CANARIE have announced that the BESOS platform being developed by this group is one of 20 recipients of its Research Software funding call! We are pleased to be funded by CANARIE to take the software tools we use internally to a wider audience. Click here for open positions related to this project.
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.
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
Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials., C. Waibel, R. Evins, J. Carmeliet, Applied Energy, 2019.
This paper presents a co-simulation framework for the simultaneous optimization of building geometries and multi-energy systems using the energy hub approach, showing that this has significant benefits over optimizing each independently.
The study shows that coupling multiple simulators into a common optimization and design workflow brings together architectural aspects, such as geometry, with engineering aspects, such as the energy system design, and microclimate conditions, such as local solar potentials. Thus, essential interdependencies between the energy supply and demand side can be captured in the design of energy efficient cities.
Building Energy Optimization: An Extensive Benchmark of Global Search Algorithms., C. Waibel, T. Wortmann, R. Evins, J. Carmeliet, Energy and Buildings, 2019.
We present an extensive benchmark of black-box optimization algorithms for a range of building energy simulation problems, the aim being to go beyond 'yet another algorithm' that is demonstrated for only one problem.
Using established and newly proposed metrics for optimizer performance evaluation, we show that no single optimizer dominates benchmark for all metrics and/or problems. There is some impact of tuning hyper-parameters of optimizers to specific problem dimensions. Overall, RBFOpt is the best algorithm for a very small evaluation budget, but best results for high evaluation budgets are obtained with CMA-ES.
The impact of local variations in a temperate maritime climate on building energy use., R. Evins, R. Alexandra, E. Weibe, M. Wood, M. Eames, Journal of Building Performance Simulation, 2018
We investigate the impact of local climatic variations on the energy performance of buildings by conducting simulations using weather files generated from high-resolution measurements from the Victoria School-Based Weather Network, covering 33 stations within a 77 km2 area in southern Vancouver Island. Weather files were created by resampling the data and applying appropriate models to obtain unmeasured values. The difference in microclimate has been analysed statistically and graphically; average annual temperature varies by around 1°C, and at certain times there is a 6°C variation across the (very small) region.
Building energy simulations of a small naturally ventilated office building and a larger air-conditioned building were performed using EnergyPlus for all weather files. Significant variation is found spatially and temporally which would have substantial implications for building design and energy use. The variation in annual heating energy use is ±5% of the mean, equivalent to 18 kWh/m2/a, with even greater relative variation in cooling energy use.
A new Combined Clustering Method to Analyse the Potential of District Heating Networks at Large-scale., J. F. Marquant, L. A. Bollinger, R. Evins, J. Carmeliet, Energy, 2018
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.
Machine Learning Recommendations for Control of Complex Building Systems Using Weather Forecasts., P. Westermann, N. David, R. Evins, IBPSA-Canada’s biennial conference themed Building simulation to support building sustainability (eSim), 10-11 May 2018, Montreal, Canada.
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
Using Multiple Linear Regression to Estimate Building Retrofit Energy Reductions., W. Bowley, P. Westermann, R. Evins, IBPSA-Canada’s biennial conference themed Building simulation to support building sustainability (eSim), 10-11 May 2018, Montreal, Canada.
Multiple linear regression was used to aggregate building retrofit data to derive a building retrofit strategy for the City of Victoria.
A Bottom Up Statistical Building Stock Model for the City of Victoria., W. Bowley, R. Evins, 1st International Conference on New Horizons in Green Civil Engineering, 25-27 April 2018, Victoria, Canada.
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 for shaping future energy systems: Design patterns and development paths., L.A.Bollinger, C.B.Davis, R. Evins, E.J.L.Chappin, I.Nikoli, Renewable and Sustainable Energy Reviews, 82(3): 3441-3451, 2018.
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.
Decarbonizing the electricity grid: the impact on urban energy systems, distribution grids and district heating potential., B. Morvaj*, R. Evins & J. Carmeliet, Applied Energy, vol. 191, pp. 125–140,
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.
Optimization framework for distributed energy systems with integrated electrical grid constraints., B. Morvaj, R. Evins & J. Carmeliet, Applied Energy, vol. 171, pp. 296-313, 2016.
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.
Variability between domestic buildings: the impact on energy use, R. Evins, K. Orehounig & V. Dorer, Journal of Building Performance Simulation, vol. 9(2), pp. 162-175, 2016.
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.
Multi-level optimization of building design, energy system sizing and operation, R. Evins, Energy, vol. 90 II, pp. 1775-1789, 2015.
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.
New formulations of the energy hub model to address operational constraints, R. Evins, K. Orehounig, V. Dorer & J. Carmeliet, Energy, vol. 73, pp. 387-398, 2014.
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.
A review of computational optimisation methods applied to sustainable building design, R. Evins, Renewable and Sustainable Energy Reviews, vol. 22, pp.230-245, 2013.
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.
A case study exploring regulated energy use in domestic buildings using design-of-experiments and multi-objective optimisation., R. Evins, P. Pointer, R. Vaidyanathan & S. Burgess, Building and Environment, vol. 54, pp. 126–136, 2012.
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.