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Short-Term Solar PV Generation Forecast Using Neural Networks and Deep Learning Models

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Fundamentals and Innovations in Solar Energy

Part of the book series: Energy Systems in Electrical Engineering ((ESIEE))

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Abstract

Accurate short-term forecasting of renewable-based generating sources is an essential tool for plants owner and system operator in order to take informed operational decisions. This chapter presents various neural network (NN) and deep learning (DL)-based approaches to forecast the solar PV generation. The random vector functional link (RVFL) model, a variant of NN and long short-term memory (LSTM), a DL-based model are developed to forecast solar PV generation considering a realistic dataset of solar PV plants at IIT Gandhinagar campus. Application of DL-based LSTM and NN-based variants of RVFL models for forecasting solar PV generation for both clear sky and cloudy day is carried out, and a performance comparison between LSTM, RVFL, and wavelet decomposition (WD)-RVFL-based solar PV forecasting algorithms for clear sky is presented. On analysis, it was found that DL-based LSTM model performs better than variants of NN-based RVFL models. As a result, the drawback of shallow NN models over DL models are highlighted. At last, the need for sophisticated DL models over shallow NN models to solve the problem of renewable energy forecasting is stressed.

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References

  1. The Ministry of New and Renewable Energy (MNRE). Government of India. https://mnre.gov.in/

  2. Shivashankar S et al (2016) Mitigating methods of power fluctuation of photovoltaic (PV) sources—a review. Renew Sustain Energ Rev 59:1170–1184

    Article  Google Scholar 

  3. Sobri S, Koohi-Kamali S, Rahim NA (2018) Solar photovoltaic generation forecasting methods: a review. Energ Convers Manag 156:459–497

    Article  Google Scholar 

  4. Sukumar S et al (2018) Ramp-rate control approach based on dynamic smoothing parameter to mitigate solar PV output fluctuations. Int J Electr Power Energ Syst 96:296–305

    Google Scholar 

  5. Sukumar S et al (2018) Ramp-rate control smoothing methods to control output power fluctuations from solar photovoltaic (PV) sources—a review. J Energ Storage 20:218–229

    Google Scholar 

  6. Abhinav R, Pindoriya NM (2018) Opportunities and key challenges for wind energy trading with high penetration in Indian power market. Energ Sustain Dev 47:53–61

    Article  Google Scholar 

  7. (2016) Framework on forecasting, scheduling and imbalance handling for variable renewable energy sources (Wind and Solar). Central Electricity Regulatory Commission (CERC)

    Google Scholar 

  8. Yadav AK, Chandel S (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energ Rev 33:772–781

    Article  Google Scholar 

  9. 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 9:11

    Article  Google Scholar 

  10. Mellit A, Pavan AM (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. Sol Energ 84:807–821

    Article  Google Scholar 

  11. Izgi E, Öztopal A, Yerli B, Kaymak MK, Şahin AD (2012) Short–mid-term solar power prediction by using artificial neural networks. Sol Energ 86:725–733

    Article  Google Scholar 

  12. Pang Z, Niu F, O’Neill Z (2020) Solar radiation prediction using recurrent neural network and artificial neural network: a case study with comparisons. Renew Energ

    Google Scholar 

  13. Galván IM et al (2017) Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Inform Sci 418:363–382

    Google Scholar 

  14. Benali L et al (2019) Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew Energ 132:871–884

    Google Scholar 

  15. Ogliari E, Grimaccia F, Leva S, Mussetta M (2013) Hybrid predictive models for accurate forecasting in PV systems. Energies 6:1918–1929

    Article  Google Scholar 

  16. Kardakos E, Alexiadis M, Vagropoulos S, Simoglou C, Biskas P, Bakirtzis A (2013) Application of time series and artificial neural network models in short-term forecasting of PV power generation. In: Power engineering conference (UPEC). In: 2013 48th international universities. IEEE, pp 1–6

    Google Scholar 

  17. Chen S, Gooi H, Wang M (2013) Solar radiation forecast based on fuzzy logic and neural networks. Renew Energ 60:195–201

