Load Forecasting Techniques for Power System: Research Challenges and Survey-Reference-Cited by-同舟云学术

Load Forecasting Techniques for Power System: Research Challenges and Survey

Published:2022 Issue: Volume:10 Page:71054-71090
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ahmad Naqash¹,Ghadi Yazeed²^ORCID,Adnan Muhammad¹^ORCID,Ali Mansoor³^ORCID

Affiliation:

1. Department of Electrical Engineering, National University of Computer and Emerging Sciences (FAST), Chiniot-Faisalabad Campus, Faisalabad, Pakistan

2. Department of Software Engineering, Al Ain University, Al Ain, United Arab Emirates

3. Electrical Engineering Department, Ecole de Technologie Superieure (ETS), Montreal, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09812604.pdf?arnumber=9812604

Reference368 articles.

1. A survey of artificial neural network in wind energy systems

2. Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

3. A review of data-driven approaches for prediction and classification of building energy consumption

4. Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

5. A review of data-driven building energy consumption prediction studies

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