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MD4: Supervisory and Control Infrastructures

This work package will forecast and intelligently manage demand side utility grid behaviour, leading to a significant reduction in peak loads, primary energy consumption and improved grid operation. A new characterisation methodology, enabling communication between each demand side instance (e.g. building, wastewater treatment plant) or aggregated entity (e.g. campus of buildings) and inter-connected utility networks will be developed, conveying quantitative data, including gas, water, electricity, heating and cooling metrics, energy footprint and CO2 emissions (DOE, 2013). New metrics, including building capacitance, temperature ramping rates, thermal lag and energy savings from harvested rainwater (Warren, 2014), will be defined, integrating end use entities with utility grids (Mancarella, 2013), enabling cohesive integration between commercial, residential (Li and Hong 2013 ; Gans et. al, 2013) and industrial demand side domains (O’Donnell, 2013). Frequency of data transfer is balanced against system needs, with statistical methods used to represent system behaviour. An analytical and modelling infrastructure will be created, leveraging new metrics, to communicate the demand side state in real time. Forecast models will optimise performance at the overall system level, enabling flexibility through virtual storage and demand response (Mahmoudi et. al., 2014). New ancillary services should reduce management workload associated with multiple demand side participants. Multi-utility consolidation can surpass current demand response and load shifting solutions that primarily focus on mitigating peak electrical loads.

 

Members


Dr James O'Donnell
Funded Investigator, Assistant Professor, School of Mechanical & Materials Engineering, University College Dublin
james.odonnell@ucd.ie
+353 1 7161839
Dr Donal Finn
Funded Investigator, Associate Professor, School of Mechanical & Materials Engineering, University College Dublin
donal.finn@ucd.ie
01 716 1947
Dr Damian Flynn
Funded Investigator, Associate Professor, School of Electrical & Electronic Engineering, University College Dublin
damian.flynn@ucd.ie
01 716 1819
Dr Eleni Mangina
Funded Investigator, Associate Professor, School of Computer Science, UCD
eleni.mangina@ucd.ie
+353 1 7162858
Cathal Hoare
Senior Energy Systems Researcher
cathal.hoare@ucd.ie
Paul Beagon
PhD Researcher
paul.beagon@ucdconnect.ie
Usman Ali
PhD Researcher
usman.ali@ucdconnect.ie
Mohammad Haris Shamsi
PhD Researcher
mohammad.shamsi@ucdconnect.ie

Research Outputs


Journal

A generalization approach for reduced order modelling of commercial buildings

2019; Journal of Building Performance Simulation; Shamsi, Mohammad Haris, Usman Ali, and James O'Donnell

DOI

Conference

Dynamic District Information Server: On the Use of W3C Linked Data Standards to Unify Construction Data

2019; 2019 European Conference on Computing in Construction, Greece; Hoare Cathal, Ali Usman, O'Donnell James

DOI | OA

Conference

Comparative Analysis of Machine Learning Algorithms for Building Archetypes Development in Urban Energy Modeling

2018; 2018 Building Performance Analysis Conference and SimBuild, United States of America; Usman Ali, Mohammad Haris Shamsi, James O'Donnell, Fawaz Alshehri and Eleni Mangina

OA

Conference

An Intelligent Knowledge-based Energy Retrofits Recommendation System for Residential Building at an Urban Scale

2018; 2018 Building Performance Analysis Conference and SimBuild, United States of America; Usman Ali, Mohammad Haris Shamsi, James O'Donnell , Cathal Hoare and Eleni Mangina

OA

Conference

A framework to assess the interoperability of commercial buildings at a district scale

2018; Building Simulation and Optimization BSO 2018, United Kingdom (excluding Northern Ireland); Shamsi, M.H., Ali, U., Alshehri, F. and O'Donnell, J.

OA

Conference

GIS-Based Residential Building Energy Modeling at the District Scale

2018; Building Simulation and Optimization BSO 2018, United Kingdom (excluding Northern Ireland); Ali, U., Shamsi, M.H., Hoare, C. and O'Donnell, J.

OA

Journal

A data-driven approach for multi-scale building archetypes development

2018; Energy and Buildings; Usman Ali, Mohammad Haris Shamsi, Cathal Hoare, Eleni Mangina, James O'Donnell

DOI | OA

Journal

Review of district-scale energy performance analysis: Outlooks towards holistic urban frameworks

2018; Sustainable Cities and Society; Reihaneh Aghamolaei, Mohammad Haris Shamsi, Mohammad Tahsildoost, James O'Donnell

DOI | OA

Journal

Quantitative evaluation of deep retrofitted social housing using metered gas data

2018; Energy and Buildings; Beagon, P., Boland, F. and O'Donnell, J.

DOI | OA

Conference

Operational characterisation of neighbourhood heat energy after large-scale building retrofit

2018; The 9th International Cold Climate Conference, Sweden; Beagon, P., Boland, L. and O'Donnell, J.

DOI | OA

Journal

Identifying Stakeholders and Key Performance Indicators for District and Building Energy Performance Analysis

2017; Energy and Buildings; Yehong, L., O'Donnell, J., Garcia-Castro, R. and Vega-Sanchez, S.

DOI | OA

Conference

A generalization approach for reduced order modelling of commercial buildings

2017; CISBAT 2017 - International Conference Future Buildings & Districts Energy Efficiency from Nano to Urban Scale, Switzerland; Shamsi, M.H., O'Grady, W., Ali, U. and O'Donnell, J.

DOI | OA

Conference

Control strategies for building energy systems to unlock demand side flexibility - A review

2017; IBPSA Building Simulation 2017, United States of America; Claus, J., Finck, C., Vogler-Finck, P. and Beagon, P.

DOI | OA

Conference

Next Generation Building and District Metrics to Enable Energy Systems Integration

2016; CLIMA 2016: 12th REHVA World Congress, Denmark; Beagon, P., Warren, J., Finn, D. and O'Donnell, J.

OA