    Article  Google Scholar 

  18. Mellit A, Sağlam S, Kalogirou S (2013) Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renew Energ 60:71–78

    Article  Google Scholar 

  19. Notton G, Paoli C, Ivanova L, Vasileva S, Nivet ML (2013) Neural network approach to estimate 10 min solar global irradiation values on tilted planes. Renew Energ 50:576–584

    Article  Google Scholar 

  20. Prado F, Minutolo MC, Kristjanpoller W (2020) Forecasting based on an ensemble autoregressive moving average-adaptive neuro-fuzzy inference system–neural network-genetic algorithm framework. Energy 197:117159

    Article  Google Scholar 

  21. Yang Z et al (2020) A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting. Neurocomputing

    Google Scholar 

  22. Wang HZ, Li GQ, Wang GB, Peng JC, Jiang H, Liu YT (2017) Deep learning based ensemble approach for probabilistic wind power forecasting. Appl Energ 188:56–70

    Article  Google Scholar 

  23. Tanveer A, Huanxin Chen (2019) Deep learning for multi-scale smart energy forecasting. Energy 175:98–112

    Article  Google Scholar 

  24. Georg H, Matthias R (2018) Deep learning for fault detection in wind turbines. Renew Sustain Energ Rev 98:189–198

    Article  Google Scholar 

  25. Zhang C, Chen CLP, Gan M, Chen L (2015) Predictive deep Boltzmann machine for multiperiod wind speed forecasting. IEEE Trans Sustain Energ 6(4):1416–1425

    Article  Google Scholar 

  26. Chang GW, Lu HJ (2019) Integrating grey data preprocessor and deep belief network for day-ahead PV power output forecast. IEEE Trans Sustain Energ 99(1):1

    Google Scholar 

  27. Huaizhi W, Haiyan Y, Jianchun P, Guibin W, Yitao L, Hui J et al (2017) Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network. Energ Convers Manag 153:409–422

    Article  Google Scholar 

  28. AlKandari M, Ahmad I (2019) Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Appl Comput Inform

    Google Scholar 

  29. Li P et al (2020) A hybrid deep learning model for short-term PV power forecasting. Appl Energ 259:114216

    Google Scholar 

  30. Wang H et al (2019) A review of deep learning for renewable energy forecasting. Energ Convers Manag 198:11799

    Google Scholar 

  31. Kushwaha V, Pindoriya NM (2019) A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast. Renew Energ 140:124–139

    Article  Google Scholar 

  32. Florian Z, Rick S (2018) Probabilistic mid and long-term electricity price forecasting. Renew Sustain Energ Rev 94:251–266

    Article  Google Scholar 

  33. Zhang L, Suganthan P (2016) A comprehensive evaluation of random vector functional link networks. Inf Sci 367e368-1094e1105. https://doi.org/10.1016/j.ins.2015.09.025

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat, India

    Shivashankar Sukumar & Naran M. Pindoriya

  2. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, UP, India

    Sri Niwas Singh

Authors
  1. Shivashankar Sukumar
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  2. Naran M. Pindoriya
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  3. Sri Niwas Singh
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Corresponding author

Correspondence to Naran M. Pindoriya .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Sri Niwas Singh

  2. Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India

    Prabhakar Tiwari

  3. Department of Mechanical Engineering, Shiv Nadar University, Dadri, Uttar Pradesh, India

    Sumit Tiwari

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Sukumar, S., Pindoriya, N.M., Singh, S.N. (2021). Short-Term Solar PV Generation Forecast Using Neural Networks and Deep Learning Models. In: Singh, S.N., Tiwari, P., Tiwari, S. (eds) Fundamentals and Innovations in Solar Energy. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-33-6456-1_7

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  • DOI: https://doi.org/10.1007/978-981-33-6456-1_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6455-4

  • Online ISBN: 978-981-33-6456-1

  • eBook Packages: EnergyEnergy (R0)

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