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Semi-Parametric Models for Electricity Consumption Forecasting

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Stochastic Processes in Communication Sciences

Electricity load forecasting faces rising challenges due to the advent of innovating technologies such as smart grids, electric cars and renewable energy production. For utilities, a good knowledge of the future electricity consumption stands as a central point for the reliability of the network, investment strategies, energy trading, optimizing the production etc. Generalized Additive Models have been investigated recently to forecasts electricity consumptions at EDF R&D. These models achieve an interesting trade-off between accuracy of forecasts and adaptation to different data sets thanks to their semi-parametric structures. We apply GAM models on different data sets corresponding to different practical applications at EDF and show how these models can be used for real forecasts at different horizon and geographical scale.

This talk is part of the Isaac Newton Institute Seminar Series series.

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