1. Example of prediction quality for the first (10 min) and last (3 h) prediction horizon element for the same sequence of WF Danilo power production events. Results show improved forecast of neural network over persistence based forecast. Lower performance for 30-50 min horizons can be attributed to lack of on-site meteorological measurement of wind and other quantities. Prediction quality in general exceeds that of a simple persistence-based prediction. SCADA data is often messy (missing values, outliers,...) Numeric Weather Prediction (NWP) models generate forecasts on a horizontal grid with many vertical levels Wind farm Danilo. WT (red) and associated NWP grid (yellow). Among all data some is unnecessary (redundant, not useful, unknown,...) and some needs to be “artificially“ derived (e.g. 3 ) to increase the chance of successful neural network training Use of large datasets decreases neural network training speed but also improves forecast quality Wind farm power prediction is dependent on historic measurements on the individual location and NWP products which have different resolutions Wind is characterized as non-stationary, turbulent, chaotic and strongly influenced by local topography which makes wind farm power prediction a hard problem Many sites have no meteorological measurements which limits prediction quality Use of neural networks was motivated by the improvement of ultra-short term weather prediction, which cannot be covered with numerical weather prediction alone, using historic, site-specific data for Neural Network (NN) training [1] Neural networks property of universal approximation is used to model input- output relationship on a set of relevant variables State of the art results achieved by deep learning methods [2] in various fields suggest potential for improvements in very short-term wind power prediction. Motivation Issuing improved wind farm power forecasts for supporting decision-making in regulating reserve management has the advantage of being more cost- effective when compared to other solutions such as increasing backup capacities. Frequently used time-series models with well-developed theoretical background lack structural capabilities for modelling complex dynamics such as that of the wind. We present a comparison of simple persistence-based prediction to prediction performance of deep neural networks. Developed neural network methodology can be used for any location with varying number of input variables and historical data samples. Performance improvements of the proposed prediction system relative to simple persistence and to commonly used neural prediction methods are compared for the wind farm location Danilo, Croatia. Developed neural network methodology with combinations of deep and shallow neural networks can be efficiently used on any location with associated sufficiently informative measurement dataset. Wide application to wind farm power prediction on various sites is possible due to neural network structural ability to handle varying number of input variables and model highly nonlinear input-output relationships. Results Challenges Conclusions Deep Neural Networks References 2nd Frontiers in Computational Physics Conference: Energy Sciences, Zürich, Switzerland, June 3-5, 2015 2nd Frontiers in Computational Physics Conference: Energy Sciences, Zürich, Switzerland, June 3-5, 2015 Very short-term prediction of wind farm power production with Deep Neural Networks Mladen Đalto, Tomislav Lončarek, Mario Vašak, Jadranko Matuško Faculty of Electrical Engineering and Computing University of Zagreb, contact: mladen.dalto@fer.hr 1.Đalto, Mladen; Matuško, Jadranko; Vašak, Mario. „Deep neural networks for ultra-short-term wind forecasting.” Proceedings of the IEEE International Conference on Industrial Technology, ICIT 2015 Seville, pp. 1657-1663, 2015. 2.Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks." International conference on artificial intelligence and statistics. 2010. Acknowledgement Project Will4Wind – Weather Intelligence for Wind Energy is co-funded by the European Regional Development Fund, grant contract No. IPA2007/HR/16IPO/001-040507.