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Short-Term Electric Load Forecasting Using Auto Regressive-Candidates Area Shifting Technique

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Electric power load forecasting is needed to be used as a consideration in providing electricity in the future. A combination of the Auto Regressive model and the Candidates Area Shifting Technique is proposed to predict the demand for electrical loads. The results of the proposed method are compared with the ones of the hybrid Particle Swarm Optimization-Support Vector Regression and FCM Clustering Technique methods. The results show that the proposed method provides better performance than the other ones. The three methods provide mean absolute percentage error and maximum absolute percentage error respectively as follows: Particle Swarm Optimization-Support Vector Regression Method 2.859% and 9,516%, FCM Clustering 1.032% and 2.798%, and Auto Regressive-Candidates Area Shifting Technique 0.298%  and 0.872%, respectively.
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Electric Load Forecasting; Autoregressive; Candidates Area Shifting; Accurate

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