Optimized LSTM Neural Networks Model applied for Solar PV Power Prediction

Itto OUZOUHOU; Abdellah LAAZIZI; Khalid  Kandoussi; Rabie El Otmani

doi:10.51646/jsesd.v15iMME.340

Authors

Itto OUZOUHOU SECNDCM-L2MC, ENSAM, Moulay Ismail University, Meknes, Morocco https://orcid.org/0000-0002-5724-4212
Abdellah LAAZIZI SECNDCM-L2MC, ENSAM, Moulay Ismail University, Meknes, Morocco https://orcid.org/0000-0002-5053-0830
Khalid Kandoussi Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco
Rabie El Otmani https://orcid.org/0000-0002-8181-6233

DOI:

https://doi.org/10.51646/jsesd.v15iMME.340

Keywords:

Photovoltaic power, , prediction, , LSTM neural networks, , Weighted Linear Regression.

Abstract

Energy and climate challenges have driven significant growth in solar power generation. However, solar power production is intermittent and unstable, which complicates its integration into power grids. Techniques of forecasting Photovoltaic (PV) energy are needed to ensure its security and cost-effectiveness. This paper deals with the use of artificial intelligence techniques to predict photovoltaic power. The proposed techniques are Weighted Linear Regression (WLR) and Long Short-Term Memory neural networks (LSTM). The investigated data in this study was obtained from a photovoltaic solar power plant located in a specific geographical area. The studied parameters are wind speed (WS), ambient temperature (T), relative humidity (RH), irradiance (GHI) and wind direction (WD). The accuracy and ability of the LSTM model to explain data variation were examined by comparing its predictions with those of the WLR model, based on three performance measures: RMSE, MAE and R2. The obtained results show that weighted linear regression produces an acceptable estimation of photovoltaic power. However, the LSTM model performs better and has the potential to improve forecast reliability and accuracy.

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References

E. Rynska, “Review of PV Solar Energy Development 2011–2021 in Central European Countries,” Energies, vol. 15, no. 21, p. 8307, 2022.Doi:doi.org/10.3390/en15218307

L. B. Chilakapati and T. G. Manohar, "Power Quality Enhancement in a Grid-Integrated Solar-PV System with a Hybrid UPQC Control Strategy," J. Solar Energy and Sustainable Development, J. vol. 13, no. 2, pp. 120–137, 2024.

Liu, L., Zhao, Y., Chang, D., Xie, J., Ma, Z., Sun, Q., ... & Wennersten, R., “Prediction of short-term PV power output and uncertainty analysis,” J. Applied Energy, vol. 228, pp. 700 711,2018.

L. P. S. S. Panagoda, R. A. H. T. Sandeepa, W. A. V. T. Perera, D. M. I. Sandunika, S. M. G. T. Siriwardhana, M. K. S. D. Alwis, and S. H. S. Dilka, “Advancements in Photovoltaic (PV) Technology for Solar Energy Generation,” J. Research Technology & Engineering, vol. 4, no. 30, pp. 30–72, 2023.Doi:https://doi.org/10.1016/j.apenergy.2018.06.112.

V. Devabhaktuni, M. Alam, S. S. S. R. Depuru, R. C. Green II, D. Nims, and C. Near, “Solar Energy: Trends and Enabling Technologies,” J. Renewable and Sustainable Energy Reviews, vol. 19, pp. 555–564, 2013. Doi:doi.org/10.1016/j.rser.2012.11.024

A. Jäger-Waldau, “Snapshot of Photovoltaics− May 2023”, EPJ Photovoltaics, vol. 14, pp. 23, 2023. Doi: doi.org/10.1051/epjpv/2023016

J. Batalla-Bejerano and E. Trujillo-Baute, “Impacts of intermittent renewable generation on electricity system costs”, J. Energy Policy, vol. 94, pp. 411-420, 2016. Doi:https://doi.org/10.1016/j.enpol.2015.10.024.

M.H. Albadi, “Solar PV Power Intermittency and its Impacts on Power Systems—An Overview”, J. Engineering Research, vol. 16, no. 2, 2019. Doi: doi.org/10.24200/tjer.vol16iss2pp142-150.

G. Li, H. Wang, S. Zhang, J. Xin, and H. Liu, “Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach,” J. Energies, vol. 12, no. 13, p. 2538, 2019. Doi:doi.org/10.3390/en12132538.

