Title: Real-time and semantic energy management across buildings in a district configuration
Funder
Engineering and Physical Sciences Research Council, Building Research Establishment Trust
Principal Investigator
Co-Investigators
Rezgui, Yacine
Kwan, Alan
Project Details
Start date: 01/10/2015
End date: 31/03/2019
Abstract
Buildings are responsible for around 40% of
global energy consumption, therefore are a vital sector to target for
energy efficiency measures to met international energy obligations. This PhD
project will aim to utilise increasingly available sensor data to make more
intelligent decisions adapting to the specific circumstances of each day rather
than follow a traditional static and reactive control scheme.
Crucially, this project will aim to address energy management from both a
supply and demand perspective by developing a supply-aware building-level
control scheme in conjunction with a demand-aware district-level control
architecture.
Related Datasets
- Raw data supporting the results presented in the article "A Zone-Level, Building Energy Optimisation Combining an Artificial Neural Network, a Genetic Algorithm, and Model Predictive Control" (2018)
- Raw data supporting the results presented in the article "Operational Supply and Demand Optimisation of a Multi-Vector District Energy System using Artificial Neural Networks and a Genetic Algorithm" (2018)
