BIGDATA: Collaborative Research: IA: Big Data Analytics for

Optimized Planning of Smart, Sustainable, and

Connected Communities

 

Sponsored by U.S. National Science Foundation (NSF) (2016 – 2022)

Project Description

The goal of this project is to develop a new planning framework for smart, connected and sustainable communities that allows meeting such zero energy, zero outage, and zero congestions goals by optimally deciding on how, when, and where to deploy or upgrade a community’s infrastructure. By bringing together interdisciplinary domain experts from data science, electrical engineering, and civil and architectural engineering, this research will yield several innovations:

  1. Novel big data techniques for faithfully creating spatiotemporal models for smart communities that integrate data from heterogeneous sources and shed light on the composition and operation of a given smart community;
  2. Novel, data driven performance metrics that advance powerful mathematical tools from stochastic geometry to explicitly quantify the health of smart communities via tractable notions of zero energy, zero outage, and zero congestion;
  3. Advanced analytical tools that bring forward novel ideas from optimization theory to devise the most effective strategies for deploying, upgrading, and operating various community infrastructure nodes, given the scale, dynamics, and structure of both the data and the community;
  4. A virtual smart community testbed that can accurately reconstruct, simulate, and evaluate the theoretical framework by leveraging open nonproprietary real world big data sets.

Project Team

Wangda Zuo, Ph.D.

Wangda Zuo, Ph.D.

Department of Architectural Engineering, Pennsylvania State University, United States

Jing Wang, Ph.D.

Jing Wang, Ph.D.

National Renewable Energy Laboratory, Golden, Colorado

Katy Hinkelman, M.S. EIT

Katy Hinkelman, M.S. EIT

Department of Architectural Engineering, Pennsylvania State University, United States

Saranya Anbarasu

Saranya Anbarasu

Department of Architectural Engineering, Penn State University, United States

Mingzhe Liu

Mingzhe Liu

Department of Architectural Engineering, Penn State University, United States

Chengnan Shi

Chengnan Shi

Department of Architectural Engineering, Penn State University, United States

Yingli Lou, Ph.D.

Yingli Lou, Ph.D.

Department of Architectural Engineering, Pennsylvania State University, United States

Yizhi Yang

Yizhi Yang

Department of Architectural Engineering, Pennsylvania State University, United States

Collaborators

Open Source Libraries

This project has developed two open source Modelica libraries:

Press Release

Journal Articles

Conference Papers

Presentation

Workshop