I. Ali, H. Hadiyanto, and A. Y. Wardaya, "Performance Analysis and Techno-Economic Evaluation of Solar Energy Retrofitting for Coal-Fired Power Plant in Central Kalimantan Province," J. Solar Energy and Sustainable Development, vol. 13, no. 2, pp. 138–150, 2024. Doi:doi.org/10.51646/jsesd.v13i2.205.

S. Saeed and T. Siraj, "Global Renewable Energy Infrastructure: Pathways to Carbon Neutrality and Sustainability," J. Solar Energy and Sustainable Development, vol. 13, no. 2, pp. 183–203, 2024. Doi:doi.org/10.51646/jsesd.v13i2.243.

K.J. Iheanetu, “Solar Photovoltaic Power Forecasting: A Review”, J. Sustainability, vol. 14, no. 24, p. 17005, 2022. Doi:doi.org/10.3390/su142417005.

A. El Hendouzi and A. Bourouhou, “Solar Photovoltaic Power Forecasting,” J. Electrical and Computer Engineering, vol. 2020, no. 1, p. 8819925, 2020.

P. Gupta, R. Singh, "PV power forecasting based on data-driven models: a review," int. J. Sustainable Engineering, vol. 14, no. 6, pp. 1733-1755, 2021.Doi:doi.org/10.1080/19397038.2021.1986590.

Al Hazza, M., Attia, H., & Hossin, K., “Solar photovoltaic power prediction using a statistical approach based on analysis of variance,” J. Solar Energy and Sustainable Development, vol. 13, no. 2, pp. 45-61, 2024. Doi: doi.org/10.51646/jsesd.v13i2.181.

A. M. Kuriakose, D. P. Kariyalil, M. Augusthy, S. Sarath, J. Jacob, and N. R. Antony, “Comparison of Artificial Neural Network, Linear Regression, and Support Vector Machine for Prediction of Solar PV Power,” in 2020 IEEE Pune Section International Conference (PuneCon), pp. 1–6, Dec.2020.Doi:10.1109/PuneCon50868.2020.9362442.

B. Singh and D. Pozo, "A guide to solar power forecasting using ARMA models," in 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), IEEE, 2019,pp.1-4. Doi: 10.1109/ISGTEurope.2019.8905430.

B. Singh and D. Pozo, “A Guide to Solar Power Forecasting Using ARMA Models,” in 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), pp. 1–4, Sep. 2019, IEEE. Doi: 10.1109/ISGTEurope.2019.8905430.

S. Atique, S. Noureen, V. Roy, V. Subburaj, S. Bayne, and J. Macfie, “Forecasting of Total Daily Solar Energy Generation Using ARIMA: A Case Study,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0114–0119, Jan. 2019, IEEE.Doi:10.1109/CCWC.2019.8666481.

L. Fara, A. Diaconu, D. Craciunescu, and S. Fara, “Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models,” Int. J. Photoenergy, vol. 2021, no. 1, p. 6777488,2021. Doi:doi.org/10.1155/2021/6777488.

Y. Tanoto, G. S. Budhi, and J. C. Widjaya, “Time Series Forecasting for Daily to Monthly Temporal Hourly-based Solar PV Output Power,” in 2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 519–523, Dec. 2023, IEEE. Doi: 10.1109/ISRITI60336.2023.10467972.

Y. Li, Y. Su and L. Shu, “An ARMAX model for forecasting the power output of a grid-connected photovoltaic system”, J. Renewable Energy, vol. 66, pp. 78-89, 2014.Doi:doi.org/10.1016/j.renene.2013.11.067.

K. J. Iheanetu, “Solar Photovoltaic Power Forecasting: A Review,” J. Sustainability, vol. 14, no. 24, p. 17005, 2022. Doi: doi.org/10.3390/su142417005.

K, Wang., Qi, X., & Liu, H., “A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network,” J. Applied Energy, vol. 251, p. 113315,2019.Doi:doi.org/10.1016/j.apenergy.2019.113315.

U. K. Das, K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M. Y. I. Idris, W. Van Deventer, et al., “Forecasting of Photovoltaic Power Generation and Model Optimization: A Review,” J. Renewable and Sustainable Energy Reviews, vol. 81, pp. 912–928, 2018. Doi:doi.org/10.1016/j.rser.2017.08.017.

K. G. Nisha and K. Sreekumar, “A Review and Analysis of Machine Learning and Statistical Approaches for Prediction,” in 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 135–139, IEEE, Mar.2017.Doi:10.1109/ICICCT.2017.7975174.

