Micro-Scale Modeling combined with Statistical Learning for

1. Micro-Scale Modeling combined with Statistical Learningfor Short-Term Wind Power Forecasting Jérémie JUBAN – Jean-Claude Houbart – María BULLIDO GARCIA–Didier DELAUNAYMETEODYN, 14 Bd. Winston Churchill, Nantes, France PO. ID 288 Abstract Case Study • We consider here a real wind farm in China with complex terrain and weather regime (snow fall, highly variable wind regime) • Period of study • Learning : 06/2010 to 02/2012 • Testing :03/2012 to 11/2012 • Forecast Horizon +22h to +46h • Forecast steps 15 min - Runs 4/day • Input variables • NWP : wind speed, direction • stability, temp., density, pressure • Park production Short-term wind power forecasting is now considered as a mature field. It has been widely put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast products around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning based on neural networks. We present here both our innovative strategy to reduce error an evaluation of the overall performance from real wind farm in complex terrain. Objectives It is widely admitted [1] that an optimal combination of both physical and statistical modeling allows to reach the highest forecast performance. Statistical learning based on past measures allows to reach high performance forecasts by explicitly minimizing average scores such as MAE and RMSE. However, it is based on the hypothesis that future will remain similar to the past or at least the recent past (stationary hypothesis). On the other hand, physical modeling allows to predict environment changes such as maintenance, repowering, forest cut. Meteodyn Forecast already benefits from an advanced physical modeling based on our expertise with Meteodyn WT. Our software features tailored mesoscale NWP inputs, state-of-the art micro-CFD modeling including atmospheric stability, forest model, turbines wakes, advanced power curve modeling (WTG). The objective here is to minimize the prediction error by introducing automatic error correction while keeping the advantage of our full physical modeling. Results • Bias on testing set - Overall Relative Improvement 42 % • Mean Absolute Error on testing set - Overall Relative Improvement 5 % Methods • We consider here a “black box” statistical correction based on park production measurements. The “black-box” learning model used here are Artificial Neural networks. • Artificial Neural Networks (ANN) date backs to early work of P. Werbos, D.E. Rumelhart, G.E. Hinton and R.J. Williams in the 70’s. ANN are now widely used as “black box” models in various field [2]. A fully-connected ANN architecture can be defined by two quantities: the number of layer and the number of neurons per layer. • For example : is a 2-3-2-1 Artificial Neural Network. • There exist a vast number of possible network, with highly variable performances. A too simple neural network is not able to fully correct. A two complicated network will over-fit the input data. One classical way to select among possible networks architectures is to use Genetic Algoritms [3]. Based on that we have developed a tailored architecture optimization comprising input variable selection . Mesoscale NWP PhysicalModeling Neural Network • Root Mean Square Error on testing set - Overall Relative Improvement 16 % Measurements Conclusions It has been widely recognize that an optimal combination of statistical and physical modeling is central to high performance forecasting [1]. However, combining both in an optimal way remains a difficult task. Based on our expertise in physical micro-CFD modeling coupled with an advanced statistical learning correction, we have demonstrated our ability to reach high performance forecasting, with an error (RMSE) reduce to a15%-16% bound for the next day (+22hto +46h) for highly complex terrain and weather regimes. We confirmed here that introducing advanced statistical learning leads to significant improvement over a pure (even advanced) physical approach (16% relative improvement). Increasing Model Complexity     Reference  [1] : Giebel G., Brownsword R., Kariniotakis G., Denhard M., Draxl C. The State-Of-The-Art in Short-Term Prediction of Wind Power A Literature Overview, 2nd Edition. Project report for the Anemos.plus and SafeWind projects., Risø, Roskilde, Denmark, 2011 [2] : Smith, Murray, Neural Networks for Statistical Modeling, Van Nostrand Reinhold, 1993. [3] : D. Whitley, "Applying Genetic Algorithms to Neural Network Problems," International Neural Network,1988 Genetic Algorithm example: 1) start with 3 initial networks; 2) compute their performance 3) Retain only the best networks 4) Cross the best networks to get possible better betworks. EWEA 2013, Vienna, Austria: Europe’s Premier Wind Energy Event