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

Arpita Samanta Santra, Cheng-Chin Taso and Pei-Chann Chang
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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

Journal of Advances in Technology and Engineering Research, 2017, vol. 3, issue 3, 79-88

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).

Keywords: Short-term Load Forecasting; Immune Memory; Immune Networks (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:apb:jaterr:2017:p:79-88

DOI: 10.20474/jater-3.3.3

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