Day Ahead Load Forecasting using Random Forest method with meteorological variables-Reference-Cited by-同舟云学术

Day Ahead Load Forecasting using Random Forest method with meteorological variables

Published:2023-04-19 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Conference on Technologies for Sustainability (SusTech)
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Author:

Vaish Jayati¹,Siddiqui Khadim Moin¹,Maheshwari Zeel²,Kumar Amit¹,Shrivastava Sandhya¹

Affiliation:

1. Shri Ramswaroop Memorial College of Engineering and Management,Department of Electrical Engineering,Lucknow,U.P.,India

2. Northern Kentucky University,Department of Physics, Geology and Engineering Technology,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10129441/10129531/10129542.pdf?arnumber=10129542

Reference42 articles.

1. Short-Term Load Forecasting Method for AC/DC Distribution System Based on Ensemble Learning;jiang;2019 IEEE 3rd International Electrical and Energy Conference (CIEEC),2020

2. A flood forecasting model based on deep learning algorithm via integrating stacked auto encoders with bp neural network;liu;2017 IEEE Third International Conference on Multimedia Big Data (BigMM),2017

3. Ensemble learning models for short-term electricity demand forecasting

4. Short Term Load Forecasting using ANN and Ensemble Models Considering Solar Ir-radiance;vaish;2020 International Conference on Electrical and Electronics Engineering (ICE3),0

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