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An artificial immune network based novel approach to predict short term load forecasting

Author

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  • Arpita Samanta Santra

    (Department of Information Management, Yuan Ze University, Chung-Li, Taiwan)

  • Cheng-Chin Taso

    (Department of Information Management, Yuan Ze University, Chung-Li, Taiwan)

  • Pei-Chann Chang

    (Department of Information Management, Yuan Ze University, Chung-Li, Taiwan)

Abstract

Recent trends of Short-Term Load Forecasting (STLF) is a key issue to regulate power in the electricity market. Many researchers have performed research in this area but it still needs an accurate and robust load forecast method. In this paper, we propose a novel Artificial Immune Network (AIN) based approach to predict forecast load depending on last three days’ mean actual load. The approach creates an immune memory using time series to forecast one day ahead hourly loads. The method takes hourly loads separately as an individual daily time series and considers it as an antigen, an affinity is calculated between an antigen and antibody in Immune Networks (INs). A cross reactivity threshold is used to find the appropriate cluster for an antigen in an immune network. The historical dataset of Poland is trained and tested by this method which predicts more accurately compare with most recent existing STLF methods, such as simple nearest neighbor (NN), Multilayer Perceptron (MLP), Fuzzy Estimators (FE) and Artificial Immune System (AIS).

Suggested Citation

  • Arpita Samanta Santra & Cheng-Chin Taso & Pei-Chann Chang, 2017. "An artificial immune network based novel approach to predict short term load forecasting," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(3), pages 79-88.
    • Handle: RePEc:apb:jaterr:2017:p:79-88
    DOI: 10.20474/jater-3.3.3
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    References listed on IDEAS

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    1. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
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    Cited by:

    1. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.

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