D, Su., Batzelis, E., & Pal, B., “Machine learning algorithms in forecasting of photovoltaic power generation,” in 2019 International Conference on Smart Energy Systems and Technologies (SEST), pp. 1–6, IEEE, Sept. 2019. Doi:10.1109/SEST.2019.8849106.

A. Youssef, M. El-Telbany and A. Zekry, “The role of artificial intelligence in photovoltaic systems design and control: A review”, J. Renewable and Sustainable Energy Reviews, vol. 78, pp. 72-79, 2017. Doi: doi.org/10.1016/j.rser.2017.04.046.

D. Su, E. Batzelis, and B. Pal, “Machine Learning Algorithms in Forecasting of Photovoltaic Power Generation,” in 2019 International Conference on Smart Energy Systems and Technologies (SEST), pp. 1–6, IEEE, Sep. 2019. Doi: 10.1109/SEST.2019.8849106.

M. AlShafeey and C. Csáki, “Evaluating Neural Network and Linear Regression Photovoltaic Power Forecasting Models Based on Different Input Methods,” J. Energy Reports, vol. 7, pp. 7601–7614, 2021. ANN. Doi:doi.org/10.1016/j.egyr.2021.10.125.

AlShafeey M, Csáki C. 2021. Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods. Energy Reports. 7:7601-7614. Doi: doi.org/10.1016/j.egyr.2021.10.125.

B.H. Vu and I.Y. Chung, "Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids", J. Renewable Energy, vol. 195, pp. 1137-1154,2022. Doi: doi.org/10.1016/j.renene.2022.06.086.

V. Suresh, P. Janik, J. Rezmer and Z. Leonowicz, "Forecasting solar PV output using convolutional neural networks with a sliding window algorithm," J. Energies, vol. 13, no. 3, p. 723, 2020. Doi: doi.org/10.3390/en13030723

J. Zeng and W. Qiao, “Short-term solar power prediction using a support vector machine," J. Renewable Energy”, vol. 52, pp. 118-127,2013. Doi:doi.org/10.1016/j.renene.2012.10.009.

D. Niu, K. Wang, L. Sun, J. Wu and X. Xu, "Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study," J. Applied Soft Computing, vol. 93, pp. 106389, 2020. Doi: doi.org/10.1016/j.asoc.2020.106389.

DKA Solar Center. Available online: https://www.dkasolarcentre.com.au (accessed on 04 April 2023).

Van Houdt, G., Mosquera, C., & Nápoles, G., "A review on the long short-term memory model," J. Artificial Intelligence Review, vol. 53, no. 8, pp. 5929-5955, 2020. Doi: 10.1007/s10462-020-09838-1.

Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A., "Applications of Long Short-Term Memory (LSTM) networks in polymeric sciences: A review," Polymers, vol. 16, no. 18, pp. 2607, 2024. Doi: 10.3390/polym16182607.

Wang, L., Liu, Y., Li, T., Xie, X., & Chang, C., "Short-term PV power prediction based on optimized VMD and LSTM," J. IEEE Access, vol. 8, pp. 165849-165862, 2020. Doi: 10.1109/ACCESS.2020.3022246.

Gao, H., Qiu, S., Fang, J., Ma, N., Wang, J., Cheng, K., et al., "Short-term prediction of PV power based on combined modal decomposition and NARX-LSTM-LightGBM," J. Sustainability, vol. 15, no. 10, pp. 8266, 2023. Doi: https://doi.org/10.3390/su15108266.

S. Theocharides, G. Makrides, G.E. Georghiou and A. Kyprianou, “Machine learning algorithms for photovoltaic system power output prediction”, in 2018 IEEE International Energy Conference (ENERGYCON), pp. 1-6, IEEE, 2018. Doi: 10.1109/ENERGYCON.2018.8398737.

Luo, X., Zhang, D., & Zhu, X., "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," J. Energy, vol. 225, pp. 120240, 2021.Doi:doi.org/10.1016/j.energy.2021.120240.

Mitrentsis, G., & Lens, H., "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," J. Applied Energy, vol. 309, pp. 118473, 2022. Doi:doi.org/10.1016/j.apenergy.2021.118473.

R. Ahmed, V. Sreeram, Y. Mishra and M.D Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization”, J. Renewable and Sustainable Energy Reviews, vol. 124, pp. 109792,2020.Doi:doi.org/10.1016/j.rser.2020.109792.