College funded PhD Studentship in Engineering Management: Intelligent Energy Management Systems for the Smart Buildings Using Smart Controller

Location: Streatham Campus, University of Exeter, EX4 4QJ

Project Description:

The energy control and management is one of the key research areas in the current literature due to the pressing societal challenge of climate change and increases the poverty/income rate. It has presented that energy demand in buildings sector is accounted for 40% of total final energy consumption, which is expected to rise by 61% according to Jazz Scenario Model.

To reduce the energy cost per household, one of the key solution is to address the demand response problem via advanced solution techniques such as the implementation of the optimised appliance management models, the usage of available renewable resources, and precise forecasting system for both demands and resources. In addition, an efficient fault detection and monitoring system will also help to prevent accidental maintenance cost. Hence, an integrated and holistic intelligent solution in built environment will both reduce the household cost and optimise the usage of grid energy to avoid the pick hours’ demands.

In the proposed projects, a detailed literature review will be conducted, a comprehensive mathematical model will be developed to illustrate the demand response problem. In addition, both deterministic and stochastic optimisation and forecasting systems will be utilised to solve the developed model. Further a fault detection algorithm will be utilised to prevent the accidental sensor errors and readings.

Application criteria:
To conduct this research, the candidate student needs to have engineering background, possibly had experience on energy management systems and optimisation algorithm design. Prior research experience in Smart Building, Energy Management and Machine Learning is desirable. Moreover, Competitive candidates should be highly motivated and holding either an MSc degree or about to finish his/her MSc degree in September 2017.
Prior experience within the following areas are desirable:
• Stochastic optimisation and forecasting system, including Genetic algorithm, the Bees algorithm, Simulated annealing, Artificial Neural Network.
• Numerical solution techniques such as numerical Laplace transformation, Fourier transform.
• Good modelling skills and experience on thermal modelling and web service development.
• Good knowledge about MATLAB, Python, JAVA (enough level to program web service development) languages.
• Raspberry Pi and Linux based systems.
• Wireless sensor connection.