Abstract
Distributed residential solar power forecasting is motivated by multiple applications including local grid and storage management. Forecasting challenges in this area include data nonstationarity, incomplete site information, and noisy or sparse site history. Gaussian process models provide a flexible, nonparametric approach that allows probabilistic forecasting. We develop fully scalable multi-site forecast models using recent advances in approximate Gaussian process methods to (probabilistically) forecast power at 37 residential sites in Adelaide (South Australia) using only historical power data. Our approach captures diurnal cycles in an integrated model without requiring prior data detrending. Further, multi-site methods show some advantage over single-site methods in variable weather conditions.
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Stationarity here refers to the property that distribution parameters remain stable (and finite) over time.
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A useful exposition of coregional models can be found at [1].
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The persistence model in the present study is applied to unflattened data.
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Note that SMLL does not apply to the non-probabilistic persistence model.
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Clear days are defined as those where daily global horizontal irradiance (GHI) was more than 90% of mean maximum daily GHI for the month of January. Measurements are from the Adelaide (West Terrace) Australian Bureau of Meteorology weather station. GHI for clear (cloudy) days ranges from 93–97 (36–90)% of the mean January maximum.
References
Alvarez, M.A., Rosasco, L., Lawrence, N.D., et al.: Kernels for vector-valued functions: a review. Found. Trends® Mach. Learn. 4(3), 195–266 (2012)
Aryaputera, A.W., Yang, D., Zhao, L., Walsh, W.M.: Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging. Sol. Energy 122, 1266–1278 (2015). http://www.sciencedirect.com/science/article/pii/S0038092X15005745
Bessa, R., Trindade, A., Silva, C.S., Miranda, V.: Probabilistic solar power forecasting in smart grids using distributed information. Int. J. Electr. Power Energy Syst. 72, 16–23 (2015). http://www.sciencedirect.com/science/article/pii/S0142061515000897, the Special Issue for 18th Power Systems Computation Conference
Bilionis, I., Constantinescu, E.M., Anitescu, M.: Data-driven model for solar irradiation based on satellite observations. Sol. Energy 110, 22–38 (2014). http://www.sciencedirect.com/science/article/pii/S0038092X14004393
Boland, J.: Spatial-temporal forecasting of solar radiation. Renew. Energy 75, 607–616 (2015). http://www.sciencedirect.com/science/article/pii/S0960148114006624
Bonilla, E.V., Krauth, K., Dezfouli, A.: Generic inference in latent Gaussian process models (2016). arXiv preprint: arXiv:1609.00577
Copper, J., Sproul, A., Jarnason, S.: Photovoltaic (pv) performance modelling in the absence of onsite measured plane of array irradiance (poa) and module temperature. Renew. Energy 86, 760–769 (2016)
Cressie, N., Wikle, C.K.: Statistics for Spatio-Temporal Data. John Wiley & Sons, Hoboken (2011)
David, M., Ramahatana, F., Trombe, P., Lauret, P.: Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Sol. Energy 133, 55–72 (2016). http://www.sciencedirect.com/science/article/pii/S0038092X16300172
Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N.: Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew. Sustain. Energy Rev. 27, 65–76 (2013). http://www.sciencedirect.com/science/article/pii/S1364032113004334
Domke, J., Engerer, N., Menon, A., Webers, C.: Distributed solar prediction with wind velocity (2016)
Gutierrez-Corea, F.V., Manso-Callejo, M.A., Moreno-Regidor, M.P., Manrique-Sancho, M.T.: Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations. Sol. Energy 134, 119–131 (2016). http://www.sciencedirect.com/science/article/pii/S0038092X16300536
Hensman, J., Fusi, N., Lawrence, N.D.: Gaussian processes for big data. In: Uncertainty in Artificial Intelligence (2013)
Inman, R.H., Pedro, H.T., Coimbra, C.F.: Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39(6), 535–576 (2013)
Lauret, P., Voyant, C., Soubdhan, T., David, M., Poggi, P.: A benchmarking of machine learning techniques for solar radiation forecasting in an insular context. Sol. Energy 112, 446–457 (2015)
Li, J., Ward, J.K., Tong, J., Collins, L., Platt, G.: Machine learning for solar irradiance forecasting of photovoltaic system. Renew. Energy 90, 542–553 (2016). http://www.sciencedirect.com/science/article/pii/S0960148115305747
Lonij, V.P., Brooks, A.E., Cronin, A.D., Leuthold, M., Koch, K.: Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors. Sol. Energy 97, 58–66 (2013)
Pelland, S., Remund, J., Kleissl, J., Oozeki, T., De Brabandere, K.: Photovoltaic and solar forecasting: state of the art. iea pvps task 14, subtask 3.1. report iea-pvps t14–01: 2013. Technical report (2013). ISBN: 978-3-906042-13-8
Quiñonero-Candela, J., Rasmussen, C.E.: A unifying view of sparse approximate Gaussian process regression. J. Mach. Learn. Res. 6, 1939–1959 (2005)
Rana, M., Koprinska, I., Agelidis, V.G.: Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers. Manag. 121, 380–390 (2016)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)
Sampson, P.D., Guttorp, P.: Nonparametric estimation of nonstationary spatial covariance structure. J. Am. Stat. Assoc. 87(417), 108–119 (1992)
Shinozaki, K., Yamakawa, N., Sasaki, T., Inoue, T.: Areal solar irradiance estimated by sparsely distributed observations of solar radiation. IEEE Trans. Power Syst. 31(1), 35–42 (2016)
Titsias, M.: Variational learning of inducing variables in sparse Gaussian processes. In: Artificial Intelligence and Statistics (2009)
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.L., Paoli, C., Motte, F., Fouilloy, A.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017). http://www.sciencedirect.com/science/article/pii/S0960148116311648
Yang, D., Gu, C., Dong, Z., Jirutitijaroen, P., Chen, N., Walsh, W.M.: Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging. Renew. Energy 60, 235–245 (2013). http://www.sciencedirect.com/science/article/pii/S0960148113002759
Yang, D., Ye, Z., Lim, L.H.I., Dong, Z.: Very short term irradiance forecasting using the lasso. Sol. Energy 114, 314–326 (2015). http://www.sciencedirect.com/science/article/pii/S0038092X15000304
Acknowledgements
This work was supported by Solar Analytics Pty Ltd. and performed on behalf of the Cooperative Research Centre for Low-Carbon Living (University of New South Wales and Solar Analytics Pty Ltd.).
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Dahl, A., Bonilla, E. (2017). Scalable Gaussian Process Models for Solar Power Forecasting. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. DARE 2017. Lecture Notes in Computer Science(), vol 10691. Springer, Cham. https://doi.org/10.1007/978-3-319-71643-5_9
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