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A novel combination of Mycielski–Markov, regime switching and jump diffusion models for solar energy

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  • Song, Xiaodong
  • Johnson, Paul
  • Duck, Peter

Abstract

With renewable energy sources growing, solar power generation is becoming ever more popular around the world, so forecasting and scenario analysis of Solar Photovoltaic production is beneficial for grid operators and investors. In this paper, we introduce a novel combination of a Mycielski–Markov model, standard regime switching, and jump diffusion models to generate 1-minute Global Horizontal Irradiance time series over any time scale. It can simulate different scenarios of solar irradiance in the future after being trained on empirical data. We verify our model using statistical tests to compare our simulations with those from an observed time-series in Mauritius. The resulting model is able to generate simulations retaining the statistical properties of the data. Further, we find the proposed calibration process to be robust, and identified that splitting the day into 16 periods to be perfect balance to counter overfitting. The proposed model has the potential to better understand the effects of including large scale Solar Photovoltaic generation into an energy network, value future investments, or even allow for a cost–benefit analysis of subsidies.

Suggested Citation

  • Song, Xiaodong & Johnson, Paul & Duck, Peter, 2021. "A novel combination of Mycielski–Markov, regime switching and jump diffusion models for solar energy," Applied Energy, Elsevier, vol. 301(C).
    • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008461
    DOI: 10.1016/j.apenergy.2021.117457
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    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    2. E. B. Iversen & J. M. Morales & J. K. Møller & H. Madsen, 2014. "Probabilistic forecasts of solar irradiance using stochastic differential equations," Environmetrics, John Wiley & Sons, Ltd., vol. 25(3), pages 152-164, May.
    3. Appino, Riccardo Remo & González Ordiano, Jorge Ángel & Mikut, Ralf & Faulwasser, Timm & Hagenmeyer, Veit, 2018. "On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages," Applied Energy, Elsevier, vol. 210(C), pages 1207-1218.
    4. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Ramgolam, Yatindra K. & Soyjaudah, K.M.S., 2015. "Unveiling the solar resource potential for photovoltaic applications in Mauritius," Renewable Energy, Elsevier, vol. 77(C), pages 94-100.
    7. Ismail, Abdul Muhaimin & Ramirez-Iniguez, Roberto & Asif, Muhammad & Munir, Abu Bakar & Muhammad-Sukki, Firdaus, 2015. "Progress of solar photovoltaic in ASEAN countries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 399-412.
    8. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    9. Andrew Schaug & Harish Chandra, 2019. "On unbiased simulations of stochastic bridges conditioned on extrema," Papers 1911.10972, arXiv.org, revised Nov 2019.
    10. Kaplanis, S. & Kaplani, E., 2010. "Stochastic prediction of hourly global solar radiation for Patra, Greece," Applied Energy, Elsevier, vol. 87(12), pages 3748-3758, December.
    11. Kumar Sahu, Bikash, 2015. "A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 621-634.
    12. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    13. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    14. Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(C).
    15. K. Fergusson & E. Platen, 2015. "Application Of Maximum Likelihood Estimation To Stochastic Short Rate Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-26, December.
    16. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    17. Borovkova, Svetlana & Schmeck, Maren Diane, 2017. "Electricity price modeling with stochastic time change," Energy Economics, Elsevier, vol. 63(C), pages 51-65.
    18. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    19. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    20. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    21. Ait-Sahalia, Yacine, 2004. "Disentangling diffusion from jumps," Journal of Financial Economics, Elsevier, vol. 74(3), pages 487-528, December.
    22. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2012. "Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation," Energy, Elsevier, vol. 39(1), pages 341-355.
    23. Li, Shuai & Ma, Hongjie & Li, Weiyi, 2017. "Typical solar radiation year construction using k-means clustering and discrete-time Markov chain," Applied Energy, Elsevier, vol. 205(C), pages 720-731.
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