The future of forecasting for renewable energy

Advanced Review

Conor Sweeney, Ricardo J. Bessa, Jethro Browell, Pierre Pinson

Published Online: Sep 10 2019

DOI: 10.1002/wene.365

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

Abstract Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

12‐month rolling average of the 48‐hr ahead root mean square error (RMSE) forecast skill for wind speed on the 850 hPa pressure level in the Northern Hemisphere. Blue lines: old/new ECMWF IFS. Black line: NCEP GFS

[ Normal View | Magnified View ]

Graphical representation of different business models in renewable energy forecasting

[ Normal View | Magnified View ]

Illustration of different types of errors: (a) Level Error, (b) Phase Error, (c) Spatial Error. (a) Forecast: Dashed line. Observed: Solid line. (b) Forecast: Dashed line. Observed: Solid line. (c) Forecast: Left plot. Observed: Right plot. Spatial difference highlighted as hatched area

[ Normal View | Magnified View ]

References

Agarwal,, A., Dahleh,, M., & Sarkar,, T. (2019). A marketplace for data: An algorithmic solution. arXiv:1805.08125, 1–26.
Andrade,, J. R., & Bessa,, R. J. (2017, oct). Improving renewable energy forecasting with a grid of numerical weather predictions. IEEE Transactions on Sustainable Energy, 8(4), 1571–1580. https://doi.org/10.1109/tste.2017.2694340
Angevine,, W. M., Olson,, J., Kenyon,, J., Gustafson,, W. I., Endo,, S., Suselj,, K., & Turner,, D. D. (2018). Shallow cumulus in wrf parameterizations evaluated against lasso large‐eddy simulations. Monthly Weather Review, 146(12), 4303–4322. https://doi.org/10.1175/MWR-D-18-0115.1
Antonanzas,, J., Osorio,, N., Escobar,, R., Urraca,, R., de Pison,, F. M., & Antonanzas‐Torres,, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78–111. https://doi.org/10.1016/j.solener.2016.06.069
Aulign,, T. (2014). Multivariate minimum residual method for cloud retrieval. Part ii: Real observations experiments. Monthly Weather Review, 142(12), 4399–4415. https://doi.org/10.1175/MWR-D-13-00173.1
Bannister,, R. N. (2017). A review of operational methods of variational and ensemble‐variational data assimilation. Quarterly Journal of the Royal Meteorological Society, 143(703), 607–633. https://doi.org/10.1002/qj.2982
Bauer,, P., Thorpe,, A., & Brunet,, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55.
Bergstrm,, S., & Lindstrm,, G. (2015, may). Interpretation of runoff processes in hydrological modelling‐experience from the HBV approach. Hydrological Processes, 29(16), 3535–3545. https://doi.org/10.1002/hyp.10510
Bessa,, R., Möhrlen,, C., Fundel,, V., Siefert,, M., Browell,, J., Gaidi,, S. E., … Kariniotakis,, G. (2017). Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies, 10(9), 1402.
Blanc,, P., Remund,, J., & Vallance,, L. (2017). Short‐term solar power forecasting based on satellite images. In Renewable energy forecasting (pp. 179–198). Cambridge, UK: Woodhead Publishing. https://doi.org/10.1016/b978-0-08-100504-0.00006-8
Bossanyi,, E. (1985). Short‐term wind prediction using Kalman filters. Wind Engineering, 9(1), 1–8.
Bossavy,, A., Girard,, R., & Kariniotakis,, G. (2013). Forecasting ramps of wind power production with numerical weather prediction ensembles. Wind Energy, 16(1), 51–63.
Botterud,, A., Wang,, J., Zhou,, Z., Bessa,, R., Keko,, H., Akilimali,, J., & Miranda,, V. (2012). Wind power trading under uncertainty in LMP markets. IEEE Transactions on Power Systems, 27(2), 894–903.
