Publications (6)
Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning
Zichao Meng, Ye Guo, Wenjun Tang +1
This paper proposes a nonparametric multivariate density forecast model based on deep learning. It not only offers the whole marginal distribution of each random variable in foreca…
Machine Learning Approach to Uncovering Residential Energy Consumption Patterns Based on Socioeconomic and Smart Meter Data
Wenjun Tang, Hao Wang, Xian-Long Lee +1
The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns…
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface
Guodong Yin, Mufeng Zhou, Yiming Chen +9
Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck. Computing-in…
Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process
Yifei Xu, Ye Guo, Wenjun Tang +3
The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart…
Data-Driven Online Interactive Bidding Strategy for Demand Response
Kuan-Cheng Lee, Hong-Tzer Yang, Wenjun Tang
Demand response (DR), as one of the important energy resources in the future's grid, provides the services of peak shaving, enhancing the efficiency of renewable energy utilization…
A Multivariate Density Forecast Approach for Online Power System Security Assessment
Zichao Meng, Ye Guo, Wenjun Tang +2
A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins…