Comparison of AI-Based Forecasting Model ANN, CNN, and ESN for Forecasting Solar Power

Authors

  • Shashikant, Binod Shaw

Keywords:

Forecasting, Solar Power, Machine Learning Model, Convolution Neural Network.

Abstract

Renewable energy has great potential in the future because of its easy availability, zero pollution, sustainability, security, electrification in rural areas, resilience, etc., in which solar energy is one of the most prominent ways to harness and utilize the energy. The challenges in solar energy is to predict the output power from the solar cells because of its nature dependency, which makes system non-linear. To predict non-linear data machine learning based forecasting model is developed to forecast solar power. Convolution neural network  (CNN) based forecasting model is developed and predicted solar power for a day ahead and a week ahead. CNN based forecasting model is compared with conventional neural network i.e. ANN and RNN to estimate its performance over non-linear data. The performance of machine learning model is evaluated over four performance indices such as MAE, MSE, MAPE and R2. On comparison CNN outperform the other two models. The model is built in MATLAB platform.

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References

R. Tawn and J. Browell, “A review of very short-term wind and solar power forecasting,” Renew. Sustain. Energy Rev., vol. 153, p. 111758, 2022, doi: https://doi.org/10.1016/j.rser.2021.111758.

J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, “Review of photovoltaic power forecasting,” Sol. Energy, vol. 136, pp. 78–111, 2016, doi: https://doi.org/10.1016/j.solener.2016.06.069.

K. J. Iheanetu, “Solar Photovoltaic Power Forecasting: A Review,” Sustain., vol. 14, no. 24, 2022, doi: 10.3390/su142417005.

G. Aburiyana and M. E. El-Hawary, “An overview of forecasting techniques for load, wind and solar powers,” in 2017 IEEE Electrical Power and Energy Conference (EPEC), 2017, pp. 1–7. doi: 10.1109/EPEC.2017.8286192.

J. S. Almeida, “Predictive non-linear modeling of complex data by artificial neural networks,” Curr. Opin. Biotechnol., vol. 13, no. 1, pp. 72–76, 2002, doi: https://doi.org/10.1016/S0958-1669(02)00288-4.

G. Chen and R. Hou, “A New Machine Double-Layer Learning Method and Its Application in Non-Linear Time Series Forecasting,” in 2007 International Conference on Mechatronics and Automation, 2007, pp. 795–799. doi: 10.1109/ICMA.2007.4303646.

S. Kaushaley, B. Shaw, and J. R. Nayak, “Optimized Machine Learning-Based Forecasting Model for Solar Power Generation by Using Crow Search Algorithm and Seagull Optimization Algorithm,” Arab. J. Sci. Eng., vol. 48, no. 11, pp. 14823–14836, 2023, doi: 10.1007/s13369-023-07822-9.

M. Gopinath and J. Beckman, “Application of Machine Learning in Forecasting International Trade Trends,” no. Gunning, 2019.

D. Hain, R. Jurowetzki, S. Lee, and Y. Zhou, “Machine learning and artificial intelligence for science , technology , innovation mapping and forecasting : Review , synthesis , and applications,” Scientometrics, vol. 128, no. 3, pp. 1465–1472, 2023, doi: 10.1007/s11192-022-04628-8.

Z. Wang, J. Zhao, H. Huang, and X. Wang, “A Review on the Application of Machine Learning Methods in Tropical Cyclone Forecasting,” vol. 10, no. June, pp. 1–17, 2022, doi: 10.3389/feart.2022.902596.

Almalaq and G. Edwards, “A Review of Deep Learning Methods Applied on Load Forecasting,” in 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, pp. 511–516. doi: 10.1109/ICMLA.2017.0-110.

N. Ahmad, Y. Ghadi, M. Adnan, and M. Ali, “Load Forecasting Techniques for Power System: Research Challenges and Survey,” IEEE Access, vol. 10, pp. 71054–71090, 2022, doi: 10.1109/ACCESS.2022.3187839.

Ahmad, T. N. Anderson, and T. T. Lie, “Hourly global solar irradiation forecasting for New Zealand,” Sol. Energy, vol. 122, pp. 1398–1408, 2015, doi: https://doi.org/10.1016/j.solener.2015.10.055.

Fouilloy et al., “Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability,” Energy, vol. 165, pp. 620–629, 2018, doi: https://doi.org/10.1016/j.energy.2018.09.116.

Y. Zahraoui, I. Alhamrouni, S. Mekhilef, and M. R. Basir Khan, “Chapter one - Machine learning algorithms used for short-term PV solar irradiation and temperature forecasting at microgrid,” in Applications of AI and IOT in Renewable Energy, R. N. Shaw, A. Ghosh, S. Mekhilef, and V. E. Balas, Eds. Academic Press, 2022, pp. 1–17. doi: https://doi.org/10.1016/B978-0-323-91699-8.00001-2.

Voyant et al., “Machine learning methods for solar radiation forecasting: A review,” Renew. Energy, vol. 105, pp. 569–582, 2017, doi: 10.1016/j.renene.2016.12.095.

M. Jawad et al., “Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters,” IEEE Access, vol. 8, pp. 146847–146864, 2020, doi: 10.1109/ACCESS.2020.3014086.

E. Spiliotis, H. Doukas, V. Assimakopoulos, and F. Petropoulos, “Chapter 4 - Forecasting week-ahead hourly electricity prices in Belgium with statistical and machine learning methods,” in Mathematical Modelling of Contemporary Electricity Markets, A. Dagoumas, Ed. Academic Press, 2021, pp. 59–74. doi: https://doi.org/10.1016/B978-0-12-821838-9.00005-0.

