Portrait of Di Wu is unavailable

Di Wu

Associate Academic Member
di.wu@mila.quebec
Associate professor, Concordia University, Department of Mechanical, Industrial and Aerospace Engineering
Research Topics
Energy Systems
Large Language Models (LLM)
Reinforcement Learning
Time Series Modeling
Trustworthy AI

Publications

Learning Assisted Identification of Scenarios Where Network Optimization Algorithms Under-Perform
Dmitriy Rivkin
David Meger
Di Wu
X. T. Chen
Xue Liu
Gregory Dudek
We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization… (see more) algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method underperforms baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator's quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.
2021-12-06
2021 IEEE Global Communications Conference (GLOBECOM) (published)
doi.org
Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting
Di Wu
Boyu Wang
Doina Precup
Benoit Boulet
Electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important for power system operation. We… (see more) propose to use multiple kernel learning (MKL) for residential electric load forecasting which provides more flexibility than traditional kernel methods. Computation time is an important issue for short-term forecasting, especially for energy scheduling. However, conventional MKL methods usually lead to complicated optimization problems. Another practical issue for this application is that there may be a very limited amount of data available to train a reliable forecasting model for a new house, while at the same time we may have historical data collected from other houses which can be leveraged to improve the prediction performance for the new house. In this paper, we propose a boosting-based framework for MKL regression to deal with the aforementioned issues for STLF. In particular, we first adopt boosting to learn an ensemble of multiple kernel regressors and then extend this framework to the context of transfer learning. Furthermore, we consider two different settings: homogeneous transfer learning and heterogeneous transfer learning. Experimental results on residential data sets demonstrate that forecasting error can be reduced by a large margin with the knowledge learned from other houses.
2020-02-29
IEEE Transactions on Smart Grid (published)
doi.org
Data-driven Chance Constrained Programming based Electric Vehicle Penetration Analysis
Di Wu
Tracy Can Cui
Doina Precup
Benoit Boulet
Transportation electrification has been growing rapidly in recent years. The adoption of electric vehicles (EVs) could help to release the d… (see more)ependency on oil and reduce greenhouse gas emission. However, the increasing EV adoption will also impose a high demand on the power grid and may jeopardize the grid network infrastructures. For certain high EV penetration areas, the EV charging demand may lead to transformer overloading at peak hours which makes the maximal EV penetration analysis an urgent problem to solve. This paper proposes a data-driven chance constrained programming based framework for maximal EV penetration analysis. Simulation results are presented for a real-world neighborhood level network. The proposed framework could serve as a guidance for utility companies to schedule infrastructure upgrades.
2018-12-31
(published)
www.semanticscholar.org
Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning
Di Wu
Guillaume Rabusseau
Vincent François-Lavet
Doina Precup
Benoit Boulet
Smart grids are advancing the management efficiency and security of power grids with the integration of energy storage, distributed controll… (see more)ers, and advanced meters. In particular, with the increasing prevalence of residential automation devices and distributed renewable energy generation, residential energy management is now drawing more attention. Meanwhile, the increasing adoption of electric vehicle (EV) brings more challenges and opportunities for smart residential energy management. This paper formalizes energy management for the residential home with EV charging as a Markov Decision Process and proposes reinforcement learning (RL) based control algorithms to address it. The objective of the proposed algorithms is to minimize the long-term operating cost. We further use a recurrent neural network (RNN) to model the electricity demand as a preprocessing step. Both the RNN prediction and latent representations are used as additional state features for the RL based control algorithms. Experiments on real-world data show that the proposed algorithms can significantly reduce the operating cost and peak power consumption compared to baseline control algorithms.
2017-12-31
(published)
www.semanticscholar.org
Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting
Di Wu
Boyu Wang
Doina Precup
Benoit Boulet
2017-12-29
Machine Learning and Knowledge Discovery in Databases (published)
doi.org