The Energy Systems and Sustainable Cities team were victorious at a trial of the new version of the Megawatts and Marbles game, beating UVic professors and other research groups to the coveted candy bars. Check out the link for more info on this interactive energy game.
Pierre Iachetti recently attended the Housing and Policy Management for Indigenous Communities conference at Simon Fraser University...
Using simulation to understand the behaviour of buildings and energy systems.
Future holistic, integrated energy systems solutions span from buildings (which are now active players in energy markets) to district and city-level designs and national scale infrastructure. Simulation is the sole means of exploring the performance of new designs and concepts in a rapidly changing techno-economic context; all buildings and systems are unique (there are no prototypes) and simulation can explore the influence of future contextual parameters (energy prices, warmer climates, upgraded technologies) on new designs.
Exploring design spaces by examining trade-offs between multiple objectives.
The design space of possible systems and their performance is vast and intricate, making it impossible to discover the best solutions by trial-and-error or by exhaustive evaluation of all options. Computational design optimization (e.g. multi-objective genetic algorithms and mixed-integer linear programming formulations) are powerful tools for finding high-performing designs and exploring their sensitivity, robustness and resilience.
Leveraging developments in machine learning to better explore systems and data.
The next steps in achieving real progress in computational design aids will encompass statistical emulation of complex models and hyper-heuristic optimisers that learn how to solve problems better. Statistical emulators (also called meta-modelling) use methods like neural networks to approximate detailed models that are too time-consuming to run directly. Hyper-heuristics involves “optimising the optimiser”, either in advance using training data or during an optimisation.
Assistant Professor; Group Leader
A city-scale case is divided into multiple districts based on the output of a density based clustering algorithm. The analysis of the clustering map along with building characteristics of each cluster reveals the required characteristics for the installation of a district heating network or distributed energy systems.
We present a machine learning model used to provide recommendations on chiller operation based on the prediction of cooling demand using a weather forecast. A long short term memory (LSTM) formulation was used, and achieved favourable results compared to a standard approach
Multiple linear regression was used to aggregate building retrofit data to derive a building retrofit strategy for the City of Victoria.
A geo-referenced bottom up statistical building stock model for the City of Victoria was created using individual building information as well as statistical energy use data from NRCan. This model was then used to estimate energy use, as well as produce maps of building energy use and building age for the city's building stock.
Multi-model ecologies are collections of small interacting software components that aid reuse and repurposing of academic software. In exploring the way in which these can help us to develop research code more effectively, we discuss two approaches, one prioritising interoperability, the other diversity.
This paper presents a new model for the calculation of 3-dimensional time-resolved solar gains to building surfaces which is much faster than Radiance but more accurate than the fast methods in EnergyPlus and DaySim. It is based on a novel interpolation scheme for the generation of annual hourly timeseries, and accounts for vegetation, snow-coverage, and diffuse and specular inter-reflections.
We present a future vision for a control approach in which simple predictive models can be used to improve the performance of complex building energy systems. The philosophy is that incremental improvements upon current control systems are possible by adding small degrees of predictive capabilities at critical points.
A design for an off grid Passivhaus built using shipping containers is presented.
This paper compares different temporal resolutions for an energy hub model: a full year in hourly time-steps, a set of optimally-selected typical days, and a rolling horizon formulation with sequential optimization.
The impact of microgrids on distributed multi energy systems is analysed on an urban district level for scenarios with different levels of renewable energy in the electricity grid supply.
Integrated coordination of multi-carrier energy networks including gas, heating, cooling and electricity can increase the flexibility, efficiency and sustainability of energy systems. Here, two approaches are developed for reducing the number of integers in such models, which dramatically harm performance. One is based on a relaxed mixed-integer linear formulation and the other on mathematical optimization with complementarity constraints.
This paper presents a custom Fast Fluid Dynamics (FFD) code in Rhinoceros and its visual programming platform Grasshopper for studying airflow around buildings and related surface pressures. Thanks to significantly faster computing time compared to CFD, it enables the consideration of airflow during the conceptual design phase and for use with computational optimization methodologies.
This paper introduces a combined clustering schema enabling quantification of the potential of district heating networks based on building characteristics.
This perspective discusses current limitations and future trends in holistic simulation and optimization in the urban energy systems domain. The HUES platform is outlined and an example application discussed. Future trends to be explored include better ways of linking computational modules (easier modularisation, a software bus, GUI-based reprogramming, semi-automated model configuration), use of statistical emulators (meta-models), and multi-method modelling.
Genetic algorithms (GAs) have long been favoured by the building optimisation community as a robust, easily applied optimiser. However, this paper examines many alternative algorithms and finds that GAs do not perform particularly well.
This paper determines the impact on district energy systems (DES) of different levels of renewable energy share in the electricity grid using a model that integrates DES optimisation, linearised AC power flow and district heating design.
Broad energy systems research regards the electrical grid as an infinite resource when optimising building and district-level systems, whereas power systems research conducts detailed analysis of the electrical grid, but treats the system components, layout and operation as fixed. This work brings together these two domains by incorporating (linearised) power flow equations into the energy hub modelling framework, optimising the energy system holistically whilst accounting for the limitations of the electrical grid. This gave significantly different results, highlighting the importance of accounting for these constraints.
There are many sources of variation between different buildings, including occupant presence and behaviour, lighting and appliance use and temperature preferences as well as physical properties like insulation and air tightness. These are of particular importance when analysing district energy systems, since the relative temporal alignment of the loads has a huge impact on the aggregated profiles. A statistical analysis was performed on these profiles to identify the greatest sources of variation, giving a better understanding of how district systems should account for this at the design stage.
In order to explore the performance of different energy system designs, it is necessary to know how they will perform operationally; the more complex the system, the more detailed this operational simulation must be to capture the true behaviour. This work nests both a building energy simulation (EnergyPlus) and an operational scheduling problem (the energy hub model) within a wider design exploration (a multi-objective genetic algorithm) so that the three levels can be explored holistically, leveraging the benefits of different solvers at each level.
The energy hub concept that sits at the core of much of my recent work balances intermittent energy demands and supplies through an operational optimisation every timestep. It can be applied from building to city scale, and can inform the design of many types of multi-energy systems and networks. However, in the simplified forms used prior to this work, the model fails to account for many significant aspects of equipment performance like minimum loads, run times or startup frequencies. This work adds rigour by including many additional constraints regarding the way energy systems really function, making this popular method much more applicable to the analysis of real energy systems.
This review summarises the current state of the field of building optimisation, analyses the trends in the rapid growth of the field, and critically addressed the gaps in the existing body of work. I commented on possible future directions that might be profitably explored, and gives opinion on the best ways that this could be achieved. This work has become very highly-cited as an overview of the progress and challenges in the field.
This paper applied the broad findings of my doctoral research to address the optimisation of domestic buildings in the UK to meet the building regulations as cheaply as possible – a situation faced frequently by industry practitioners. It uses design-of-experiments and systems concepts to reduce the scale of the problem to make it computationally manageable, also critical for application in real practice. The underlying simulation process was encapsulated in a software tool that could be easily applied by design team members.