Browell,, J., Drew,, D. R., & Philippopoulos,, K. (2018). Improved very short‐term spatio‐temporal wind forecasting using atmospheric regimes. Wind Energy, 21(11), 968–979. https://doi.org/10.1002/we.2207
Brown,, B. G., Katz,, R. W., & Murphy,, A. H. (1984). Time series models to simulate and forecast wind speed and wind power. Journal of Climate and Applied Meteorology, 23(8), 1184–1195. https://doi.org/10.1175/1520-0450(1984)023¡1184:TSMTSA¿2.0.CO;2
Camal,, S., Teng,, F., Michiorri,, A., Kariniotakis,, G., & Badesa,, L. (2019, may). Scenario generation of aggregated wind, photovoltaics and small hydro production for power systems applications. Applied Energy, 242, 1396–1406. https://doi.org/10.1016/j.apenergy.2019.03.112
Carminati,, F., Candy,, B., Bell,, W., & Atkinson,, N. (2018). Assessment and assimilation of fy‐3 humidity sounders and imager in the UK met office global model. Advances in Atmospheric Sciences, 35(8), 942–954.
Cavalcante,, L., Bessa,, R. J., Reis,, M., & Browell,, J. (2016). Lasso vector autoregression structures for very short‐term wind power forecasting. Wind Energy, 20, 657–675. https://doi.org/10.1002/we.2029
Cheng,, W. Y., Liu,, Y., Bourgeois,, A. J., Wu,, Y., & Haupt,, S. E. (2017). Short‐term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation. Renewable Energy, 107, 340–351. https://doi.org/10.1016/j.renene.2017.02.014
Choi,, I.‐J., Park,, R.‐S., & Lee,, J. (2019). Impacts of a newly‐developed aerosol climatology on numerical weather prediction using a global atmospheric forecasting model. Atmospheric Environment, 197, 77–91. https://doi.org/10.1016/j.atmosenv.2018.10.019
Chow,, C. W., Urquhart,, B., Lave,, M., Kleissl,, A. D. J., Shields,, J., & Washom,, B. (2011). Intra‐hour forecasting with a total sky imager at the uc San Diego solar energy testbed. Solar Energy, 85(11), 2881–2893.
Chowdhury,, B. H., & Rahman,, S. (1987). Forecasting sub‐hourly solar irradiance for prediction of photovoltaic output. Paper presented at 19th IEEE photovoltaic specialists conference (pp. 171–176).
Christidis,, K., & Devetsikiotis,, M. (2016). Blockchains and smart contracts for the internet of things. IEEE Access, 4, 2292–2303.
Chu,, Y., Pedro,, H., Li,, M., & Coimbra,, C. F. M. (2015). Real‐time forecasting of solar irradiance ramps with smart image processing. Solar Energy, 114, 91–104.
Chui,, T. C. Y., Siuta,, D., West,, G., Modzelewski,, H., Schigas,, R., & Stull,, R. (2019). On producing reliable and affordable numerical weather forecasts on public cloud‐computing infrastructure. Journal of Atmospheric and Oceanic Technology, 36(3), 491–509. https://doi.org/10.1175/JTECH-D-18-0142.1
Cutler,, N. J., Outhred,, H. R., & MacGill,, I. F. (2012). Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting. Wind Energy, 15(2), 245–258. https://doi.org/10.1002/we.465
de Haan,, S. (2011). High‐resolution wind and temperature observations from aircraft tracked by mode‐s air traffic control radar. Journal of Geophysical Research: Atmospheres, 116(D10), 1‐13. https://doi.org/10.1029/2010JD015264
Du,, P., & Matevosyan,, J. (2018). Forecast system inertia condition and its impact to integrate more renewables. IEEE Transactions on Smart Grid, 9(2), 1531–1533.
