Swarthmore Battery OptimizationHorton AWS SimulationsHorton AWS ResultsNYC TypologiesYale Berkeley UpdateNYC Electrification Results - Capacity ConstraintsBay Area CFE AppendixNYC Electrification Results - Updated MetricsCoorsTek 9th St. RedevelopmentCharacterization Experiments100 (ish) cities to simulateDisaggregation ExplorationsUtilitiesDisaggregation Graph V2Combinations TestCharacterization ExperimentsCombinations Test First Week ConstrainedCharacterization Graph V2UT 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
Model Input Calibration
md`# Model Input Calibration`
xlsx = require('https://bundle.run/xlsx@0.14.1')
chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append("g")
.call(yAxis_ticks);
const g = svg.selectAll(".vars")
.data(counts)
.join("g")
.attr("class", "vars")
.attr("transform", d => `translate(0,${y(d.variable) + y.bandwidth()/2})`);
g.each(function(d, i) {
const x = d3.scaleLinear()
// .domain([0, 3]).nice()
.domain([0, 2]).nice()
.range([margin.left, width - margin.right]);
const xAxis = g => g
// .call(d3.axisBottom(x).tickValues([0, 1, 2, 3]).tickFormat(dd => x_lookup[d.variable] [dd]).tickPadding(12))
.call(d3.axisBottom(x).tickValues([0, 1, 2]).tickFormat(dd => x_lookup[d.variable][dd]).tickPadding(12))
.call(g => g.select(".domain").remove())
.call(g => g.selectAll(".tick line").remove())
.call(g => g.selectAll("text").attr("font-weight", "bold"))
.call(g => g.selectAll("text").attr("color", (dd, i) => highlight(d, i)))
.call(g => g.selectAll("text").style("mix-blend-mode", "multiply"));
d3.select(this).selectAll(".dots")
// .data([0, 1, 2, 3])
.data([0, 1, 2])
.join("circle")
.attr("class", "dots")
.style("mix-blend-mode", "multiply")
.attr("stroke", "#00a1b0")
.attr("stroke-width", 1)
.attr("fill", chroma("#00a1b0").brighten(1.5).desaturate())
.attr("fill-opacity", .5)
.attr("cy", 0)
.attr("cx", (d, i) => x(i))
.attr("r", dd => scale(d[dd.toString(16)]))
d3.select(this).selectAll(".points")
// .data([0, 1, 2, 3])
.data([0, 1, 2])
.join("circle")
.attr("class", "points")
.style("mix-blend-mode", "multiply")
// .attr("stroke", "#00a1b0")
// .attr("stroke-width", 2)
.attr("fill", (dd, i) => highlight(d, i))
.attr("cy", 0)
.attr("cx", (d, i) => x(i))
.attr("r", 4)
d3.select(this).append("g").call(xAxis);
})
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
svg.selectAll("text").attr("font-size", 16).attr("font-family", "Open Sans")
return svg.node();
}
filename = binary.name.split(".")[0]
input = new Uint8Array(await Files.buffer(binary))
wb = xlsx.read(input, {type: 'array'})
sheets = wb.SheetNames.filter(d => d != "Building_Design_Space_Original" && d != "Building Information" && d != "Baseline")
// data must be structured with typical input fields as column headers, with one column calling out building name
// data = FileAttachment("UT Baseline Inputs@1.csv").csv({typed: true})
excel = wb.Sheets["Baseline"]
data = xlsx.utils.sheet_to_json(excel)
margin = ({top: 80, right: 100, bottom: 50, left: 350})
height = 1500
d3 = require("d3@^6.1")
_ = require('lodash@4.17.15/lodash.js').catch(() => window["_"])
dates = d3.timeMonth.range(new Date(2019, 0, 1), new Date(2019, 11, 30)).map(d3.timeFormat("%b"));
colors = ({"S": "#f79320", "E": "#fed202", "C":"#00a1b0"})
chroma = require('chroma-js')
v_lookup = ({
"WWR": "Window-Wall Ratio (%)",
"WINU": "Window Assembly U (BTU/h-ft2-°F)",
"WINSHGC": "Window SHGC",
"WALLR": "Effective Wall R (h-ft2-°F/BTU)",
"ROOFR": "Effective Roof R (h-ft2-°F/BTU)",
"OCCSCHED": "Occupancy Schedule",
"EQUIPSCHED": "Equipment Usage Schedule",
"LIGHTUSAGE": "Lighting Usage Schedule",
"HEATSCHED": "Heating Schedule",
"COOLSCHED": "Cooling Schedule",
"EPD": "Equipment Power Density (W/ft2)",
"LPD": "Lighting Power Density (W/ft2)",
"HRV": "Sensible ERV Effectiveness",
"ACH": "Air Changes per Hour",
"VENTSCHED": "Ventilation Schedule",
"HUMIDMIN": "Humidity Control Min",
"HUMIDMAX": "Humidity Control Max",
"SUPPTEMPRESET": "Max Supply Air Temp (°F)",
