2b:null 2c:["$","title",null,{"children":"\"December’s Energy Peak: Winter Brings the Highest Power Demand of the Year at Boston's City Hall\" | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:[{"id":"27de1d52c6e5983915dbd80404c3b3493111f7df217d9dadfa46d222fcdd5338d20054f084ba0f947f1c923cf0a2a29a0db6471270dd1df2721cee0cedae0ab2","href":"https://static.observableusercontent.com/files/27de1d52c6e5983915dbd80404c3b3493111f7df217d9dadfa46d222fcdd5338d20054f084ba0f947f1c923cf0a2a29a0db6471270dd1df2721cee0cedae0ab2","name":"City_Hall_power_consumption.csv","type":"text/csv","lastModified":1730397462407.831,"size":2638455}] 2f:{"id":"ed449ab360785ad5","slug":null,"title":"\"December’s Energy Peak: Winter Brings the Highest Power Demand of the Year at Boston's City Hall\"","thumbnail":"2a0ed5cecf082e98f0a8268149921101f502f201335c4a9cfcf2ca82868c437d","access_level":"public","create_time":"$D2024-10-31T17:55:30.966Z","update_time":"$D2024-11-16T02:47:03.851Z","view_time":null,"first_public_time":"$D2024-10-31T21:46:06.283Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","bio":""},"version":605,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"c43a3832073d59e0","login":"jonathan-tarun-rajasekaran","name":"Jonathan Tarun Rajasekaran","avatar_url":"https://avatars-next.observableusercontent.com/7236be39798128ec4d4f3c6f7847b6409a07099a3c87825dcc50c3ec2860644e","tier":null,"bio":""}],"members":[{"type":"individual","id":"c43a3832073d59e0","login":"jonathan-tarun-rajasekaran","name":"Jonathan Tarun Rajasekaran","avatar_url":"https://avatars-next.observableusercontent.com/7236be39798128ec4d4f3c6f7847b6409a07099a3c87825dcc50c3ec2860644e","tier":null,"role":"editor"},{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"viewer"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"\"December’s Energy Peak: Winter Brings the Highest Power Demand of the Year at Boston's City Hall\"","cells":[{"id":0,"value":"# \"December’s Energy Peak: Winter Brings the Highest Power Demand of the Year at Boston's City Hall\"\n\nThis is a data visualization project done by [Jonathan Tarun Rajasekaran](https://observablehq.com/d/8586f881717414c5) from Northeastern University, Oakland for the course [CS7295 - Special Topics in Data Visualization](https://johnguerra.co/classes/data_visualization_fall_2024/) under Professor [John Alexis Guerra Gomez](https://johnguerra.co/).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":356,"value":"This visualization project aims to explore various approaches to visualizing temporal data, focusing on patterns in power demand over time. By examining Power Demand at Boston’s City Hall from 2016 to 2020, the project seeks to reveal insights into how energy consumption fluctuates, identify any recurring patterns, and provide a basis for potential improvements in energy management and sustainability efforts.\n\nWarning: Since this notebook has a lot of visualizations it is slow at processing when you change some filters, so kindly be a little patient!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"intro","database":"$undefined"},{"id":556,"value":"## Table of Contents\n\n1. [Introduction](#intro)\n2. Idioms (Visualizations)\n - [Idiom 1](#ID1)\n - [Idiom 2](#ID2)\n - [Idiom 3](#ID3)\n - [Idiom 4](#ID4)\n - [Idiom 5](#ID5)\n - [Idiom 6](#ID6)\n - [Idiom 7](#ID7)\n3. [Example Insights](#ei)\n - Example Insight 1\n - Example Insight 2\n4. [Data Abstraction](#what)\n - Dataset Type\n - Data Availability\n - Attributes\n - Data Processing\n5. [Task Abstraction](#why)\n - Task 1\n - Task 1.1\n - Task 2\n - Task 2.1\n6. [Marks and Channels](#how)\n7. [Target Audience](#users)\n8. [Expressiveness and Effectiveness](#ee)\n9. [Real Insights](#ri)\n10. [YouTube Video](#yt)\n11. [Tweet](#x)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":427,"value":"## Idiom 1 (Iteration 1)\n\nThis is a basic line chart that represents time on the x-axis and the power demand (kW) on the y-axis. This is super congested as we have over a 100k records in our dataset, but using date truncate we can change the filters for the x-axis and reveal some patterns.