A Novel Approach for Electricity Load Forecasting using Hybrid Deep Learning Models-Reference-Cited by-同舟云学术

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A Novel Approach for Electricity Load Forecasting using Hybrid Deep Learning Models

Published:2024-02-16 Issue: Volume:9 Page:570-575
ISSN:
Container-title:2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)
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Author:

J Shyam Prasath¹,B Natarajan²,K Venkatraman¹,Nallusamy M.³,Revathi M.⁴,Muruganantham T.⁵

Affiliation:

1. Amrita Vishwa Vidyapeetham,Amrita School of Computing,Department of Computer Science and Engineering,Chennai,India

2. VIT University,School of Computer Science and Engineering,Chennai,Tamilnadu,India

3. Roever Engineering College,Department of Computer Science and Engineering,Perambalur,Tamilnadu,India

4. University College of Engineering Bharathidasan Institute of Technology Campus, Anna University,Department of Computer Science and Engineering,Tiruchirappalli,Tamilnadu,India

5. K. Ramakrishnan College of Engineering,Dept. of ECE,Tiruchirappalli,Tamil Nadu,India,621112

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10502393/10502381/10502461.pdf?arnumber=10502461

Reference28 articles.

1. A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks

2. Long Short-Term Memory

3. A Comparison of ARIMA and LSTM in Forecasting Time Series

4. A comparative analysis of forecasting financial time series using ARIMA, LSTM, and BiLSTM;Siami-Namini,2019

5. Forecasting economics and financial time series: ARIMA vs. LSTM;Siami-Namini,2018

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