Reading through their plan on sustainability: https://storage.googleapis.com/sidewalk-toronto-ca/wp-content/uploads/2019/06/23143305/MIDP_Vol.2_Chap.4_Sustainab

Reading through their plan on sustainability: https://storage.googleapis.com/sidewalk-toronto-ca/wp-content/uploads/2019/06/23143305/MIDP_Vol.2_Chap.4_Sustainability.pdf https://storage.googleapis.com/sidewalk-toronto-ca/wp-conten... I think tracking building energy and indoor ambient conditions in order to optimize the former is a good example of how collected data should be used. There's a lot of subtlety in being able to manage building systems to run passively (i.e using natural ventilation to condition buildings) that are too complex for facility managers or residents. This is a problem domain that desperately requires predictive tools, integrated with effective visual/interactive methods for dynamic feedback from occupants and owners. One area that could be improved in their write-up, as it stands now, is a better discussion of demand-side energy management (meaning controlling the energy _demand_ that is a function of the architecture, material elements, to be resolved with passive and active systems of energy _supply_). This proposal reflects the kind of thinking I always see from engineers/energy modelers, which tends to tackle energy reduction primarily from the supply-side (i.e with more optimized HVAC systems) and omit strategies to reduce the the demand-side of the energy equation. There is a section on using Passive House methods which addresses this in terms of building insulation, window thermal properties etc, but that's primarily for small residential use, and starts to show it's limitations once you get into multi-family housing, and commercial buildings. At that scale there's a lot more interesting opportunities to coordinate energy demand trade-offs that they could explore. For example, there's the classic case of optimizing window size and placement to reduce heating loss while increasing daylight, or trying to reduce peak loads, typically associated with cooling energy, versus designing for annual energy reduction associated with heating energy (in Toronto). The pace of the design cycle means most of this kind of optimization is done using rules of thumbs, even though the simulation engines to model this exists. I also think this is important because proper consideration of demand-side energy forces you to grapple with the building's interaction with the local environment. Therefore it's the most obvious way in which the building energy logic translates to issues of urban identity and scale. Without that nuance, you just end up with abstract, large boxes crammed with hyper-efficient HVAC systems (i.e the worst of modernism). You need the constraining factor of demand-energy to provide some needed traction for the architecture.