Edward O'Dwyer

Imperial College London | Imperial · Department of Chemical Engineering

Advantages of Model Predictive Control (MPC) strategies for control of building energy systems have been widely reported. A key requirement for successful realisation of such approaches is that strategies are formulated in such a way as to be easily adapted to fit a wide range of buildings with little commissioning effort. This paper introduces an...

Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle man...

Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces t...

As Internet of Things (IoT) technologies enable greater communication between energy assets in smart cities, the operational coordination of various energy networks in a city or district becomes more viable. Suitable tools are needed that can harness advanced control and machine learning techniques to achieve environmental, economic and resilience...

A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we extend these methods to incorporate segmented prediction trajectories. The proposed segmentation enab...

Cost-effective decarbonisation of the built environment is a stepping stone to achieving net-zero carbon emissions since buildings are globally responsible for more than a quarter of global energy-related CO$_2$ emissions. Improving energy utilization and decreasing costs naturally requires considering multiple domain-specific performance criteria....

Existing methods for nonlinear robust control often use scenario‐based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time‐varying uncertainty. This paper draws from local reduction methods used in...

Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time-varying uncertainty. This paper draws from local reduction methods used in...

Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framew...

Robust optimal control enables ensuring correct operation of a system subject to uncertainty. Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as nonlinear optimisation problems. Solving the resulting optimisation problems is a challenge due to the size of the problem. Mitigating the...

We highlight the key pillars of urban energy systems which would leverage on AI and digital technologies for a low carbon future. We summarise a couple of real world applications where optimisation, intelligent control systems and cloud-based infrastructure have played a transformative role in improving system performance, cost-effectiveness and de...

Despite a large body of research, the widespread application of Model Predictive Control (MPC) to residential buildings has yet to be realised. The modelling challenge is often cited as a significant obstacle. This chapter establishes a systematic workflow, from detailed simulation model development to control-oriented model generation to act as a...

Residential buildings account for about a quarter of the global energy use. As such, residential buildings can play a vital role in achieving net-zero carbon emissions through efficient use of energy and balance of intermittent renewable generation. This chapter presents a co-design framework for simultaneous optimisation of the design and operatio...

To evaluate risks and characterise the responses of a rainwater harvesting system under different rainfall types, this paper presents a model agnostic evaluation framework where a k-means clustering approach is supplemented with a statistical Partial Least Squares model. Four response modes were identified for a studied system. Using these response...

A class of data-driven control methods has recently emerged based on Willems’ fundamental lemma. Such methods can ease the modeling burden in control design but can be sensitive to disturbances acting on the system under control. In this article, we propose a restructuring of the problem to incorporate segmented prediction trajectories. The propose...

This paper describes a multi-energy system optimisation software, “Sustainable Energy Management System” (SEMS), developed as part of a Siemens, Greater London Authority and Royal Borough of Greenwich partnership in collaboration with the University of Nottingham, Nottingham Trent University and Imperial College London. The software was developed f...

In this work, we develop a novel data-driven model predictive controller using advanced techniques in the field of machine learning. The objective is to regulate control signals to adjust the desired internal room setpoint temperature, affected indirectly by the external weather states. The methodology involves developing a time-series machine lear...

Buildings are responsible for about a quarter of global energy-related CO2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through increased efficiency in using energy generated by intermittent resources. The paper presents a co-des...

As Internet of Things (IoT) technologies enable greater communication between energy assets in smart cities, the operational coordination of various energy networks in a city or district becomes more viable. Suitable tools are needed that can harness advanced control and machine learning techniques to achieve environmental, economic and resilience...

Due to complex composition of carbohydrates, lipid, protein, cellulose, hemicellulose and lignin, wastewater (WW) and organic fraction municipal solid waste (OFMSW) represent nutrient and carbon rich resources. Conventionally, value chains in the waste sector have considered OFMSW and WW as unwanted by-products as opposed to potential valuable reso...

The benefits of applying advanced control approaches such as Model Predictive Control to the building energy domain are well understood. Furthermore, to facilitate the decarbonisation of the sector, district heating, communal heating and heat pumps are set to become more common, leading to a greater need to employ advanced approaches to enable flex...

Decentralized rainwater detention tanks are usually implemented as a method for reducing rainfall runoff volumes entering centralized reservoirs and treatment plants, but these systems also provide the opportunity for harvesting and locally treating rainwater such that local demand for potable water can be reduced. Here we evaluate the effectivenes...

To enable a more sustainable wastewater treatment processes, a transition towards resource recovery methods that have minimal environmental impact while being financially viable is imperative. Phosphorus (P) is a finite resource that is being discharged into the aqueous environment in excessive quantities. As such, understanding the financial and e...

The expanding population and rapid urbanisation, in particular in the Global South, are leading to global challenges on resource supply stress and rising waste generation. A transformation to resource-circular systems and sustainable recovery of carbon-containing and nutrient-rich waste offers a way to tackle such challenges. Eco-industrial parks h...

A considerable amount of carbon-containing and nutrient-rich waste resources are generated globally each year, which could be converted via various routes to bioenergy and other value-added bio-products. In an industrial park setting in which multiple waste-streams of different compositions along with various treatment/recovery technologies may be...

Predictive control strategies for building heating and cooling systems have been proposed as an energy efficient alternative to traditional strategies. The performance of such strategies is highly dependent on the underlying system models used. In an effective strategy, these models used are required to be accurate enough for informative prediction...

As research in the area of model predictive control (MPC) for building energy systems intensifies, appropriate methods are required to model a building's thermodynamic properties. In this paper, building models are considered from two perspectives - simulation and optimisation. First, a methodology is devised for the development of complex simulati...

Inefficient design and operation of building heating systems can have a large impact on global energy consumption. Traditional heuristic approaches often supply excess heat and cannot adapt to faults and changes in the building and heating system. Model Predictive Control (MPC) based strategies can incorporate future building usage and weather cond...

A hierarchical Model Predictive Control (MPC) strategy for optimally controlling the heating system of a building is developed to satisfy comfort constraints with reduced energy consumption. MPC based strategies can prevent unnecessary en-ergy use by predicting the future heating requirement of the building and only supplying heat when necessary. I...