X. Chen, Z. Y. Dong, K. Meng, Y. Xu, K. P. Wong, and H. W. Ngan, “Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping,” IEEE Trans. Power Syst., vol. 27, no. 4, pp. 2055–2062, 2012, doi: 10.1109/TPWRS.2012.2190627.

D. Papalexopoulos, S. Hao, and T.-M. Peng, “An implementation of a neural network based load forecasting model for the EMS,” IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1956–1962, 1994, doi: 10.1109/59.331456.

Di Piazza, M. C. Di Piazza, G. La Tona, and M. Luna, “An artificial neural network-based forecasting model of energy-related time series for electrical grid management,” Math. Comput. Simul., vol. 184, pp. 294–305, 2021, doi: https://doi.org/10.1016/j.matcom.2020.05.010.

N. Agami, A. Atiya, M. Saleh, and H. El-Shishiny, “A neural network based dynamic forecasting model for Trend Impact Analysis,” Technol. Forecast. Soc. Change, vol. 76, no. 7, pp. 952–962, 2009, doi: https://doi.org/10.1016/j.techfore.2008.12.004.

S. Namasudra, S. Dhamodharavadhani, and R. Rathipriya, “Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases,” Neural Process. Lett., vol. 55, no. 1, pp. 171–191, 2023, doi: 10.1007/s11063-021-10495-w.

G. S. M. Thakur, R. Bhattacharyya, and S. S. Mondal, “Artificial Neural Network Based Model for Forecasting of Inflation in India,” Fuzzy Inf. Eng., vol. 8, no. 1, pp. 87–100, 2016, doi: 10.1016/j.fiae.2016.03.005.

G. Li, H. Wang, S. Zhang, J. Xin, and H. Liu, “Recurrent neural networks based photovoltaic power forecasting approach,” Energies, vol. 12, no. 13, pp. 1–17, 2019, doi: 10.3390/en12132538.

S. Ryu, J. Noh, and H. Kim, “Deep Neural Network Based Demand Side Short Term Load Forecasting,” Energies, vol. 10, no. 1, 2017, doi: 10.3390/en10010003.

Y.-F. Lin, T.-M. Huang, W.-H. Chung, and Y.-L. Ueng, “Forecasting Fluctuations in the Financial Index Using a Recurrent Neural Network Based on Price Features,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 5, no. 5, pp. 780–791, 2021, doi: 10.1109/TETCI.2020.2971218.

L. Cheng et al., “Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach,” Energies, vol. 11, no. 8, 2018, doi: 10.3390/en11081958.

S. Qiao et al., “A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model,” ACM Trans. Intell. Syst. Technol., vol. 12, no. 6, Nov. 2021, doi: 10.1145/3447988.

H. H. Goh et al., “Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting,” IEEE Access, vol. 9, pp. 118528–118540, 2021, doi: 10.1109/ACCESS.2021.3107954.

A.-A. Semenoglou, E. Spiliotis, and V. Assimakopoulos, “Image-based time series forecasting: A deep convolutional neural network approach,” Neural Networks, vol. 157, pp. 39–53, 2023, doi: https://doi.org/10.1016/j.neunet.2022.10.006.

S. A. Haider et al., “LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan,” Agronomy, vol. 9, no. 2, 2019, doi: 10.3390/agronomy9020072.

L. Huang, T. Cai, Y. Zhu, Y. Zhu, W. Wang, and K. Sun, “LSTM-Based Forecasting for Urban Construction Waste Generation,” Sustainability, vol. 12, no. 20, 2020, doi: 10.3390/su12208555.

W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-Based Model to Forecast Stock Prices,” Complexity, vol. 2020, no. 1, p. 6622927, 2020, doi: https://doi.org/10.1155/2020/6622927.

H. Abbasimehr, M. Shabani, and M. Yousefi, “An optimized model using LSTM network for demand forecasting,” Comput. Ind. Eng., vol. 143, p. 106435, 2020, doi: https://doi.org/10.1016/j.cie.2020.106435.

S. García, A. Fernández, J. Luengo, and F. Herrera, “A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability,” Soft Comput., vol. 13, no. 10, pp. 959–977, 2009, doi: 10.1007/s00500-008-0392-y.

E. Avuçlu and A. Elen, “Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements,” Med. Biol. Eng. Comput., vol. 58, no. 11, pp. 2775–2788, 2020, doi: 10.1007/s11517-020-02260-3.

J. N. Goetz, A. Brenning, H. Petschko, and P. Leopold, “Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling,” Comput. Geosci., vol. 81, pp. 1–11, 2015, doi: https://doi.org/10.1016/j.cageo.2015.04.007.

J. Zhang, Z. Li, Z. Pu, and C. Xu, “Comparing Prediction Performance for Crash Injury Severity Among Various Machine Learning and Statistical Methods,” IEEE Access, vol. 6, pp. 60079–60087, 2018, doi: 10.1109/ACCESS.2018.2874979.

E. Levin, “A recurrent neural network: Limitations and training,” Neural Networks, vol. 3, no. 6, pp. 641–650, 1990, doi: https://doi.org/10.1016/0893-6080(90)90054-O.

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Published

12.06.2024

How to Cite

Shashikant. (2024). Comparison of AI-Based Forecasting Model ANN, CNN, and ESN for Forecasting Solar Power. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 1794–1800. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6492

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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