Dwork,, C. (2006). Differential privacy. In ICALP 2006: Automata, languages and programming (pp. 1–12). Berlin, Heidelberg: Springer.
ElBahrawy,, A., Alessandretti,, L., Kandler,, A., Pastor‐Satorras,, R., & Baronchelli,, A. (2017). Evolutionary dynamics of the cryptocurrency market. Royal Society Open Science, 4(11), 170623.
Fang,, L., Zhan,, X., Hain,, C., Yin,, J., Liu,, J., & Schull,, M. (2018). An assessment of the impact of land thermal infrared observation on regional weather forecasts using two different data assimilation approaches. Remote Sensing, 10(4), 625.
Feist,, M. M., Westbrook,, C. D., Clark,, P. A., Stein,, T. H., Lean,, H. W., & Stirling,, A. J. (2019). Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations. Quarterly Journal of the Royal Meteorological Society, 145(719), 727–744. https://doi.org/10.1002/qj.3462
Fitch,, A. C., Olson,, J. B., Lundquist,, J. K., Dudhia,, J., Gupta,, A. K., Michalakes,, J., & Barstad,, I. (2012). Local and mesoscale impacts of wind farms as parameterized in a mesoscale nwp model. Monthly Weather Review, 140(9), 3017–3038. https://doi.org/10.1175/MWR-D-11-00352.1
Frolov,, A. V. (2017). Can a quantum computer be applied for numerical weather prediction? Russian Meteorology and Hydrology, 42(9), 545–553. https://doi.org/10.3103/S1068373917090011
Gallego‐Castillo,, C., Cuerva‐Tejero,, A., & Lopez‐Garcia,, O. (2015). A review on the recent history of wind power ramp forecasting. Renewable and Sustainable Energy Reviews, 52, 1148–1157. https://doi.org/10.1016/j.rser.2015.07.154
Gentry,, C. (2010). Computing arbitrary functions of encrypted data. Communications of the ACM, 53(3), 97–105.
Gilbert,, C., Browell,, J., & McMillan,, D. (2019). Leveraging turbine‐level data for improved probabilistic wind power forecasting. submitted.
Gilbert,, C., Messner,, J. W., Pinson,, P., Trombe,, P.‐J., Verzijlbergh,, R., van Dorp,, P., & Jonker,, H. (2019). Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting. (Submitted)
Goertzel,, B., Giacomelli,, S., Hanson,, D., Pennachin,, C., & Argentieri,, M. (2017). SingularityNET: A decentralized, open market and inter‐network for AIs. https://public.singularitynet.io/whitepaper.pdf (The SingularityNET whitepaper)
Golestaneh,, F., Pinson,, P., Azizipanah‐Abarghooee,, R., & Gooi,, H. B. (2018). Ellipsoidal prediction regions for multivariate uncertainty characterization. IEEE Transactions on Power Systems, 33(4), 4519–4530.
Golestaneh,, F., Pinson,, P., & Gooi,, H. B. (2019). Polyhedral predictive regions for power system applications. IEEE Transactions on Power Systems, 34(1), 693–704.
Goodfellow,, I., Pouget‐Abadie,, J., Mirza,, M., Xu,, B., Warde‐Farley,, D., Ozair,, S., … Bengio,, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems 27 (NIPS 2014) (pp. 2672–2680). Montreal, Canada: Neural Information Processing Systems Foundation, Inc.
Gottschall,, J., Gribben,, B., Stein,, D., & Würth,, I. (2017). Floating lidar as an advanced offshore wind speed measurement technique: Current technology status and gap analysis in regard to full maturity. Wiley Interdisciplinary Reviews: Energy and Environment, 6(5), e250. https://doi.org/10.1002/wene.250
Gragne,, A. S., Sharma,, A., Mehrotra,, R., & Alfredsen,, K. (2015). Improving real‐time inflow forecasting into hydropower reservoirs through a complementary modelling framework. Hydrology and Earth System Sciences, 19(8), 3695–3714. https://doi.org/10.5194/hess-19-3695-2015
