Load forecasting using Two-level Heterogeneous Ensemble Method for Smart Metered Distribution System

Articles in Press

Document Type : Article

Authors

Department of Electrical Engineering, NIT Patna, Patna, Bihar, 800005, India

Abstract

A heterogeneous ensemble methods for short-term and mid-term load forecasting is proposed here. The proposed approach comprises of a two-level hierarchy of machine learning based methods and classical methods to form the ensemble forecaster, where output of the first-stage forecasters are used as input in the second stage. Artificial Neural Network and Support Vector Regression methods are incorporated in the proposed approach as ML forecasters, while Holt’s double exponential smoothening method and multiple linear regression method are included as classical forecasters. The proposed two-level ensemble approach forecasts realistic smart metered data more accurately and efficiently for multiple short-term and mid-term load forecasting scenarios with improved accuracy compared to any individual single stage forecasting methods. The prediction accuracy is shown to improve manifolds for the tested practical system. The proposed model also show improvements compared to existing ensemble based model.

Keywords

Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 31 January 2023

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History

  • Receive Date: 17 January 2022
  • Revise Date: 16 July 2022
  • Accept Date: 30 January 2023

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