Wind Power Forecasting Using Non-Linear ARMAX Models and Neural Networks

Transcription

1 Wind Power Forecasting Using Non-Linear ARMAX Models and Neural Networks Frede Aakmann Tøgersen & Kim Emil Andersen Wind & Site Competence Centre Technology R & D Vestas Wind Systems A/S

2 Agenda Introducing a case study used in developing the forecast method Explaining power curves for wind turbine generators Fitting power curve model Windspeed measured at turbine and power prediction Numerical weather predictions models (NWP) Correcting NWP data to turbine data using ARMAX models and NN Future work 2 The 30th Annual International Symposium on Forecasting, June

3 A case study 3 The 30th Annual International Symposium on Forecasting, June

4 Google Earth Map 4 The 30th Annual International Symposium on Forecasting, June

5 Tararua Wind Farm (Panoramio, Alan Blackley) 5 The 30th Annual International Symposium on Forecasting, June

6 Observed Power and Wind Speed From physics the power is related to the windspeed as given by the following formula. Lower bound is 0 whereas upper bound is determined by components of the turbine 6 The 30th Annual International Symposium on Forecasting, June

7 Different Power Curves For Different Air Density Different theoretical power curves for different air densities. Mismatch is mostly due to the windspeed is measured behind rotor But canalsobeattributed to difficult climatic conditions (complex terrain) 7 The 30th Annual International Symposium on Forecasting, June

8 Five Parameters Logistic Model 8 The 30th Annual International Symposium on Forecasting, June

9 Other Variables Influencing Power Curves TI: turbulence intensity, coefficient of variation of windspeed Inflow angle: the angle between wind direction and rotor plane (depends on the orography of the site) Windshear: characterizes how fast windspeed increases with increasing height above ground 9 The 30th Annual International Symposium on Forecasting, June

10 10 The 30th Annual International Symposium on Forecasting, June

11 Splitting Oberved Data After MET And CFD Data From meteorological data: Wind speed, wind direction Pressure, temperature, humidity giving air density Turbulence kinetic energy (TKE), substitute for TI From CFD (for each turbine) Wind directions is split into usually 12 wind sectors Sectorwise wind shear Sectorwise TI (if no TKE) Sectorwise inflow angle 11 The 30th Annual International Symposium on Forecasting, June

12 Power Curves Fits 12 The 30th Annual International Symposium on Forecasting, June

13 13 The 30th Annual International Symposium on Forecasting, June

14 Wind Speed in Two Weeks for One Turbine 14 The 30th Annual International Symposium on Forecasting, June

15 Power Prediction for One Turbine 15 The 30th Annual International Symposium on Forecasting, June

16 One to One Correspondance? 16 The 30th Annual International Symposium on Forecasting, June

17 Weather Forecast Numerical weather prediction uses current weather conditions as input into mathematical models of the atmosphere to predict the weather. It usually operates on the scale of up to 1000 km. There exists numerous models, e.g. ETA, WRF, RAMS, MM5, ALADIN to mention a few Mesoscale meteorology is the study of weather systems smaller than synoptic scale systems but larger than microscale and stormscale cumulus systems. Horizontal dimensions generally range from around 5 kilometers to several hundred kilometers. 17 The 30th Annual International Symposium on Forecasting, June

18 Turbine Layout and Grid Points from Mesoscale Model 18 The 30th Annual International Symposium on Forecasting, June

19 Wind Speed Predictions from Mesoscale Model 19 The 30th Annual International Symposium on Forecasting, June

20 Power Predictions from Mesoscale Model Wind Speeds 20 The 30th Annual International Symposium on Forecasting, June

21 ARMAX models Linear model: ARMAX(p,q,b) where are the parameters of the autoregressive part of the model, are the parameters of the moving average part of the model are the parameters of the exogenous part of the model. Standard statistical methods exist to estimate the parameters of the linear model 21 The 30th Annual International Symposium on Forecasting, June

22 Non-linear ARMAX models The model parameters depends on the functional form of No generic methods from the statistical community to estimate the parameters of the model. However neural network is able to approximate the model using non-linear activation functions at the nodes of the layers of the network. 22 The 30th Annual International Symposium on Forecasting, June

23 Output from Neural Network Forecasts is based on observed turbine data. These will not be available at prediction time but must be estimated by some iterative prediction method (not implemented yet) 23 The 30th Annual International Symposium on Forecasting, June

24 Output from Neural Network 24 The 30th Annual International Symposium on Forecasting, June

25 Output from Neural Network 25 The 30th Annual International Symposium on Forecasting, June

26 Future work Implement a procedure for iterative predictions of future values of turbine data Other input variables to consider such as e.g. wind directions etc. Explore how many training data is necessary for a good training and reliable forecasts How many mesoscale gridpoints is needed as input? How do we find the optimal spatial resolution of the mesoscale models? How far into the future do we need or dare to forecast? How often per day do we need to forecast? 26 The 30th Annual International Symposium on Forecasting, June

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