Gu,, H., Yan,, R., & Saha,, T. K. (2018). Minimum synchronous inertia requirement of renewable power systems. IEEE Transactions on Power Systems, 33(2), 1533–1543.
Gustafsson,, N., Janji,, T., Schraff,, C., Leuenberger,, D., Weissmann,, M., Reich,, H., … Fujita,, T. (2018). Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres. Quarterly Journal of the Royal Meteorological Society, 144(713), 1218–1256. https://doi.org/10.1002/qj.3179
Haessig,, P., Multon,, B., Ahmed,, H. B., Lascaud,, S., & Bondon,, P. (2015). Energy storage sizing for wind power: Impact of the autocorrelation of day‐ahead forecast errors. Wind Energy, 18(1), 43–57.
Hoffman,, R. N., Kumar,, V. K., Boukabara,, S.‐A., Ide,, K., Yang,, F., & Atlas,, R. (2018). Progress in forecast skill at three leading global operational nwp centers during 2015‐17 as seen in summary assessment metrics (sams). Weather and Forecasting, 33(6), 1661–1679. https://doi.org/10.1175/WAF-D-18-0117.1
Ivanov,, S., Michaelides,, S., & Ruban,, I. (2018). Mesoscale resolution radar data assimilation experiments with the harmonie model. Remote Sensing, 10(9), 1453.
Iversen,, E. B., Juhl,, R., Møller,, J. K., Kleissl,, J., Madsen,, H., & Morales,, J. M. (2017). Spatio‐temporal forecasting by coupled stochastic differential equations: Applications to solar power. (Arxiv preprint: arXiv:1706.04394v1)
Jensenius,, J., & Cotton,, G. (1981). The development and testing of automated solar energy forecasts based on the model output statistics (MOS) technique. Paper presented at 1st workshop on terrestrial solar resource forecasting and on use of satellites for terrestrial solar resource assessment.
Jimenez,, P. A., Hacker,, J. P., Dudhia,, J., Haupt,, S. E., Ruiz‐Arias,, J. A., Gueymard,, C. A., … Deng,, A. (2016). Wrf‐solar: Description and clear‐sky assessment of an augmented nwp model for solar power prediction. Bulletin of the American Meteorological Society, 97(7), 1249–1264. https://doi.org/10.1175/BAMS-D-14-00279.1
Jung,, J., & Broadwater,, R. P. (2014). Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews, 31, 762–777. https://doi.org/10.1016/j.rser.2013.12.054
Kara,, E. C., Tabone,, M., Roberts,, C., Kiliccote,, S., & Stewart,, E. M. (2016). Estimating behind‐the‐meter solar generation with existing measurement infrastructure. Paper presentea at Buildsys `16 proceedings of the 3rd ACM international conference on systems for energy‐efficient built environments. Palo Alto, CA, USA.
Kazantzidis,, A., Tzoumanikas,, P., Blanc,, P., Massip,, P., Wilbert,, S., & Ramirez‐Santigosa,, L. (2017). Short‐term forecasting based on all‐sky cameras. In Renewable energy forecasting (pp. 153–178). Woodhead Publishing. https://doi.org/10.1016/b978-0-08-100504-0.00005-6
Kealy,, J. C., Efstathiou,, G. A., & Beare,, R. J. (2019). The onset of resolved boundary‐layer turbulence at grey‐zone resolutions. Boundary‐Layer Meteorology, 171(1), 31–52. https://doi.org/10.1007/s10546-018-0420-0
Kurtulmus,, A., & Daniel,, K. (2018). Trustless machine learning contracts; evaluating and exchanging machine learning models on the ethereum blockchain. arXiv:1802.10185, 1–11.
Landry,, M., Erlinger,, T. P., Patschke,, D., & Varrichio,, C. (2016). Probabilistic gradient boosting machines for GEFCom2014 wind forecasting. International Journal of Forecasting, 32(3), 1061–1066. https://doi.org/10.1016/j.ijforecast.2016.02.002
