A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

  • Morten Goodwin
  • Anis Yazidi
Conference paper

DOI: 10.1007/978-3-662-44654-6_26

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)
Cite this paper as:
Goodwin M., Yazidi A. (2014) A Pattern Recognition Approach for Peak Prediction of Electrical Consumption. In: Iliadis L., Maglogiannis I., Papadopoulos H. (eds) Artificial Intelligence Applications and Innovations. AIAI 2014. IFIP Advances in Information and Communication Technology, vol 436. Springer, Berlin, Heidelberg

Abstract

Predicting and mitigating demand peaks in electrical networks has become a prevalent research topic. Demand peaks pose a particular challenge to energy companies because these are difficult to foresee and require the net to support abnormally high consumption levels. In smart energy grids, time-differentiated pricing policies that increase the energy cost for the consumers during peak periods, and load balancing are examples of simple techniques for peak regulation. In this paper, we tackle the task of predicting power peaks prior to their actual occurrence in the context of a pilot Norwegian smart grid network.

While most legacy studies formulate the problem as time-series-based estimation problem, we take a radically different approach and map it to a classical pattern recognition problem using a simple but subtle formulations. Among the key findings of this study is the ability of the algorithms to accurately detect 80% of energy consumption peaks up to one week ahead of time. Further, different classification methods have been rigorously tested and applied on real-life data from a Norwegian smart grid pilot project.

Keywords

Peak Prediction Energy Consumption Classification 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Morten Goodwin
    • 1
    • 2
  • Anis Yazidi
    • 3
  1. 1.Department of ICTUniversity of AgderGrimstadNorway
  2. 2.Teknova ASGrimstadNorway
  3. 3.Institute for ICTOslo and Akershus University College of Applied SciencesNorway

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