This work aims to compare several data-driven models using different Numerical Weather Prediction (NWP) input data and then to build up an outperforming Multi-Model Ensemble (MME) and its prediction intervals. Statistic, stochastic and hybrid machine-learning algorithms were developed and the NWP data from IFS and WRF models were used as input. It was found that the same machine learning algorithm differs in performance even if it make use of NWP data with comparable accuracy. This apparent inconsistency depends on the capability of the machine learning model to correct the bias error of the input data. The stochastic and the hybrid model using the same WRF input, as well as the stochastic and the non-linear statistic models using the same IFS input, produce very similar results. The MME resulting from the averaging of the best data-driven forecasts, improves the accuracy of the outperforming member of the ensemble, bringing the skill score from 42% to 46%. To reach this performance, the ensemble should include forecasts with similar accuracy but generated with the higher variety of different data-driven technology and NWP input.

Pierro, M., Bucci, F., De Felice, M., Maggioni, E., Moser, D., Perotto, A., et al. (2016). Multi-Model Ensemble for day ahead PV power forecasting improvement. In Proceedings of the 32nd European Photovoltaic Solar Energy Conference (EUPVSEC) 2016. Munich : WIP.

Multi-Model Ensemble for day ahead PV power forecasting improvement

CORNARO, CRISTINA
2016-01-01

Abstract

This work aims to compare several data-driven models using different Numerical Weather Prediction (NWP) input data and then to build up an outperforming Multi-Model Ensemble (MME) and its prediction intervals. Statistic, stochastic and hybrid machine-learning algorithms were developed and the NWP data from IFS and WRF models were used as input. It was found that the same machine learning algorithm differs in performance even if it make use of NWP data with comparable accuracy. This apparent inconsistency depends on the capability of the machine learning model to correct the bias error of the input data. The stochastic and the hybrid model using the same WRF input, as well as the stochastic and the non-linear statistic models using the same IFS input, produce very similar results. The MME resulting from the averaging of the best data-driven forecasts, improves the accuracy of the outperforming member of the ensemble, bringing the skill score from 42% to 46%. To reach this performance, the ensemble should include forecasts with similar accuracy but generated with the higher variety of different data-driven technology and NWP input.
2016
Settore ING-IND/11 - FISICA TECNICA AMBIENTALE
English
Rilevanza internazionale
Articolo scientifico in atti di convegno
Pierro, M., Bucci, F., De Felice, M., Maggioni, E., Moser, D., Perotto, A., et al. (2016). Multi-Model Ensemble for day ahead PV power forecasting improvement. In Proceedings of the 32nd European Photovoltaic Solar Energy Conference (EUPVSEC) 2016. Munich : WIP.
Pierro, M; Bucci, F; De Felice, M; Maggioni, E; Moser, D; Perotto, A; Spada, F; Cornaro, C
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/184466
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