 397–402.
Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N., 2013. Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable and Sustainable Energy Reviews, 27: 65–76.
Gök, O.A., Yildiz, C., Şekkeli̇, M., 2019. Short-term solar power plant production forecasting using artificial neural networks: Kahramanmaraş case study. International Journal of Eastern Anatolia Science Engineering and Design, 1(2): 186–195.
Gutiérrez, L., Patiño, J., Duque-Grisales, E., 2021. A Comparison of the performance of supervised learning algorithms for solar power prediction. Energies, 14(15): 4424.
Hocaoǧlu, F.O., Gerek, Ö.N., Kurban, M., 2008. Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Solar Energy, 82(8): 714–726.
Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359–366.
Khatib, T., Mohamed, A., Sopian, K., Mahmoud, M., 2012. Assessment of artificial neural networks for hourly solar radiation prediction. International Journal of Photoenergy, 2012(1): 946890.
Kim, J.G., Kim, D.H., Yoo, W.S., Lee, J.Y., Kim, Y.B., 2017. Daily prediction of solar power generation based on weather forecast information in Korea. IET Renewable Power Generation, 11(10): 1268–1273.
Kou, J., Liu, J., Li, Q., Fang, W., Chen, Z., Liu, L., Guan, T., 2013. Photovoltaic power forecasting based on artificial neural network and meteorological data. IEEE Region 10 Annual International Conference, Proceedings / TENCON.
Kudo, M., Takeuchi, A., Nozaki, Y., Endo, H., Sumita, J., 2009. Forecasting electric power generation in a photovoltaic power system for an energy network. Electrical Engineering in Japan, 167(4): 16–23.
Kumar, K.P., Saravanan, B., 2017. Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review. Renewable and Sustainable Energy Reviews, 71: 348–358.
Linares-Rodríguez, A., Ruiz-Arias, J.A., Pozo-Vázquez, D., Tovar-Pescador, J., 2011. Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks. Energy, 36(8): 5356–5365.
Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G., 2009. Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1): 2–10.
McCulloch, W.S., Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4): 115–133.
Mellit, A., Kalogirou, S.A., 2008. Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science, 34(5): 574–632.
Mellit, A., Pavan, A.M., 2010. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy, 84(5): 807–821.
Mirzapour, F., Lakzaei, M., Varamini, G., Teimourian, M., Ghadimi, N., 2019. A new prediction model of battery and wind-solar output in hybrid power system. Journal of Ambient Intelligence and Humanized Computing, 10(1): 77–87.
Paulin, B.J., Praynlin, E., 2016. Solar photovoltaic output power forecasting using back propagation neural network. ICTACT Journal on Soft Computing, 6(2): 1144–1152.
Prokop, L., Mišák, S., Novosád, T., Krömer, P., Platoš, J., Snášel, V., 2012. Photovoltaic Power plant output estimation by neural networks and fuzzy inference. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7435 LNCS, pp. 810–817.
Qasrawi, I., Awad, M., 2015. Prediction of the power output of solar cells using neural networks: solar cells energy sector in palestine. Mohammad Awad International Journal of Computer Science and Security (IJCSS), 9: 280.
Rana, M., Koprinska, I., Agelidis, V.G., 2015. Forecasting solar power generated by grid connected PV systems using ensembles of neural networks. Proceedings of the International Joint Conference on Neural Networks, September.
Shi, J., Lee, W.J., Liu, Y., Yang, Y., Wang, P., 2012. Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Transactions on Industry Applications, 48(3): 1064–1069.
Su, Y., Chan, L.C., Shu, L., Tsui, K.L., 2012. Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems. Applied Energy, 93: 319–326.
Voyant, C., Muselli, M., Paoli, C., Nivet, M.L., 2012. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy, 39(1): 341–355.
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.L., Paoli, C., Motte, F., and Fouilloy, A., 2017. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105: 569–582.
Vrettos, E., Kara, E.C., Stewart, E.M., and Roberts, C., 2019. Estimating PV power from aggregate power measurements within the distribution grid. Journal of Renewable and Sustainable Energy, 11(2): 23707.
Wang, F., Mi, Z., Su, S., Zhang, C., 2011. A practical model for single-step power prediction of grid-connected PV plant using artificial neural network. 2011 IEEE PES Innovative Smart Grid Technologies, ISGT Asia 2011 Conference: Smarter Grid for Sustainable and Affordable Energy Future.
Wang, S., Yu, L., Tang, L., Wang, S., 2011. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy, 36(11): 6542–6554.
Yang, H.T., Huang, C.M., Huang, Y.C., Pai, Y.S., 2014. A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Transactions on Sustainable Energy, 5(3): 917–926.
Zhang, N., Behera, P., Williams, C., 2013. Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks. SysCon 2013 - 7th Annual IEEE International Systems Conference, Proceedings Book, pp. 280–286.
Zhu, H., Li, X., Sun, Q., Nie, L., Yao, J., Zhao, G., 2015. A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks. Energies, 9(1): 11.
İndir
Yayınlanmış
Nasıl Atıf Yapılır
Sayı
Bölüm
Lisans
Telif Hakkı (c) 2025 EJONS Uluslararası Matematik, Mühendislik ve Doğa Bilimleri Dergisi
Bu çalışma Creative Commons Attribution-NonCommercial 4.0 International License ile lisanslanmıştır.
