A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid-Reference-Cited by-同舟云学术

A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid

Author:

Alquthami Thamer¹^ORCID,Zulfiqar Muhammad²,Kamran Muhammad¹,Milyani Ahmad H.¹^ORCID,Rasheed Muhammad Babar³^ORCID

Affiliation:

1. Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia

2. Department of Electrical Engineering, University of Engineering and Technology Lahore, Lahore, Pakistan

3. Department of Electronics and Electrical Systems, The University of Lahore, Lahore, Pakistan

Funder

Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia

European Union Horizon 2020 Research and Innovation Program through the Marie Sklodowska-Curie

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference72 articles.

1. Artificial neural network approach for short term load forecasting for Illam region;hayati;World Acad Sci Eng Technol,2007

2. Making smart grids smarter by using machine learning;pisica;Proc 46th Int Univ Power Eng Conf (UPEC),2011

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