"FANPOWER": "Fan Pressure Drop (in. H2O)",
// "OAAIR": "Outside Air System",
"ECON": "Economizer"
// "CCOP": "Cooling COP",
// "HFFACTOR": "Heating Fuel Factor",
// "HEFACTOR": "Heating Electrical Factor"
})
counts = {
const count = (d, i) => {
const arr = data.map(dd => dd[d]);
return arr.filter(dd => dd === i).length;
}
const keymap = Object.keys(v_lookup).map(d => ({variable: d, "0": count(d, 0), "1": count(d, 1), "2": count(d, 2), "3": count(d, 3)}));
return keymap;
}
energy = d => {
let r = "Low";
switch(d) {
case 0:
r= "High";
break;
case 1:
// r= "Mid-High";
r= "Mid";
break;
case 2:
// r= "Low-High";
r= "Low";
break;
default:
r="Low";
}
return r;
}
y = d3.scaleBand()
.domain(Object.keys(v_lookup).reverse())
.range([height - margin.bottom, margin.top])
.padding(.1)
.paddingOuter(.5)
x = d3.scaleLinear()
// .domain([0, 3]).nice()
.domain([0, 2]).nice()
.range([margin.left, width - margin.right])
xAxis = g => g
.attr("transform", `translate(0,${margin.top})`)
// .call(d3.axisTop(x).tickValues([0, 1, 2, 3]).tickFormat((d, i) => energy(i)).tickPadding(10))
.call(d3.axisTop(x).tickValues([0, 1, 2]).tickFormat((d, i) => energy(i)).tickPadding(10))
.call(g => g.select(".domain").remove())
.call(g => g.select(".tick:first-of-type text").clone()
.attr("y", -50)
.attr("x", -18)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.html("Energy Use Impact"))
yAxis = g => g
.attr("transform", `translate(${5},0)`)
.call(d3.axisRight(y).tickFormat(d => v_lookup[d]))
.call(g => g.select(".domain").remove())
.call(g => g.selectAll(".tick line").remove())
yAxis_ticks = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisRight(y).tickFormat(d => v_lookup[d]))
.call(g => g.select(".domain").remove())
.call(g => g.selectAll("text").remove())
.call(g => g.selectAll(".tick line")
.attr("stroke-opacity", 0.1)
.attr("x2", width - margin.left - margin.right))
scale = d3.scaleLinear()
.domain([0, 25])
.range([0, y.bandwidth()/1.5])
x_lookup = ({
"WWR": ["70%", "50%", "30%", "NA"],
"WINU": ["0.9", "0.6", "0.3", "NA"],
"WINSHGC": ["0.2", "0.4", "0.6", "NA"],
"WALLR": ["5", "15", "25", "NA"],
"ROOFR": ["10", "25", "40", "NA"],
"OCCSCHED": ["Industrial", "Lab", "Office", "NA"],
"EQUIPSCHED": ["Industrial", "Lab", "Office", "NA"],
"LIGHTUSAGE": ["Industrial", "Lab", "Office", "NA"],
"HEATSCHED": ["Industrial", "Lab", "Office", "NA"],
"COOLSCHED": ["Industrial", "Lab", "Office", "NA"],
"EPD": ["6", "3", "1", "NA"],
"LPD": ["2", "1", "0.5", "NA"],
"HRV": ["0.0", "0.3", "0.6", "NA"],
"ACH": ["12", "6", "1", "NA"],
"VENTSCHED": ["High", "Mid", "Low", "NA"],
"HUMIDMIN": ["Yes", "No", "NA", "NA"],
"HUMIDMAX": ["Yes", "No", "NA", "NA"],
"SUPPTEMPRESET": ["55", "60", "65", "NA"],
"FANPOWER": ["8", "6", "4", "NA"],
"OAAIR": ["Once Through", "Recirculating", "NA", "NA"],
"ECON": ["No Economizer", "Fixed Dry Bulb", "NA", "NA"],
"CCOP": ["3", "5", "7", "NA"],
"HFFACTOR": ["0.7", "2.0", "4.0", ">100"],
"HEFACTOR": ["1.0", "6.0", "12.0", ">100"]
})
highlight = (d, i) => {
const objs = Object.entries(d).filter(dd => dd[0] != "variable").sort((a, b) => b[1] - a[1]);
const index = +objs[0][0];
return index === i ? "#00a1b0": "#dfdfdf";
}
serialize = {
const xmlns = "http://www.w3.org/2000/xmlns/";
const xlinkns = "http://www.w3.org/1999/xlink";
const svgns = "http://www.w3.org/2000/svg";
return function serialize(svg) {
svg = svg.cloneNode(true);
const fragment = window.location.href + "#";
const walker = document.createTreeWalker(svg, NodeFilter.SHOW_ELEMENT, null, false);
while (walker.nextNode()) {
for (const attr of walker.currentNode.attributes) {
if (attr.value.includes(fragment)) {
attr.value = attr.value.replace(fragment, "#");
}
}
}
svg.setAttributeNS(xmlns, "xmlns", svgns);
svg.setAttributeNS(xmlns, "xmlns:xlink", xlinkns);
const serializer = new window.XMLSerializer;
const string = serializer.serializeToString(svg);
return new Blob([string], {type: "image/svg+xml"});
};
}
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.
