Yan Lu
Princeton University
31 Papers
352 Citations
Yan Lu is an academic researcher from Princeton University. The author has contributed to research in topics: Vulnerability (computing) & Smart grid. The author has an hindex of 14, co-authored 31 publications. Previous affiliations of Yan Lu include Siemens & National Institute of Standards and Technology.
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Papers
Protecting Smart Grid Automation Systems Against Cyberattacks
TL;DR: A conceptual layered framework for protecting power grid automation systems against cyberattacks without compromising timely availability of control and signal data is proposed and the proposed “bump-in-the-wire” approach provides security protection for legacy systems which do not have enough computational power or memory space to perform security functionalities.
186
Prognostics enabled resilient control for model-based building automation systems
Kun Ji,Yan Lu,Linxia Liao,Zhen Song,Dong Wei +4 more
- 01 Jan 2011
TL;DR: In this article, an intelligent Resilient Control Strategy (RCS) for model-based building control is proposed to improve the Building Automation System (BAS) performance against unanticipated adverse conditions or incidents such as model mismatch, weather disturbances, and component failures/ faults.
143
Modeling and forecasting of cooling and electricity load demand
TL;DR: In this article, a generalized form of a Cochrane-Orcutt estimation technique that combines a multiple linear regression model and a seasonal autoregressive moving average model is proposed.
121
Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming
TL;DR: This work proposes a model predictive control strategy to optimally schedule the campus central plant based on plant system dynamics and predicted campus cooling load and proposes a heuristic algorithm to obtain suboptimal solutions for the MPC problem.
A Hybrid Physics-Based and Data Driven Approach to Optimal Control of Building Cooling/Heating Systems
TL;DR: This work integrates a physics-based model with a data driven time-series model to forecast and optimally manage building energy and can be implemented in commercial smart energy boxes to optimally control total daily energy-use costs.