Demand forecasting in smart homes using deep learning techniques-Reference-Cited by-同舟云学术

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Demand forecasting in smart homes using deep learning techniques

Published:2023-05-19 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Control, Communication and Computing (ICCC)
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Author:

Steephen Sanjay¹,S Reshmi Krishnan¹,R Sheeba¹,Sundar Sumod²

Affiliation:

1. APJ Abdul Kalam Technological University (KTU),TKM College of Engineering, Kollam,Dept. of Electrical and Electronics Engineering,Kerala

2. APJ Abdul Kalam Technological University (KTU),Centre for Artificial Intelligence, TKM College of Engineering, Kollam,Kerala

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10164829/10164822/10165034.pdf?arnumber=10165034

Reference20 articles.

1. Non-intrusive Load Monitoring Method Based on Transfer Learning and Sequenceto-point Model;li;2021 IEEE Sustainable Power and Energy Conference (iSPEC),2021

2. Deep Learning based Smart Meter Data Analytics for Electricity Load Prediction

3. Classification performance using gated recurrent unit recurrent neural network on energy disaggregation

4. Multimodal Feature Extraction and Fusion Deep Neural Networks for Short-Term Load Forecasting

5. Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Leveraging Weather Parameters in Generative Adversarial Networks for Energy Consumption Prediction;2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES);2024-06-21

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