Published
Edited
Dec 28, 2021
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
width = 960
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
measures = md`</br>
##### Efficiency Measures
Each efficiency measure was evaluated at the typology level, and the results were applied back to each building individually to generate an estimate of greenhouse gas emissions (GHG) savings. The range of savings that are shown for each efficiency measure are the result of the distribution of input parameters in the ensemble model. The goal of this exercise was to identify which types of efficiency measures are likely to have the biggest impact for each building. The results will help generate a list of high-priority, maximum-impact projects across the entire portfolio. Note that this information comes directly from a statistical model, and some of the identified measures might not make sense for this particular building. However, it is nevertheless useful to try and understand why the model is predicting savings for certain measures, and not others, as it may help identify opportunities that were not apparent at the outset of this analysis. The GHG emissions reductions are calculated based on hourly grid emissions data provided by Google.`
Insert cell
Insert cell
Insert cell
Insert cell
buildingData[building]
Insert cell
Insert cell
updateGHGI = GHGIChart.updateGHGI(buildingData[building].scenarios)
Insert cell
Insert cell
updateEUI = EUIChart.updateEUI(buildingData[building].scenarios)
Insert cell
Insert cell
updateCFE = CFEChart.updateCFE(buildingData[building].scenarios)
Insert cell
Insert cell
Insert cell
d3 = require("d3@^6.1")
Insert cell
_ = require('lodash@4.17.15/lodash.js').catch(() => window["_"])
Insert cell
chroma = require('chroma-js')
Insert cell
createTooltip = el => {
el
.attr("class", "tooltip")
.style("border-radius", "3px")
.style("pointer-events", "none")
.style("display", "none")
.style("position", "absolute")
.style("z-index", "1000")
.style("padding", "12px")
.style("font-weight", "regular")
.style("font-family", "Open Sans")
.style("font-size", "12px")
.style("background", "white")
.style("box-shadow", "0 0 10px rgba(0,0,0,.25)")
.style("color", "#333333")
.style("line-height", "1.6")
.style("pointer-events", "none");
}
Insert cell
mapboxgl = {
const gl = await require("mapbox-gl@2.0.0");
if (!gl.accessToken) {
gl.accessToken = "pk.eyJ1IjoiaW50ZWdyYWxncm91cCIsImEiOiJjazM0dzc1aTMxOXUxM21uOXQ4c2ZseGxvIn0.SO3zITcSqsb5qRhEvSqsTA";
const href = await require.resolve("mapbox-gl@2.0.0/dist/mapbox-gl.css");
document.head.appendChild(html`<link href=${href} rel=stylesheet>`);
}
return gl;
}
Insert cell

One platform to build and deploy the best data apps

Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.
Learn more