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NowCasting: Accurate and Precise Short-Term Wind Power Prediction using Hyperlocal Wind Forecasts

Authors:

Varun Badrinath Krishna,

Wander S. Wadman,

Younghun KimAuthors Info & Claims

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

Pages 63 - 74

https://doi.org/10.1145/3208903.3208919

Published: 12 June 2018 Publication History

Abstract

To increase wind power integration, it is essential for electric utilities to accurately predict how much power wind turbines will generate. While purely autoregressive (and nonlinear autoregressive) approaches to prediction using historical data perform well for immediate future (10 to 30 minutes ahead) horizons, their accuracy dramatically deteriorates for farther time horizons. Predicting generation up to 5-6 hours ahead is essential for scheduling multi-tier generation systems that have varying dynamic response. We propose a method that augments autoregressive approaches with exogenous inputs from hyperlocal wind speed forecasts to improve the prediction accuracy and precision beyond 30 min ahead. Our approach reduces the mean absolute error to 2.11%--14.25% for predictions made 10 min to 6 hours ahead. Importantly, it also reduces the uncertainty associated with the predictions by over 15% in comparison with approaches presented in related work.

References

[1]

CBS AP. 2016. Winter storm hits frozen East Coast. (February 2016). https://www.cbsnews.com/news/winter-storm-hits-frozen-east-coast-possible-tornadoes-in-the-south/

[2]

Varun Badrinath Krishna, Carl Gunter, and William H. Sanders. 2018. Evaluating Detectors on Optimal Attack Vectors that Enable Electricity Theft and DER Fraud. In IEEE Journal of Selected Topics in Signal Processing. IEEE.

[3]

Varun Badrinath Krishna, Ravishankar K. Iyer, and William H. Sanders. 2015. ARIMA-Based Modeling and Validation of Consumption Readings in Power Grids. In Proc. Int. Conf. Critical Inform. Infrastructures Security (LNCS), Vol. 9578. Springer, 199--210.

[4]

T. G. Barbounis and J. B. Theocharis. 2007. Locally recurrent neural networks for wind speed prediction using spatial correlation. Elsevier Information Sciences 177 (May 2007), 5775--5797.

Digital Library

[5]

T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, and P. S. Dokopoulos. 2006. Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans. on Energy Conversion 21, 1 (March 2006), 273--284.

[6]

Rudy Blonbou. 2010. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning. Elsevier Renewable Energy 36 (August 2010), 1118--1124.

[7]

Stephen Boyd and Lieven Vandenberghe. 2004. Convex optimization. Cambridge university press.

Digital Library

[8]

Erasmo Cadenas, Wilfrido Rivera, Rafael Campos-Amezcua, and Roberto Cadenas. 2016. Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, Mexico. Neural Computing and Applications 27, 8 (01 Nov 2016), 2417--2428.

Digital Library

[9]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2 (2011), 27:1--27:27. Issue 3. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Digital Library

[10]

Dong Chen and David Irwin. 2017. SunDance: Black-box Behind-the-Meter Solar Disaggregation. In Proceedings of the Eighth International Conference on Future Energy Systems. ACM, 45--55.

Digital Library

[11]

Chris Dolce. 2015. Storm Brings Strong Winds, Heavy Rain, Power Outages and Some Snow to Midwest, Northeast. (October 2015). https://weather.com/travel/commuter-conditions/news/eastern-storm-rain-wind-snow

[12]

Aoife M. Foley, Paul G. Leahy, Antonino Marvuglia, and Eamon J. McKeogh. 2012. Current methods and advances in forecasting of wind power generation. Renewable Energy 37, 1 (2012), 1--8.

[13]

Lingwen Gan, Adam Wierman, Ufuk Topcu, Niangjun Chen, and Steven H. Low. 2013. Real-time Deferrable Load Control: Handling the Uncertainties of Renewable Generation. In Proceedings of the Fourth International Conference on Future Energy Systems (e-Energy '13). ACM, New York, NY, USA, 113--124.

Digital Library

[14]

Mohammad H. Hajiesmaili, Chi-Kin Chau, Minghua Chen, and Longbu Huang. 2016. Online Microgrid Energy Generation Scheduling Revisited: The Benefits of Randomization and Interval Prediction. In Proceedings of the Seventh International Conference on Future Energy Systems (e-Energy '16). ACM, New York, NY, USA, Article 1, 11 pages.

