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A Neural Approach to Electricity Demand Forecasting

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Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

Abstract

Electricity demand forecasting is significant in supply-demand management, service provisioning, and quality. This chapter introduces a short-term load forecasting model using Fuzzy Cognitive Map, a popular neural computation technique. The historic data of intraday load levels are mapped to network nodes while a differential Hebbian technique is used to train the network’s adjacency matrix. The inferred knowledge over weekly training window is then used for demand projection with Mean Absolute Percentage Error (MAPE) of 5.87 % for 12 h lead time, and 8.32 % for 24 h lead time. A Principal Component Analysis is also discussed to extend the model for training using big data, and to facilitate long-term load forecasting.

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Authors and Affiliations

  1. CSIRO Land and Water, Bayview Ave., Clayton, Victoria, 3168, Australia

    Omid Motlagh & George Grozev

  2. Department of Computer Engineering, Technological Educational Institute of Central Greece, Old National Road Lamia-Athens, 35100, Lamia, Greece

    Elpiniki I. Papageorgiou

Authors
  1. Omid Motlagh
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  2. George Grozev
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  3. Elpiniki I. Papageorgiou
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Correspondence to Omid Motlagh .

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Editors and Affiliations

  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

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Motlagh, O., Grozev, G., Papageorgiou, E.I. (2016). A Neural Approach to Electricity Demand Forecasting. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-28495-8_12

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