Work

Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

Year: 2018

Type: article

Source: Applied Energy

DOI: 10.1016/j.apenergy.2018.05.023

Authors: Alex Nutkiewicz, Zheng Yang, Rishee K. Jain

Institution: Stanford University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Energy, UPS Foundation, National Oceanic and Atmospheric Administration, Center for Integrated Facility Engineering, Stanford University

Awards: EAGER: Exploring the Coupled Dynamics of Urban Systems Using Data Science and Micro-Experimentation, SEES Fellows: Building Informatics: Utilizing Data-Driven Methodologies to Enable Energy Efficiency and Sustainability Planning of Urban Building Systems

OpenAlex ID: W2806066720

FWCI: 15.91

Cites: 76

Cited by: 235

Related to: 10