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IFIP International Conference on Artificial Intelligence Applications and Innovations

AIAI 2014: Artificial Intelligence Applications and Innovations pp 265–275Cite as

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

  • Conference paper

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 436)

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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Author information

Authors and Affiliations

  1. Department of ICT, University of Agder, Grimstad, Norway

    Morten Goodwin

  2. Teknova AS, Grimstad, Norway

    Morten Goodwin

  3. Institute for ICT, Oslo and Akershus University College of Applied Sciences, Norway

    Anis Yazidi

Authors
  1. Morten Goodwin
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  2. Anis Yazidi
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Editor information

Editors and Affiliations

  1. Department of Forestry and Management of the Environment, Democritus University of Thrace, Pandazidou 193, 68200, Orestiada, Greece

    Lazaros Iliadis

  2. Department of Digital Systems, University of Piraeus, 80, Karaoli and Dimitriou Str., 18534, Piraeus, Greece

    Ilias Maglogiannis

  3. Department of Computer Science and Engineering, Frederick University, 7 Yianni Frederickou Str., Pallouriotissa, 1036, Nicosia, Cyprus

    Harris Papadopoulos

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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. https://doi.org/10.1007/978-3-662-44654-6_26

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  • DOI: https://doi.org/10.1007/978-3-662-44654-6_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44653-9

  • Online ISBN: 978-3-662-44654-6

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