Abstract
The widespread attention in the growth of clean energy for electricity production necessitates an accurate and reliable generation and demand forecasts. However, the decision-making process in electric power industry involves more uncertainty due to the transition towards distributed energy systems, which are not addressed in the conventional point forecasts. This paper proposes a probabilistic method termed as Deep Quantile Regression (DQR) for the construction of prediction intervals (PIs) that can potentially quantify uncertainty in the point forecasts of wind power generation and demand. The effectiveness of DQR is examined using the low and high seasonal wind and demand datasets. PIs with various confidence levels of 99%, 95% and 90% are estimated by constructing the appropriate quantiles using the proposed DQR method. The quantitative comparison of the quality in all the estimated PIs using the proposed method proves to outperform the other state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hong, T., Fan, S.: Probabilistic electric load forecasting: a tutorial review. Int. J. Forecast. 32(3), 914–938 (2016)
Jinhua, Z., Jie, Y., Wenjing, W., Yongqian, L.: Research on short-term forecasting and uncertainty of wind turbine power based on relevance vector machine. Energy Procedia 158, 229–236 (2019)
Xie, J., Hong, T., Laing, T., Kang, C.: On normality assumption in residual simulation for probabilistic load forecasting. IEEE Trans. Smart Grid 8(3), 1046–1053 (2017)
Foley, A.M., Leahy, P.G., Marvuglia, A., McKeogh, E.J.: Current methods and advances in forecasting of wind power generation. Renew. Energy 37(1), 1–8 (2012)
Kusiak, A., Zhang, Z.: Short-horizon prediction of wind power: a data-driven approach. IEEE Trans. Energy Convers. 25(4), 1112–1122 (2010)
Lange, M., Focken, U.: Physical Approach to Short-term Wind Power Prediction. Springer, Heidelberg (2005)
Burton, N., Bossanyi, E.: Wind Energy Handbook. Wiley, Hoboken (2001)
Bremnes, J.B.: A comparison of a few statistical models for making quantile wind power forecasts. Wind Energy 9(1–2), 3–11 (2006)
Pinson, P., Kariniotakis, G.: Conditional prediction intervals of wind power generation. IEEE Trans. Power Syst. 25(4), 1845–1856 (2010)
Khosravi, A., Nahavandi, S.: An optimized mean variance estimation method for uncertainty quantification of wind power forecasts. Electr. Power Energy Syst. 61, 446–454 (2014)
Pinson, P., Girard, R.: Evaluating the quality of scenarios of short-term wind power generation. Appl. Energy 96, 12–20 (2012)
Pinson, P., Nielsen, H.A., Mller, J.K., Madsen, H., Kariniotakis, G.N.: Non-parametric probabilistic forecasts of wind power: required properties and evaluation. Wind Energy 10(6), 497–516 (2007)
Abbas, K., Nahavandi, S., Creighton, D.: Prediction intervals for short-term wind farm power generation forecasts. IEEE Trans. Sustain. Energy 4(3), 602–610 (2013)
Abbas, K., Nahavandi, S., Creighton, D., Naghavizadeh, R: Uncertainty quantification for wind farm power generation. In: IEEE World Congress on Computational Intelligence, Australia (2012)
Hao, Q., Srinivasan, D., Abbas, K.: Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 303–315 (2014)
Kavasseri, R.G., Seetharaman, K.: Day-ahead wind speed forecasting using fARIMA models. Renew. Energy 34(5), 1388–1393 (2009)
Barbounis, T., Theocharis, J., Alexiadis, M., Dokopoulos, P.: Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans. Energy Convers. 21(1), 273–284 (2006)
LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521, 436–444 (2015)
Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back propagating errors. Nature 323, 533–536 (1986)
Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F.: A lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans. Neural Netw. 22(3), 337–346 (2011)
Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.: Comprehensive review of neural ne work-based prediction intervals and new advances. IEEE Trans. Neural Netw. 22(9), 1341–1356 (2011)
NIWE Homepage. http://niwe.res.in:8080/NIWE_WRA_DATA/. Accessed 20 Nov 2018
SRLDC Homepage. https://srldc.in/DailyReport.aspx. Accessed 20 Nov 2018
Vishnupriyadharshini, A., Vanitha, V., Palanisamy, T.: Wind speed forecasting based on statistical auto regressive integrated moving average (ARIMA) method. Int. J. Control Theory Appl. 9(15), 7681–7690 (2016)
Nair, K.R., Vanitha, V., Jisma, M.: Forecasting of wind speed using ANN, ARIMA and hybrid models. In: IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 170–175 (2017)
Lahmiri, S.: Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthc. Technol. Lett. 1(3), 104–109 (2014)
https://en.wind-turbine-models.com/turbines/428-gamesa-g114-2.0mw. Accessed 24 Dec 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kirthika, N., Ramachandran, K.I., Kottayil, S.K. (2021). Deep Quantile Regression Based Wind Generation and Demand Forecasts. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-49345-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49344-8
Online ISBN: 978-3-030-49345-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)