Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
Cristina Cornaroa,b, Marco Pierroa,e, Francesco Buccia, Matteo De Feliced,
Enrico Maggionic, David Mosere, Alessandro Perottoc, Francesco Spadac,
Multi-Model Ensemble for day ahead PV power
forecasting improvement
aDepartment of Enterprise Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome,
Italy, e-mail: [email protected], [email protected], [email protected], University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy 7cIdeam Srl, via Frova 34 Cinisello Balsamo, Italy, e-mail: alessandro.perotto, enrico.maggioni,
[email protected] R.C., ENEA Climate Modelling Laboratory, Rome, Italy e-mail: [email protected] Research, Viale Druso, 1, 39100 Bolzano, Italy e-mail: [email protected]
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
Large share of PV power introduces into the electric demand a stochastic variability dependent on meteorological conditions: residual load = load-PV generation
Why day ahead PV power forecast
example of regional load and PV generation trend with 8.3% PV penetration
PV Power
ramp
Reserve
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
Day ahead PV power forecast could mitigate these effects
Why day ahead PV power forecast
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.
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
PREDICTION INTERVALS
• to reduce uncertainty in the
electric demand so that lower
energy reserves are needed
• for energy trading issues
Why day ahead PV power forecast
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
•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.
Aim of the work
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
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
In the last years a data-driven approach has been extensively tested for PV
power generation forecast from 24 to 72 hours horizon.
Hybrid models could be obtained using
different models in series.
While combining together different forecast models a Multi-Model Ensemble can be built.
Data driven approach for day ahead PV production fo recast
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
Historical weather and PV power production data
Four years of monitored irradiance, temperature and production data
(2011-2014) 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
Data used for training and test
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
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.
Two Numerical Weather Prediction data were used as models input
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
Data used for training and test
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
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)
Data driven techniques
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
1
2
Based on Ensemble of MLPNNs using NWP inputs from WRF
Hybrid model based on MOSRH + ANNs Ensemble using NWP inputs from WRF
PV power
forecast
Data driven forecasting models
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
4
3 Based on Ensemble of MLPNNs using GHI inputs from ECMWF
Based on Support Vector Machine using GHI inputs from ECMWF
Data driven forecasting models
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
Results: forecast models accuracy
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
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
Results: Multi Model Ensemble construction and eval uation
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
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)
Results: Multi Model Ensemble construction and eval uation
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
The prediction Intervals could be calculated forecasting the standard deviation of the residuals (σfor) under the hypothesis that the residuals are normally distributed with zero expected value
Ensemble of MLPNNs using MME power forecast
Results: MME prediction intervals construction and evaluation
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
Observation (dots), MME forecast (white line) and prediction intervals (grey lines) for five days of 2011
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.
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
• 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%.
Conclusions
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
C. Cornaro, F. Bucci, M. Pierro, F. Del Frate, S. Peronaci, A. Taravat, 2015. 24-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, 2015. Master optimization process based on neural network
ensemble for 24h solar radiation forecast. Solar Energy, 111, 297-312, 2015.
M. Pierro, F. Bucci, C. Cornaro, E. Maggioni, A. Perotto, M. Pravettoni, F. Spada, 2015.
Model Output Statistics cascade to improve day ahead solar irradiance forecast. Solar
Energy, Volume 117, July 2015, Pages 99-113.
M. Pierro, F. Bucci, M. De Felice, E. Maggioni, D. Moser, A. Perotto, F.Spada, C.Cornaro,
2016. Multi-Model Ensemble for day ahead prediction of photovoltaic power generation.
Solar Energy, Volume 134, September 2016, Pages 132–146.
M. Pierro, F. Bucci, M. De Felice, E. Maggioni, D. Moser, A. Perotto, F.Spada, C.Cornaro,
2016. Deterministic and stochastic approaches for day-ahead solar power
forecasting.Published online J. Sol. Energy Eng., doi: 10.1115/1.4034823.
References
Dipartimento Ingegneria dell’ImpresaUniversità degli Studi di Roma Tor Vergata
Mercati energetici e metodi quantitativi: un ponte tra Università e Aziende, 13 0ttobre 2016, Padova
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