Building Energy Modeling: A Data-Driven Approach
abstract: Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of...
Other Authors: | Cui, Can (Author) |
---|---|
Format: | Doctoral Thesis |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | http://hdl.handle.net/2286/R.I.38640 |
Similar Items
-
Data Consistency for Data-Driven Smart Energy Assessment
by: Gianfranco Chicco
Published: (2021-05-01) -
The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature
by: Stefano Villa, et al.
Published: (2020-12-01) -
Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA
by: Rezvan Mohammadiziazi, et al.
Published: (2020-08-01) -
A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models
by: Niraj Agarwal, et al.
Published: (2021-09-01) -
SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays
by: Feng, Menghong
Published: (2020)