Digital Library

[15]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput. 9, 8 (Nov. 1997), 1735--1780.

Digital Library

[16]

Rob Hyndman and Yeasmin Khandakar. 2008. Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software 27, 1 (2008), 1--22.

[17]

Srinivasan Iyengar, Navin Sharma, David Irwin, Prashant Shenoy, and Krithi Ramamritham. 2014. SolarCast: a cloud-based black box solar predictor for smart homes. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. ACM, 40--49.

Digital Library

[18]

Jan Kleissl. 2013. Solar Energy Forecasting and Resource Assessment. (2013).

[19]

Sagar Kurandwad, Chandrasekar Subramanian, Venkata Ramakrishna P, Arunchandar Vasan, Venkatesh Sarangan, Vijaysekhar Chellaboina, and Anand Sivasubramaniam. 2014. Windy with a Chance of Profit: Bid Strategy and Analysis for Wind Integration. In Proceedings of the 5th International Conference on Future Energy Systems (e-Energy '14). ACM, New York, NY, USA, 39--49.

Digital Library

[20]

A. Kusiak, H. Zheng, and Z. Song. 2009. Short-Term Prediction of Wind Farm Power: A Data Mining Approach. IEEE Transactions on Energy Conversion 24, 1 (March 2009), 125--136.

[21]

A. Lahouar and J. Ben Hadj Slama. 2017. Hour-ahead wind power forecast based on random forests. Renewable Energy 109 (2017), 529--541.

[22]

Antonino Marvuglia and Antonio Messineo. 2012. Monitoring of wind farms' power curves using machine learning techniques. Applied Energy 98 (2012), 574--583.

[23]

J. Hazra S. A. Husain C. Basu L. C. DeSilva D. Seetharam N. Y. Voo S. Kalyanaraman Mathew, S. and Z. Sulaiman. 2011. An advanced model for the short-term forecast of wind energy. In MODSIM 2011-19th International Congress on Modelling and Simulation-Sustaining Our Future: Understanding and Living with Uncertainty. MSSANZ, 1745--1752.

[24]

Z. Men, E. Yee, F. Lien, Z. Yang, and Y. Liu. 2014. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting. International Scholarly Research Notices 2014, 972580 (2014).

[25]

Balakrishnan Narayanaswamy, Vikas K. Garg, and T. S. Jayram. 2012. Online Optimization for the Smart (Micro) Grid. In Proceedings of the 3rd International Conference on Future Energy Systems (e-Energy '12). ACM, New York, NY, USA, Article 19, 10 pages.

Digital Library

[26]

National Center for Atmospheric Research (NCAR). Weather Research And Forecasting Model. National Center for Atmospheric Research (NCAR). https://www.mmm.ucar.edu/weather-research-and-forecasting-model Accessed January 2018.

[27]

National Weather Service 2015. Winter storm hits frozen East Coast. National Weather Service. http://www.weather.gov/media/ilx/Stormdata/may2015.pdf

[28]

M. Negnevitsky, P. Mandal, and A. K. Srivastava. 2009. Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems. In 2009 15th International Conference on Intelligent System Applications to Power Systems. 1--6.

[29]

Wang Ningbo, Lu Liang, Liu Guangtu, Wang Dingmei, and Lu Qingquan. 2014. Ultrashort-term prediction method for wind electricity power of ARMA model with anemometer network real-time correction. (Aug. 13 2014). CN Patent App. CN 201,410,187,023.

[30]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.

Digital Library

[31]

A. Di Piazza, M. Di Piazza, and G. Vitale. 2014. Estimation and Forecast of Wind Power Generation by FTDNN and NARX-net based models for Energy Management Purpose in Smart Grids. Renewable Energies and Power Quality Journal (RE&PQJ) 1, 12 (April 2014).

[32]

Pierre Pinson and Henrik Madsen. 2012. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models. Journal of Forecasting 31, 4 (2012), 281--313.

[33]

Renewable Energy Policy Network for the 21st Century (REN21) 2016. Renewables 2016 Global Status Report. Renewable Energy Policy Network for the 21st Century (REN21). http://www.ren21.net/wp-content/uploads/2016/06/GSR_2016_Full_Report.pdf.

