Yi Hu

College of Engineering

Works (4)

Updated: July 22nd, 2024 08:05

2024 article

BERT-PIN: A BERT-Based Framework for Recovering Missing Data Segments in Time-Series Load Profiles

Hu, Y., Ye, K., Kim, H., & Lu, N. (2024, July 1). IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol. 7.

By: Y. Hu n , K. Ye n, H. Kim n & N. Lu n 

author keywords: Load modeling; Transformers; Encoding; Data models; Bidirectional control; Power systems; Adaptation models; Bidirectional encoder representations from transformers (BERT); conservation voltage reduction; machine learning; missing data restoration; power system; transformer
UN Sustainable Development Goal Categories
Sources: ORCID, Web Of Science, NC State University Libraries
Added: July 8, 2024

2024 journal article

Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

IEEE TRANSACTIONS ON SMART GRID, 15(2), 2251–2260.

By: Y. Li *, L. Song n, Y. Hu n , H. Lee n, D. Wu *, P. Rehm, N. Lu n 

author keywords: Conservation voltage reduction; deep learning; generative adversarial nets (GAN); gated convolution; generative adversarial network; missing data restoration; self-attention mechanism
TL;DR: This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event and benchmark the performance of Load-PIN with five existing deep-learning methods. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: ORCID, Web Of Science, NC State University Libraries
Added: February 2, 2024

2024 journal article

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

IEEE TRANSACTIONS ON SMART GRID, 15(2), 2309–2320.

By: Y. Hu n , Y. Li  n, L. Song n, H. Lee n, P. Rehm, M. Makdad, E. Miller, N. Lu n 

author keywords: Load modeling; Training; Transformers; Correlation; Data models; Generative adversarial networks; Predictive models; Data augmentation; generative adversarial networks; load profile group generation; machine learning; negative sample generation; synthetic data
TL;DR: Simulation results show that MultiLoad-GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: ORCID, Web Of Science, NC State University Libraries
Added: February 2, 2024

2023 article

A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM.

By: K. Ye n, H. Kim n, Y. Hu n , N. Lu n , D. Wu * & P. Rehm

author keywords: demand response; HVAC load; load disaggregation; machine learning; convolutional neural network (CNN); transfer learning
TL;DR: Simulation results show that the proposed modified S2P algorithm outperforms the original S1P model and the support-vector machine based approach in accuracy, adaptability, and transferability. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: November 20, 2023

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