Leutbecher,, M., Lock,, S.‐J., Ollinaho,, P., Lang,, S. T. K., Balsamo,, G., Bechtold,, P., … Weisheimer,, A. (2017). Stochastic representations of model uncertainties at ecmwf: State of the art and future vision. Quarterly Journal of the Royal Meteorological Society, 143(707), 2315–2339. https://doi.org/10.1002/qj.3094
Lorenc,, A. C., & Jardak,, M. (2018). A comparison of hybrid variational data assimilation methods for global nwp. Quarterly Journal of the Royal Meteorological Society, 144(717), 2748–2760. https://doi.org/10.1002/qj.3401
Ma,, W.‐J., Wang,, J., Gupta,, V., & Chen,, C. (2018). Distributed energy management for networked microgrids using online ADMM with regret. IEEE Transactions on Smart Grid, 9(2), 847–856.
Mahoney,, W. P., Parks,, K., Wiener,, G., Liu,, Y., Myers,, W. L., Sun,, J., … Haupt,, S. E. (2012). A wind power forecasting system to optimize grid integration. IEEE Transactions on Sustainable Energy, 3(4), 670–682. https://doi.org/10.1109/TSTE.2012.2201758
Maier,, H. R., & Dandy,, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environmental Modelling %26 Software, 15(1), 101–124. https://doi.org/10.1016/s1364-8152(99)00007-9
Matos,, M., Lopes,, J. P., Rosa,, M., Ferreira,, R., da Silva,, A. L., Sales,, W., … López,, R. (2009). Probabilistic evaluation of reserve requirements of generating systems with renewable power sources: The portuguese and spanish cases. International Journal of Electrical Power %26 Energy Systems, 31(9), 562–569.
Matos,, M. A., & Bessa,, R. (2011). Setting the operating reserve using probabilistic wind power forecasts. IEEE Transactions on Power Systems, 26(2), 594–603.
McCandless,, T., Haupt,, S., & Young,, G. (2016). A regime‐dependent artificial neural network technique for short‐range solar irradiance forecasting. Renewable Energy, 89, 351–359. https://doi.org/10.1016/j.renene.2015.12.030
McMichael,, L. A., Mechem,, D. B., Wang,, S., Wang,, Q., Kogan,, Y. L., & Teixeira,, J. (2019). Assessing the mechanisms governing the daytime evolution of marine stratocumulus using large‐eddy simulation. Quarterly Journal of the Royal Meteorological Society, 145(719), 845–866. https://doi.org/10.1002/qj.3469
McNicholas,, C., & Mass,, C. F. (2018). Impacts of assimilating smartphone pressure observations on forecast skill during two case studies in the pacific northwest. Weather and Forecasting, 33(5), 1375–1396. https://doi.org/10.1175/WAF-D-18-0085.1
Messner,, J. W., & Pinson,, P. (2018). Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2018.02.001
Möhrlen,, C., Bessa,, R., Barthod,, M., Goretti,, G., & Siefert,, M. (2016). Use of forecast uncertainties in the power sector: State‐of‐the‐art of business practices. Paper presented at proceedings of the 15th international workshop on large‐scale integration of wind power into power systems as well as on transmission networks for offshore wind power plants. Vienna, Austria.
Morren,, J., de Haan,, S., Kling,, W., & Ferreira,, J. (2006). Wind turbines emulating inertia and supporting primary frequency control. IEEE Transactions on Power Systems, 21(1), 433–434.
Orwig,, K. D., Ahlstrom,, M. L., Banunarayanan,, V., Sharp,, J., Wilczak,, J. M., Freedman,, J., … Marquis,, M. (2015). Recent trends in variable generation forecasting and its value to the power system. IEEE Transactions on Sustainable Energy, 6(3), 924–933.