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID1","database":"$undefined"},{"id":438,"value":" Date Truncate: ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":166,"value":"viewof timeTruncate = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":274,"value":"timeTruncateSelected","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"id1 = vl.markLine({tooltip: true}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(timeTruncateSelected).axis({ title: `Time (${timeTruncateSelected})` }),\n vl.y().fieldQ(\"Total_Demand_KW\").axis({title: \"Power Demand at Boston City Hall (kW)\"}),\n vl.color().value(\"#aabbff\"),\n vl.strokeWidth().value(2)\n )\n .data(data)\n .width(1000)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":444,"value":"## Idiom 2 (Iteration 2)\n\nThis is also a line chart but the y-axis has been changed to a temporal attribute as well. Here, I have used date parts instead of date truncate. As you can see this is super messy and it is almost impossible to get any useful information from this visualization. However, I am able to see that between the months of May and November (In the years 2017-19), there are quite a few periods of low power demand in the city hall.\n\nDate Parts: ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID2","database":"$undefined"},{"id":162,"value":"viewof unitsX = select({\n options: timeUnitOptions,\n value: [\"month\"],\n title: \"x\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"viewof unitsY = select({\n options: timeUnitOptions,\n value: [\"day\"],\n title: \"y\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":235,"value":"id2 = vl.markLine({tooltip: true}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(unitsX).axis({ title: `Time (${unitsX})` }),\n vl.y().fieldT(\"DateTime_Measured\").timeUnit(unitsY).axis({title: `Time (${unitsY})`}),\n vl.color().fieldQ(\"Total_Demand_KW\"),\n vl.strokeWidth().value(3)\n )\n .data(data)\n .width(1000)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"## Idiom 3 (Iteration 3)\n\nHere, I have changed the idiom from a line to Rectangle (Area). As you can see there is a HUGE difference between these 2 iterations. This visualization is much clearer and I am able to gather some interesting information. There is a very clear outlier that I noticed in the visualization (on the top-left), as we can see for the months of Feb and March in 2020 the power demand was 0 kW - likely due to the COVID-19 pandemic lockdowns.\n\nDate Parts:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID3","database":"$undefined"},{"id":359,"value":"viewof unitsX2 = select({\n options: timeUnitOptions,\n value: [\"month\"],\n title: \"x\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":362,"value":"viewof unitsY2 = select({\n options: timeUnitOptions,\n value: [\"day\"],\n title: \"y\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"id3 = vl.markRect({tooltip: true}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(unitsX2).axis({ title: `Time (${unitsX})` }),\n vl.y().fieldT(\"DateTime_Measured\").timeUnit(unitsY2).axis({title: `Time (${unitsY})`}),\n vl.color().fieldQ(\"Total_Demand_KW\"),\n vl.strokeWidth().value(2)\n )\n .data(d3data)\n .width(800)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":456,"value":"## Idiom 4 (Iteration 4)\n\nNow, for the 4th iteration I have changed the y-axis back to the total power demand while using the date parts for time on the x-axis, and the idiom has been changed to points. Personally, I think this idiom serves the Task 1 (T1) very well (when date part is selected to \"hours\"), as I can clearly see the time periods with high and low power demand, while it also shows the range indicating the peaks and low points for each hour.\n\nDate Parts: ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID4","database":"$undefined"},{"id":367,"value":"viewof unitsX3 = select({\n options: timeUnitOptions,\n value: [\"hours\"],\n title: \"x\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":225,"value":"id4 = vl.markPoint({tooltip: true}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(unitsX3).axis({ title: `Time (${unitsX3})` }),\n vl.y().fieldQ(\"Total_Demand_KW\").axis({title: \"Power Demand at Boston City Hall\"}),\n vl.color().fieldQ(\"Total_Demand_KW\"),\n vl.strokeWidth().value(2)\n )\n .data(d3data)\n .width(800)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"## Idiom 5 (Iteration 5)\n\nThis is similar to iteration 4, but I have changed the idiom to and Area chart. This makes one big change - the y-axis now shows some very large values, but why? It is because the area is stacked thereby totaling the power demand for the specific date part chosen. This idiom would be useful to summarize the months, days or hours which have required the most power in previous years.