Dr Bianca Howard EPSRC Innovation Fellow
Lecturer in Urban Energy Modelling
Dr. Howard joined Loughborough University in 2017 through the highly competitive Excellence 100 Initiative. Her research aims to develop techniques and technologies to aid communities and cities reduce their energy consumption and greenhouse gas emissions. Specifically, Dr. Howard focuses on the interactions between buildings and energy systems.
- PhD in Mechanical Engineering, Columbia University USA
- MSc in Mechanical Engineering, Columbia University USA
- BSc in Mechanical Engineering, University of Nebraska – Lincoln USA
- UK Engineering and Physical Science Research Council Innovation Fellow, 2018-2021
- US National Science Foundation IGERT Fellow, 2011-2015
- Urban Energy Demand Modelling
- Hybrid Energy System Design and Optimisation
- Collaborative Control of Building Energy Flexibility
- Dynamic Thermal Modelling
- Linear and Non-linear Programming
- Model Predictive Control
- Global Sensitivity Analysis
- System Identification
- Stochastic Modelling
- Time Series and Machine Learning Forecasting Techniques
Grants and Contracts:
UK Engineering and Physical Science Research Council Innovation Fellow, 2018-2021
Title: FlexTECC: Flexible Timing of Energy Consumption in Communities
- Howard, B., Waite, M., Modi, V.
Current and near-term GHG emissions factors from electricity production for New York State and New York City
(2017) Applied Energy, 187, pp. 255-271.
- Howard, B., Modi, V.
Examination of the optimal operation of building scale combined heat and power systems under disparate climate and GHG emissions rates
(2017) Applied Energy, 185, pp. 280-293.
- Howard, B., Saba, A., Gerrard, M., Modi, V.
Combined heat and power's potential to meet New York city's sustainability goals
(2014) Energy Policy, 65, pp. 444-454. Cited 6 times.
- Howard, B., Parshall, L., Thompson, J., Hammer, S., Dickinson, J., Modi, V.
Spatial distribution of urban building energy consumption by end use
(2012) Energy and Buildings, 45, pp. 141-151.
Current Teaching Responsibilities:
- CVC045 Collaborative BIM, Architectural Engineering and Design Management
- CVA058 Building Science, Architecture
Over three years of the ESPRC Innovation fellowship, I will develop new hierarchical and distributed control systems that will allow the heating, ventilation, and air conditioning systems of homes and workplaces to provide flexibility without compromising thermal comfort. The new technologies will be tested and validated in physical test buildings as well as through community scale simulations. The fellowship with be executed in partnership with TREND Controls Inc.
The work plan consists of three phases: theoretical development, physical demonstration and an economic assessment enabled through large scale community simulations.
Physical Demonstration Buildings:
The physical demonstrations will use two of the Universities Test Homes and the West Park Teaching Hub. The homes will be installed with heat pumps and development IOT Controllers. The West park teaching hub has 7 electrically driven air handling units and a centralised chiller centrally controlled with a TREND building energy management system.
Principal Systems Engineer
Research and Development
TREND Control Systems
Professor of Electrical Energy Systems
Department of Electrical and Electronic Engineering
Imperial College London
Professor of Building Simulation
School of Architecture, Building, and Civil Engineering
*** Currently Advertising for 2 PhD Students and 1 Research Associate in Community Scale Energy System Modelling and Design (2018/2019 Start). Please email for more information***
MRes/PhD in the LoLo CDT in Energy Demand
Topic: Modelling Contracted Flexibility in Electrically Heated Residences
MSc in Low Carbon Building Design and Modelling
Topic: Global Sensitivity Analysis and Optimisation of Commercial District Building and Energy System Design
BEng in Architectural Engineering and Design Management
Topic: Environmental Evaluation of Water Villages in Brunei
Diogo Ramos de Magalhaes Vaz Guedes
BEng in Civil Engineering
Topic: Forecasting Building Electricity Demand using Machine Learning Techniques