Multi-Model Ensemble for day ahead PV power forecasting …unponte2016.stat.unipd.it/slides/Cornaro.pdf · 2016. 10. 12. · Dipartimento Ingegneria dell’Impresa Università degli

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]



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



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.



PREDICTION INTERVALS

• to reduce uncertainty in the

electric demand so that lower

energy reserves are needed

• for energy trading issues




•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



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



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



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



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



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



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



Results: forecast models accuracy



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



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



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



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.



• 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



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



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Multi-Model Ensemble for day ahead PV power forecasting …unponte2016.stat.unipd.it/slides/Cornaro.pdf · 2016. 10. 12. · Dipartimento Ingegneria dell’Impresa Università degli