Multi-Model Ensemble for day ahead PV power forecasting improvement

Transcription

1 Multi-Model Ensemble for day ahead PV power forecasting improvement Cristina Cornaro a,b, Marco Pierro a,e, Francesco Bucci a, Matteo De Felice d, Enrico Maggioni c, David Moser e,alessandro Perotto c, Francesco Spada c, a Department of Enterprise Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, Italy, cornaro@uniroma2.it, marco.pierro@gmail.com, frabucci@gmail.com b CHOSE, University of Rome Tor Vergata, Via del Politecnico 1, Rome, Italy 7 c Ideam Srl, via Frova 34 Cinisello Balsamo, Italy, alessandro.perotto, enrico.maggioni, francesco.spada@ideamweb.com d Casaccia R.C., ENEA Climate Modelling Laboratory, Rome, Italy matteo.defelice@enea.it e EURAC Research, Viale Druso, 1, Bolzano, Italy david.moser@eurac.edu

2 Why day ahead PV power forecast Large share of PV power introduces into the electric demand a stochastic variability dependent on meteorological conditions: residual load = load-pv generation Reserve PV Power ramp example of regional load and PV generation trend with 8.3% PV penetration

3 Why day ahead PV power forecast Day ahead PV power forecast could mitigate these effects PV POWER FORECAST to improve the capability of residual load tracking and transmission scheduling to obtain a better match between the day-ahead market commitment and the real PV production, reducing the energy imbalance costs.

4 Why day ahead PV power forecast PREDICTION INTERVALS to reduce uncertainty in the electric demand so that lower energy reserves are needed for energy trading issues

5 Aim of the work to develop and test several data-driven models for day ahead site PV power forecast (with hour granularity) using different NWP forcing to build up an outperforming Multi-Model Ensemble with its prediction intervals.

6 Data driven approach for day ahead PV production forecast In the last years a data-driven approach has been extensively tested for PV power generation forecast from 24 to 72 hours horizon. This approach involves a wide range of machine learning techniques that can be built making use of Numerical Weather Prediction (possibly corrected by Model Output Statistic) and weather and PV generation historical data. These algorithms try to reconstruct relationships between input and output through a training and validation procedure on historical data Hybrid models could be obtained using different models in series. While combining together different forecast models a Multi-Model Ensemble can be built.

7 Data used for training and test Historical weather and PV power production data Four years of monitored irradiance, temperature and production data ( ) from a 662 kwp Cadmium Telluride PV plant, located in Bolzano (Italy), were employed to train and test the models. Data were acquired every 15 minutes and then averaged each hours Daily reference and final yield Monthly average of daily power yield

8 Data used for training and test Two Numerical Weather Prediction data were used as models input 1) NWP generated by the Weather Research and Forecasting (WRF ARW 3.6.1) mesoscale model developed by National Center of Atmospheric Research (NCAR) Forecast horizon: 24 hour Temporal output resolution: 20 minute and then averaged each hours Spatial resolution 3 km centered on the region of interest Initial and contour data for model initialization: GSF model Radiation scheme: Rapid Radiative Transfer Model (RRTM) Global Horizontal Irradiance (GHI) provided by WRF was post processed with an original Model Output Statistic called MOSRH. 2) NWP generated by the Integrated Forecasting System (IFS) the global weather forecasting model from the European Centre for Medium-Range Weather Forecasts(ECMWF). Forecast horizon: 24 hour Temporal output resolution: 1 hour Spatial resolution 16 km Radiation scheme: RRTM

9 Data driven techniques Two data-driven techniques were adopted to built the forecast models 1. Qualified ensemble of 300 MLPNNs with one hidden layer 500 MLPNN with the optimal hidden neuron (S) were generated using a Sub-Sample Random Validation Procedure on the training data A qualified ensemble was selected (around 300 ANNs), choosing all the ANNs with the MSE lower than the average MSE of the 500 networks Forecast was obtained by averaging the ensemble outputs. 2. Support Vector Regression method called ε-svr, Gaussian Kernel was adopted an extensive grid search on more than 400 combinations was performed to set the model parameters: regularization parameter (C), insensitive zone (ε), std (γ)

10 Data driven forecasting models 1 Based on Ensemble of MLPNNs using NWP inputs from WRF PV power forecast 2 Hybrid model based on MOSRH + ANNs Ensemble using NWP inputs from WRF

11 Data driven forecasting models 3 Based on Ensemble of MLPNNs using GHI inputs from ECMWF 4 Based on Support Vector Machine using GHI inputs from ECMWF

12 Results: forecast models accuracy

13 Results: Multi Model Ensemble construction and evaluation Since all the models show similar errors in different predicted typologies of days (identifies by daily clear sky predicted by WRF), the Multi-Model ensemble was built just averaging the different prediction trajectories

14 Results: Multi Model Ensemble construction and evaluation The MME outperforms the best model of the ensemble GTNN(ECMWF) MME reaches a skill score with respect to the RMSE of PM of 46% while the best forecast model GTNN(ECMWF) obtains a skill score of 42%. It was proved that the best performance of multi-model approach could be achieved averaging the higher variety of different algorithms and different NWP models with the only condition that all the ensemble members should have similar RMSE (RMSE difference less than 1% measured on one year data)

15 Results: MME prediction intervals construction and evaluation The prediction Intervals could be calculated forecasting the standard deviation of the residuals (σ for ) under the hypothesis that the residuals are normally distributed with zero expected value Ensemble of MLPNNs using MME power forecast

16 Results: MME prediction intervals construction and evaluation The frequency of observations falling inside the prediction interval is greater or equal than the confidence level associated to that interval for all years considered. Observation (dots), MME forecast (white line) and prediction intervals (grey lines) for five days of 2011

17 Conclusions Models based on different non linear machine learning algorithms (stochastic or statistic) making use of the same NWP data provide forecast with similar accuracy. The best performance of multi-model approach could be achieved averaging the higher variety of different algorithms and different NWP models with the only condition that all the ensemble members should have similar RMSE (RMSE difference less than 1% measured on one year data). The MME reaches a skill score with respect to the RMSE of PM of 46% while the best forecast model obtains a skill score of 42%.

18 References C. Cornaro, F. Bucci, M. Pierro, F. Del Frate, S. Peronaci, A. Taravat, H solar irradiance forecast based on neural networks and numerical weather prediction. J. Sol. Energy Eng. 2015; 137(3). C. Cornaro, M. Pierro, F. Bucci, Master optimization process based on neural network ensemble for 24h solar radiation forecast. Solar Energy, 111, , M. Pierro, F. Bucci, C. Cornaro, E. Maggioni, A. Perotto, M. Pravettoni, F. Spada, Model Output Statistics cascade to improve day ahead solar irradiance forecast. Solar Energy, Volume 117, July 2015, Pages M. Pierro, F. Bucci, M. De Felice, E. Maggioni, D. Moser, A. Perotto, F.Spada, C.Cornaro, Multi-Model Ensemble for day ahead prediction of photovoltaic power generation. Solar Energy, Volume 134, September 2016, Pages M. Pierro, F. Bucci, M. De Felice, E. Maggioni, D. Moser, A. Perotto, F.Spada, C.Cornaro, Deterministic and stochastic approaches for day-ahead solar power forecasting.published online J. Sol. Energy Eng., doi: /

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