[34]

D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1. MIT Press, Cambridge, MA, USA, Chapter Learning Internal Representations by Error Propagation, 318--362. http://dl.acm.org/citation.cfm?id=104279.104293

Digital Library

[35]

Scikit Learn. LinearSVR. Scikit Learn. http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html Accessed January 2018.

[36]

Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy. 2011. Predicting solar generation from weather forecasts using machine learning. In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on. IEEE, 528--533.

[37]

S. Shokrzadeh, M. Jafari Jozani, and E. Bibeau. 2014. Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods. IEEE Transactions on Sustainable Energy 5, 4 (Oct 2014), 1262--1269.

[38]

Saurabh S Soman, Hamidreza Zareipour, Om Malik, and Paras Mandal. 2010. A review of wind power and wind speed forecasting methods with different time horizons. In North American Power Symposium (NAPS), 2010. IEEE, 1--8.

[39]

Jay Taneja, Ken Lutz, and David Culler. 2013. Flexible Loads in Future Energy Networks. In Proceedings of the Fourth International Conference on Future Energy Systems (e-Energy '13). ACM, New York, NY, USA, 285--286.

Digital Library

[40]

Ryan Wiser and Mark Bolinger. 2016. 2015 Wind Technologies Market Report. US Deparment of Energy. https://energy.gov/sites/prod/files/2016/08/f33/2015-Wind-Technologies-Market-Report-08162016.pdf.

Cited By

del Campo RAnguiano ERomero ÁDorronsoro J(2023)Deep Learning Applied to Wind Power Forecasting: A Spatio-Temporal ApproachTheory and Applications of Time Series Analysis10.1007/978-3-031-40209-8_14(207-219)Online publication date: 10-Nov-2023
https://doi.org/10.1007/978-3-031-40209-8_14
Gao XGao LLin HHuo YRen YGuo W(2022)Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power PredictionEnergies10.3390/en1519733215:19(7332)Online publication date: 6-Oct-2022
https://doi.org/10.3390/en15197332
Meng AChen SOu ZXiao JZhang JChen SZhang ZLiang RZhang ZXian ZWang CYin HYan B(2022)A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial networkEnergy10.1016/j.energy.2022.125276261(125276)Online publication date: Dec-2022
https://doi.org/10.1016/j.energy.2022.125276
Show More Cited By

Index Terms

NowCasting: Accurate and Precise Short-Term Wind Power Prediction using Hyperlocal Wind Forecasts
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Conferences

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

June 2018

657 pages

ISBN:9781450357678

DOI:10.1145/3208903

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 12 June 2018

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e-Energy '18

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e-Energy '18: The Ninth International Conference on Future Energy Systems

June 12 - 15, 2018

Karlsruhe, Germany

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

del Campo RAnguiano ERomero ÁDorronsoro J(2023)Deep Learning Applied to Wind Power Forecasting: A Spatio-Temporal ApproachTheory and Applications of Time Series Analysis10.1007/978-3-031-40209-8_14(207-219)Online publication date: 10-Nov-2023
https://doi.org/10.1007/978-3-031-40209-8_14
Gao XGao LLin HHuo YRen YGuo W(2022)Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power PredictionEnergies10.3390/en1519733215:19(7332)Online publication date: 6-Oct-2022
https://doi.org/10.3390/en15197332
Meng AChen SOu ZXiao JZhang JChen SZhang ZLiang RZhang ZXian ZWang CYin HYan B(2022)A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial networkEnergy10.1016/j.energy.2022.125276261(125276)Online publication date: Dec-2022
https://doi.org/10.1016/j.energy.2022.125276
Grushin ATyagi AGluck JMohseni SNigam NKlopfenstein MLee R(2020)GUMP: General Urban Area Microclimate Predictions ToolAIAA AVIATION 2020 FORUM10.2514/6.2020-3213Online publication date: 8-Jun-2020
https://doi.org/10.2514/6.2020-3213
Al-Zadjali SAl Maashri AAl-Hinai AAl-Yahyai SBakhtvar M(2019)An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management SystemsEnergies10.3390/en1222435512:22(4355)Online publication date: 15-Nov-2019
https://doi.org/10.3390/en12224355

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