Papavasiliou,, A., & Oren,, S. (2013). Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network. Operations Research, 61(3), 578–592.
Parodi,, A., Pulvirenti,, L., Lagasio,, M., Pierdicca,, N., Marzano,, F. S., Riva,, C., … Rommen,, B. (2018). Ingestion of sentinel‐derived remote sensing products in numerical weather prediction models: First results of the esa steam project. Paper presented at IGARSS 2018–2018 IEEE international geoscience and remote sensing symposium (pp. 3901–3904). doi: https://doi.org/10.1109/IGARSS.2018.8518830
Pinson,, P. (2013). Wind energy: Forecasting challenges for its operational management. Statistical Science, 28(4), 564–585.
Pinson,, P., & Madsen,, H. (2009). Ensemble‐based probabilistic forecasting at horns rev. Wind Energy, 12(2), 137–155.
Pinson,, P., Madsen,, H., Nielsen,, H. A., Papaefthymiou,, G., & Klöckl,, B. (2009). From probabilistic forecasts to statistical scenarios of short‐term wind power production. Wind Energy, 12(1), 51–62.
Pinto,, R., Bessa,, R., & Matos,, M. (2017). Multi‐period flexibility forecast for low voltage prosumers. Energy, 141, 2251–2263.
Redfern,, S., Olson,, J. B., Lundquist,, J. K., & Clack,, C. T. M. (2019). Incorporation of the rotor‐equivalent wind speed into the weather research and forecasting models wind farm parameterization. Monthly Weather Review, 147(3), 1029–1046. https://doi.org/10.1175/MWR-D-18-0194.1
Sanz Rodrigo,, J., Chvez Arroyo,, R. A., Moriarty,, P., Churchfield,, M., Kosovi,, B., Rthor,, P.‐E., … Rife,, D. (2017). Mesoscale to microscale wind farm flow modeling and evaluation. Wiley Interdisciplinary Reviews: Energy and Environment, 6(2), e214. https://doi.org/10.1002/wene.214
Siuta,, D. M., & Stull,, R. B. (2018). Benefits of a multimodel ensemble for hub‐height wind prediction in mountainous terrain. Wind Energy, 21(9), 783–800.
Soares,, T., Bessa,, R., Pinson,, P., & Morais,, H. (2018). Active distribution grid management based on robust AC optimal power flow. EEE Transactions on Smart Grid, 9(6), 6229–6241.
Stedinger,, J. R., Sule,, B. F., & Loucks,, D. P. (1984, nov). Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research, 20(11), 1499–1505. https://doi.org/10.1029/wr020i011p01499
Stone,, E. K. (2018). A comparison of mode‐s enhanced surveillance observations with other in situ aircraft observations. Quarterly Journal of the Royal Meteorological Society, 144(712), 695–700. https://doi.org/10.1002/qj.3238
Tastu,, J., Pinson,, P., & Madsen,, H. (2015). Space‐time trajectories of wind power generation: Parametrized precision matrices under a gaussian copula approach. In A. Antoniadis,, J.‐M. Poggi,, & X. Brossat, (Eds.), Modeling and stochastic learning for forecasting in high dimensions (pp. 267–296). Cham: Springer International Publishing.
Taylor,, J. W. (2017). Probabilistic forecasting of wind power ramp events using autoregressive logit models. European Journal of Operational Research, 259(2), 703–712.
Tripathy,, A., Wang,, Y., & Ishwar,, P. (2019). Privacy‐preserving adversarial networks. arXiv:1712.07008, 1–15.
Trombe,, P., Pinson,, P., & Madsen,, H. (2014). Automatic classification of offshore wind regimes with weather radar observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 116–125.
Trombe,, P., Pinson,, P., Vincent,, C., Bøvith,, T., Cutululis,, N. A., Draxl,, C., … Sommer,, A. (2014). Weather radars‐the new eyes for offshore wind farms? Wind Energy, 17(11), 1767–1787.
