Swarthmore Battery OptimizationHorton AWS SimulationsHorton AWS ResultsNYC TypologiesYale Berkeley UpdateNYC Electrification Results - Capacity ConstraintsNYC Electrification Results - Updated MetricsCoorsTek 9th St. RedevelopmentCharacterization Experiments100 (ish) cities to simulateDisaggregation ExplorationsUtilitiesDisaggregation Graph V2Combinations TestCharacterization ExperimentsCombinations Test First Week ConstrainedCharacterization Graph V2Model Input CalibrationUT Model ResultsUT EEM All BuildingsMIT Model Input CalibrationMIT EEMsMIT Final Model ResultsMIT EEM All BuildingsMIT Monthly Baseline ResultsHorton Analysis ResultsHorton Solar + StorageGoogle Input CalibrationGoogle Monthly Baseline ResultsGoogle EEMsDC Energy Data AggregationEmissions VisualizationEPW ReaderSunpath DiagramPyschrometric Chart
Bay Area CFE Appendix
width = 960
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.`
buildingData[building]
updateGHGI = GHGIChart.updateGHGI(buildingData[building].scenarios)
updateEUI = EUIChart.updateEUI(buildingData[building].scenarios)
updateCFE = CFEChart.updateCFE(buildingData[building].scenarios)
d3 = require("d3@^6.1")
_ = require('lodash@4.17.15/lodash.js').catch(() => window["_"])
chroma = require('chroma-js')
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");
}
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;
}
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