\n\nDate Parts: ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID5","database":"$undefined"},{"id":376,"value":"viewof unitsX4 = select({\n options: timeUnitOptions,\n value: [\"month\"],\n title: \"x\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"id5 = vl.markArea({tooltip: true, opacity: 0.5}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(unitsX4).axis({ title: `Time (${unitsX4})` }),\n vl.y().fieldQ(\"Total_Demand_KW\").aggregate(\"mean\").axis({title: \"Power Demand at Boston City Hall\"}),\n vl.color().fieldQ(\"Total_Demand_KW\"),\n //vl.strokeWidth().value(2)\n )\n .data(d3data)\n .width(800)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"## Idiom 6 (Iteration 6)\n\nI have used a bar chart along with time truncate for this iteration, since bar charts are the easiest for us to do comparisons and identify. But due to the sheer number of records this is useless for anything more specific than \"yearmonth\".\n\nDate Truncate:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID6","database":"$undefined"},{"id":386,"value":"viewof timeTruncate2 = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":391,"value":"timeTruncateSelected2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":345,"value":"id6 = vl.markBar({tooltip: true}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(timeTruncateSelected2).axis({ title: `Time (${timeTruncateSelected2})` }),\n vl.y().fieldQ(\"Total_Demand_KW\").axis({title: \"Power Demand at Boston City Hall\"}),\n vl.color().fieldQ(\"Total_Demand_KW\"),\n vl.strokeWidth().value(7)\n )\n .data(d3data)\n .width(800)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":484,"value":"## Idiom 7 (Iteration 7):\n\nThis is another bar chart but using date parts instead of truncate, this allows us to view patterns for time periods smaller than a month such as day and hours. While this is again great to compare overall across the years which time of the day requires the most power, we are not able to find peaks and lows for specific hours since the bars are stacked.\n\nDate Parts:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ID7","database":"$undefined"},{"id":419,"value":"viewof unitsX5 = select({\n options: timeUnitOptions,\n value: [\"month\"],\n title: \"x\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":416,"value":"id7 = vl.markBar({tooltip: true}).encode(\n vl.x().fieldT(\"DateTime_Measured\").timeUnit(unitsX5).axis({ title: `Time (${unitsX5})` }),\n vl.y().fieldQ(\"Total_Demand_KW\").axis({title: \"Power Demand at Boston City Hall\"}),\n vl.color().fieldQ(\"Total_Demand_KW\"),\n vl.strokeWidth().value(7)\n )\n .data(d3data)\n .width(800)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"## Example Insights\nExample Insight 1: \nThe average power demand at the Boston City Hall is at its highest from 10 am to 2 pm with a peak of 2000 KW.\n
\n\nExample Insight 2: The average power demand at Boston city Hall is highest during the winter (from December - February) with December being the peak.\n
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ei","database":"$undefined"},{"id":36,"value":"## Data Abstraction (What?)\n\nDataset Type: A table with 2 columns (attributes) and 106,766 rows (records)\n
\n\nData Availability: It is a static dataset taken from [data.boston.gov](https://data.boston.gov/dataset/1b894599-21ff-478f-937d-653954977951/resource/f123e65d-dc0e-4c83-9348-ed46fec498c0/download/tmpq6k6kydy.csv)\n
\n\nAttributes: \n
\n1. DateTime_Measured: A timestamp indicating when the reading was taken. (Temporal)\n\n2. Total_Demand_KW: The power consumption demand in Kilo Watts (KW). (Quantitative, Sequential)\n\nData Processing: The power demand was in string format, so I had to [convert the records to a numerical value](#dp). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"what","database":"$undefined"},{"id":34,"value":"## Task Abstraction (Why?)\n\nT1: Identify which period of the day has the highest and lowest power consumption demands. (Identify, Trends)\n - T1.1: Identify the peaks during those time periods. (Identify, Extremes)\n\nT2: Identify which season (3 month periods) of the year has the highest power demand. (Identify, Trends)\n - T2.1: Identify the month which has the highest demand in that season. (Identify, Extremes)\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"why","database":"$undefined"},{"id":498,"value":"## Marks and Channels (How?)\n\nMarks: \n- Line (Idioms 1 & 2)\n- Point (Idiom 4)\n- Bar (Idioms 6 & 7)\n- Area (Idioms 3 & 5)\n\nChannels: \n- Position on an aligned scale(x-axis): \"DateTime_Measured\" our temporal attribute is represented using position on an aligned scale. (Idioms - 1,2,3,4,5,6 & 7)