Valldecabres,, L., Nygaard,, N., Vera‐Tudela,, L., von Bremen,, L., & Kühn,, M. (2018). On the use of dual‐doppler radar measurements for very short‐term wind power forecasts. Remote Sensing, 10(11), 1701. https://doi.org/10.3390/rs10111701
Valldecabres,, L., Peña,, A., Courtney,, M., von Bremen,, L., & Kühn,, M. (2018, may). Very short‐term forecast of near‐coastal flow using scanning lidars. Wind Energy Science, 3(1), 313–327. https://doi.org/10.5194/wes-3-313-2018
van der Meer,, D., Shepero,, M., Svensson,, A., Widén,, J., & Munkhammar,, J. (2018). Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian processes. Applied Energy, 213, 195–207.
Wang,, J., Botterud,, A., Bessa,, R., Keko,, H., Miranda,, V., Akilimali,, J., … Issicaba,, D. (2011). Wind power forecasting uncertainty and unit commitment. Applied Energy, 88(11), 4014–4023.
Wang,, Y., Zhang,, N., Chen,, Q., Kirschen,, D. S., Li,, P., & Xia,, Q. (2018). Data‐driven probabilistic net load forecasting with high penetration of behind‐the‐meter PV. IEEE Transactions on Power Systems, 33(3), 3255–3264.
Wendell,, L., Wegley,, H., & Verholek,, M. (1978). Report from a working group meeting on wind forecasts for WECS operation. pnl‐2513 (Tech. Rep.). United States Department of Commerce, 5205 Port Royal Road, Springfield, Virginia: Pacific Northwest Laboratory.
Wilczak,, J., Finley,, C., Freedman,, J., Cline,, J., Bianco,, L., Olson,, J., … Marquis,, M. (2015). The wind forecast improvement project (wfip): A public‐private partnership addressing wind energy forecast needs. Bulletin of the American Meteorological Society, 96(10), 1699–1718. https://doi.org/10.1175/BAMS-D-14-00107.1
Würth,, I., Valldecabres,, L., Simon,, E., Mhrlen,, C., Uzunoğlu,, B., Gilbert,, C., … Kaifel,, A. (2019). Minute‐scale forecasting of wind power—Results from the collaborative workshop of IEA wind task 32 and 36. Energies, 12(4), 712. https://doi.org/10.3390/en12040712
Wyngaard,, J. C. (2004). Toward numerical modeling in the terra incognita. Journal of the Atmospheric Sciences, 61(14), 1816–1826. https://doi.org/10.1175/1520-0469(2004)061¡1816:TNMITT¿2.0.CO;2
Yang,, J., Astitha,, M., Delle Monache,, L., & Alessandrini,, S. (2018). An analog technique to improve storm wind speed prediction using a dual nwp model approach. Monthly Weather Review, 146(12), 4057–4077 https://search.ebscohost.com/login.aspx?direct=true%26db=a9h%26AN=133447861%26site=ehost-live
Zhang,, F., Qiang Sun,, Y., Magnusson,, L., Buizza,, R., Lin,, S.‐J., Chen,, J.‐H., & Emanuel,, K. (2019). What is the predictability limit of midlatitude weather? Journal of the Atmospheric Sciences, Early Online, 76, 1077–1091. https://doi.org/10.1175/JAS-D-18-0269.1
Zhang,, X., & Grijalva,, S. (2016). A data‐driven approach for detection and estimation of residential PV installations. IEEE Transactions on Smart Grid, 7(5), 2477–2485.
Zhou,, N., Huang,, Z., Meng,, D., Elbert,, S., Wang,, S., & Diao,, R. (2014). Capturing dynamics in the power grid: Formulation of dynamic state estimation through data assimilation (Tech. Rep. No. PNNL‐23213). Pacific Northwest National Laboratory.

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The future of forecasting for renewable energy

Browse by Topic

Access to this WIREs title is by subscription only.

The latest WIREs articles in your inbox