论文信息 - Multi-Plant Photovoltaic Energy Forecasting Challenge: Second Place Solution

Multi-Plant Photovoltaic Energy Forecasting Challenge: Second Place Solution

This paper presents the approach we took to solve the Multi-Plant Photovoltaic Energy Forecasting Challenge for ECML/PKDD 2017. The approach we took granted us the second place of that challenge. In the paper, we will present how we moved from standard regression techniques to simple function optimization to tackle the challenge.

Yann Dauxais | Clément Gautrais | Maël Guillemé

[1] Prashant J. Shenoy,et al. Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[2] Henrik Madsen,et al. Online short-term solar power forecasting , 2009 .

[3] H. Pedro,et al. Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

[4] Peng Wang,et al. Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2011, IEEE Transactions on Industry Applications.

[5] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .

[6] Michelangelo Ceci,et al. Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction? , 2017, IEEE Transactions on Industrial Informatics.

[7] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..