\n\n- Position on an aligned scale(y-axis): \"Total_Demand_KW\"(Quantitative, Sequential), the power demand is represented as a position on an aligned scale. (Idioms - 1,4,5,6 & 7)
\n\n- Position on an aligned scale(y-axis): \"DateTime_Measured\"(Temporal), the power demand is represented as a position on an aligned scale. (Idioms - 2 & 3)
\n\n- Color Saturation: The \"Total_Demand_KW\"(Quantitative, Sequential) is represented using color saturation. (Idioms - 2,3,4,5,6 & 7)
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"how","database":"$undefined"},{"id":539,"value":"\n## Target Audience (Users)\n1. City Planners and Policy Makers: They could use these insights to make data-driven decisions about energy policies, optimizing energy use, and scheduling power distribution to avoid overloads. For example, if peak demand is identified between 10 am and 2 pm, strategies for load management could be employed during these hours.\n\n2. Utility Companies and Energy Providers: Insights into high-demand times and seasonal patterns allow utility companies to better anticipate demand surges, plan energy distribution, and prevent blackouts. They may also use the data for rate planning based on peak and off-peak times.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"users","database":"$undefined"},{"id":568,"value":"## Expressiveness and Effectiveness\n\nI have chosen Idiom 4 as the one suited best for Task 1 (T1)\n\nExpressiveness: The idiom is very expressive as it displays each record as a point along an x-axis (Time) and y-axis(Power demand), therefore using the best channels (position on an aligned scale) for both our attributes, while also double encoding the power demand using color.\n\nEffectiveness: The idiom clearly helps us accomplish our task of identifying which time period requires the most power and which time period requires the least.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ee","database":"$undefined"},{"id":533,"value":"## Real Insights\n1. Insight 1 (From Idiom 4): The hours of the day between 9 am and 4 pm require the most power with a peak of 2257 kW. While the hours between midnight (12 am) and 5 am require the least power with the max power demand being 1724 kW. (T1)\n\n2. Insight 2 (From Idiom 7): The winter season (November - January) has the highest power demand overall (T2). With December being the peak month (T2.1)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ri","database":"$undefined"},{"id":588,"value":"## YouTube Video\nClick [here](https://youtu.be/w9DL4FXQMp8) to check out my YouTube video on the project!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"yt","database":"$undefined"},{"id":590,"value":"## Tweet\n\nClick [here](https://x.com/JonathanTa84074/status/1852540227149320687) to check out my tweet on the project!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"x","database":"$undefined"},{"id":12,"value":"Inputs.table(data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"navio(d3data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"d3data = FileAttachment(\"City_Hall_power_consumption.csv\")\n .text()\n .then(d3.csvParse)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":174,"value":"timeTruncateSelected = timeUnitOptions.slice(0, timeTruncate).join(\"\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":388,"value":"timeTruncateSelected2 = timeUnitOptions.slice(0, timeTruncate2).join(\"\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":152,"value":"timeUnitOptions = [\"year\", \"month\", \"date\", \"hours\", \"minutes\", \"seconds\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"dp = d3data.forEach(d => {\n d.Total_Demand_KW = parseFloat(d.Total_Demand_KW); // Convert the attribute from string to float\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"data = FileAttachment(\"City_Hall_power_consumption.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"import { navio} from \"@john-guerra/navio\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"import {vl} from \"@vega/vega-lite-api-v5\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"import { select } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":213,"value":"d3 = require(\"d3-dsv\", \"d3-time-format\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W31","stdlib":"1"}} 30:"$2f:body"