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Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models

  • Pascal PompeyEmail author
  • Alexis Bondu
  • Yannig Goude
  • Mathieu Sinn
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Abstract

The emergence of Smart Grids is posing a wide range of challenges for electric utility companies and network operators: Integration of non-dispatchable power from renewable energy sources (e.g., photovoltaics, hydro and wind), fundamental changes in the way energy is consumed (e.g., due to dynamic pricing, demand response and novel electric appliances), and more active operations of the networks to increase efficiency and reliability. A key in managing these challenges is the ability to forecast network loads at low levels of locality, e.g., counties, cities, or neighbourhoods. Accurate load forecasts improve the efficiency of supply as they help utilities to reduce operating reserves, act more efficiently in the electricity markets, and provide more effective demand-response measures. In order to prepare for the Smart Grid era, there is a need for a scalable simulation environment which allows utilities to develop and validate their forecasting methodology under various what-if-scenarios. This paper presents a massive-scale simulation platform which emulates electrical load in an entire electrical network, from Smart Meters at individual households, over low- to medium-voltage network assets, up to the national level. The platform supports the simulation of changes in the customer portfolio and the consumers’ behavior, installment of new distributed generation capacity at any network level, and dynamic reconfigurations of the network. The paper explains the underlying statistical modeling approach based on Generalized Additive Models, outlines the system architecture, and presents a number of realistic use cases that were generated using this platform.

Keywords

Mean Square Error Wind Power Wind Farm Smart Grid Area Under Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pascal Pompey
    • 1
    Email author
  • Alexis Bondu
    • 2
  • Yannig Goude
    • 2
  • Mathieu Sinn
    • 1
  1. 1.IBM Research, Damastown Industrial EstateDublin 15Ireland
  2. 2.EDF R&DClamartFrance

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