Multi-Model Ensemble for day ahead prediction of photovoltaic power generation

Pierro, Marco; Bucci, Francesco; Felice, Matteo De; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

doi:10.1016/j.solener.2016.04.040

Cited by 98 publications

(44 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [12], the proposed methodology had a skill score between 33% and 36% depending on the plant examined, for five plants located in northern Spain. The hourly forecast for a 662 kW plant in Northern Italy was evaluated at [15] using several forecasting techniques and an ensemble of the forecasts obtained from these models. The individual models produced skill scores between 35% and 42%, while the use of the ensemble of models technique made it possible to achieve a score of 46%.…”

Section: Discussionmentioning

confidence: 99%

“…The probabilistic forecast intervals were instead estimated using a k-nearest neighbors regression procedure. Pierro et al [15] created a multi-model forecast ensemble with forecasts of two different weather models, obtaining performances superior to the best among the members of the forecast ensemble. The main reason for this was the ability of the ensemble averaging to reduce the noise of individual predictors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

Massidda

Marrocu

2018

Energies

View full text Add to dashboard Cite

Abstract:The inclusion of photo-voltaic generation in the distribution grid poses technical difficulties related to the variability of the solar source and determines the need for Probabilistic Forecasting procedures (PF). This work describes a new approach for PF based on quantile regression using the Gradient-Boosted Regression Trees (GBRT) method fed by numerical weather forecasts of the European Centre for Medium Range Weather Forecast (ECMWF) Integrated Forecasting System (IFS) and Ensemble Prediction System (EPS). The proposed methodology is compared with the forecasts obtained with Quantile Regression using only IFS forecasts (QR), with the uncalibrated EPS forecasts and with the EPS forecasts calibrated with a Variance Deficit (VD) procedure. The proposed methodology produces forecasts with a temporal resolution equal to or better than the meteorological forecast (1 h for the IFS and 3 h for EPS) and, in the case examined, is able to provide higher performances than those obtained with the other methods over a forecast horizon of up to 72 h.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

Massidda

Marrocu

2018

Energies

View full text Add to dashboard Cite

show abstract

“…As in ref. [11], the authors compare several data-driven models using input data from two NWPs and building two artificial hybrid and stochastic ensemble models based on ANN, the model that combines multiple models outperforms the rest of the models. It points out that the ensemble is enhanced by including forecasts with similar accuracy, but generated from NWP data of higher variance and different data-driven techniques.…”

Section: Introductionmentioning

confidence: 99%

Random forest ensemble of support vector regression models for solar power forecasting

Abuella

Chowdhury

2017

2017 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

View full text Add to dashboard Cite

Abstract-To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.

show abstract

“…Additionally, the ANN can be used in conjunction with numerical weather prediction (NWP) models [28], which can be based on satellite and land-based sky imaging [29]. NWP models have been also used to build an outperforming multi-model ensemble (MME) for day-ahead prediction of PV power generation [30]. More complex schemes are proposed in [31], where an ANN is used to improve the performance of baseline prediction models, i.e., a physical deterministic model based on cloud tracking techniques, an ARMA model and a k-nearest neighbor (kNN) model, and in [32], where a Kalman filter is used in conjunction with a state-space model (SSM).…”

Section: Introductionmentioning

confidence: 99%

Prediction in Photovoltaic Power by Neural Networks

et al. 2017

View full text Add to dashboard Cite

Abstract:The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

show abstract

Multi-Model Ensemble for day ahead prediction of photovoltaic power generation

Cited by 98 publications

References 27 publications

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

Random forest ensemble of support vector regression models for solar power forecasting

Prediction in Photovoltaic Power by Neural Networks

Contact Info

Product

Resources

About