ARC Temperature & Humidity Dashboard

Interactive environmental monitoring dashboard for the Architecture for Resilient Communities (ARC) Cool Buildings Programme. Tracks temperature and humidity inside ARC buildings in two regions: the Al-Mizan Children's Ecovillage (CEV) near Mkuranga, Tanzania, and the UK.

The dashboard is a self-contained HTML page that updates automatically and works offline once loaded.

Live dashboard: actionresearchprojects.net/graphs/arc-temp-humid

What This Project Does

This project collects temperature and humidity data from sensors placed inside and around ARC buildings. It combines that data with external weather information and climate patterns, then builds an interactive dashboard where you can explore it all visually.

The main goals are:

Monitor indoor conditions in House 5, the Schoolteacher's House, Grove Cottage and Holywell Barn over time
Assess thermal comfort using the EN16798-1 adaptive comfort standard for naturally ventilated buildings
Compare indoor vs outdoor temperatures to understand how building design affects conditions inside
Track climate patterns like El Nino, the Indian Ocean Dipole, and the Madden-Julian Oscillation that influence local weather
Share findings through a public, interactive web dashboard that anyone can explore

Data Sources

The dashboard pulls data from four different sources:

Source	What It Measures	How It Gets Updated
TinyTag loggers	Indoor temperature and humidity at 24+ locations (rooms, ceilings, exterior walls)	Manually downloaded as Excel files from physical sensors
Omnisense sensors	Indoor temperature and humidity from 10 wireless IoT sensors	Automatically fetched twice daily by GitHub Actions
Open-Meteo API	Outdoor temperature and humidity (historical + 7-day forecast)	Automatically fetched twice daily by GitHub Actions
Climate indices	ENSO (El Nino/La Nina), IOD (Indian Ocean Dipole), MJO (Madden-Julian Oscillation)	Automatically fetched weekly by GitHub Actions

Buildings and Regions

The building selector offers each building on its own, plus a combined view per region:

ARC Tanzania            <- both Tanzanian buildings on one chart
     House 5
     Schoolteacher's House
ARC UK                  <- both UK buildings on one chart
     Grove Cottage
     Holywell Barn

Region rows are selectable in their own right. In a region view the sidebar groups loggers under a heading per building, because names repeat across buildings (both House 5 and the Schoolteacher's House have a "Bedroom 1").

Each region carries its own timezone and season names:

Region	Timezone	Seasons
ARC Tanzania	Africa/Dar_es_Salaam (EAT, no DST)	Kiangazi, Masika, Kiangazi, Vuli
ARC UK	Europe/London (GMT/BST, observes DST)	Winter, Spring, Summer, Autumn

Timestamps always display as wall-clock time at the site, whatever timezone the viewer's browser is in.

The UK buildings have no Open-Meteo feed yet -- each carries its own external ambient sensor, which is what its adaptive comfort running mean uses. To add one, see the commented LOCATIONS["uk"] entry in fetch_openmeteo.py.

How It Works

Build Process

A Python script (build.py) does all the heavy lifting:

Reads sensor data from all sources
Aligns timestamps to East African Time (UTC+3)
Calculates adaptive comfort using the EN16798-1 exponential running mean
Processes climate cycle data into phase indicators
Packages everything into a single index.html file with embedded data and interactive Plotly.js charts

The output is a completely self-contained HTML file. No database, no backend, no API calls at runtime (except loading a small config file for custom logger names).

Automation

Three GitHub Actions workflows keep the dashboard current without any manual work:

Dashboard data update (runs at 04:00 and 16:00 UTC daily) - fetches fresh Omnisense and Open-Meteo data, rebuilds the dashboard, and pushes changes
Climate cycle update (runs every Monday at 06:30 UTC) - fetches the latest ENSO, IOD, and MJO data
Main site notification (runs on any push) - tells the main ARC website to pull the updated dashboard

Dashboard Features

Interactive line graphs for temperature and humidity over time
Adaptive comfort scatter plot showing indoor temperature vs the EN16798-1 running mean, with comfort band overlays (see the applicability note below)
Density heatmap showing how indoor temperatures are distributed
32C thermal threshold marker for quick visual reference
Climate cycle overlays showing ENSO, IOD, and MJO phases alongside the data
Time range selection to zoom into specific periods
Logger filtering to show or hide individual sensors
Metric toggle to switch between temperature and humidity views
Long-term mode that shows extended climate trends and projections
PNG export for use in reports and presentations
Admin config page (config.html) for renaming loggers and changing categories without rebuilding

Adaptive comfort: applicability

Method is applicable only for occupant-controlled naturally conditioned spaces that meet all of the following criteria: (a) There is no mechanical cooling system installed. No heating system is in operation; (b) Metabolic rates ranging from 1.0 to 1.5 met; and (c) Occupants are free to adapt their clothing to the indoor and/or outdoor thermal conditions within a range at least as wide as 0.5-1.0 clo.

This applies to the adaptive comfort scatter plot, its comfort band, and the running mean on its x-axis. The dashboard shows the same note beside the comfort band selector and in every info tooltip that explains the method.

Project Structure

arc_temp_humid/
|
|-- build.py                 Core build script (generates index.html)
|-- fetch_openmeteo.py       Downloads weather data from Open-Meteo API
|-- fetch_omnisense.py       Scrapes sensor data from Omnisense platform
|-- fetch_cycles.py          Fetches climate cycle indices (ENSO, IOD, MJO)
|-- index.html               Generated dashboard (do not edit directly)
|-- config.html              Admin page for editing logger names
|
|-- data/
|   |-- config.json          User overrides for logger names (tracked)
|   |-- loggers.json         Logger manifest (generated, tracked)
|   |-- sensor_snapshot.json Pre-processed TinyTag data for fast rebuilds
|   |-- openmeteo/           Weather API data files, Tanzania (tracked)
|   |-- omnisense/           IoT sensor data files, Tanzania (tracked)
|   |-- omnisense_uk/        IoT sensor data files, UK (tracked)
|   |-- cycles/              Climate index data (tracked)
|   |-- hist_proj/           Long-term climate projection data (tracked)
|   |-- house5/              TinyTag Excel files for House 5 (local only)
|   |-- schoolteacher/               TinyTag Excel files for Schoolteacher's House (local only)
|                            (Grove Cottage and Holywell Barn are Omnisense-only -- no Excel files)
|
|-- .github/workflows/      GitHub Actions automation
|-- UPDATE.md                Instructions for data updates and git workflow
|-- CHANGELOG.md             Record of all changes
|-- runningmean.md           Technical explanation of the adaptive comfort algorithm

Buildings Monitored

House 5

The primary building being monitored. Has 24 sensor locations covering:

Living room, kitchen, bedrooms, study, washrooms
Above-ceiling spaces (to measure heat transfer through the roof)
Exterior walls
Both TinyTag (wired) and Omnisense (wireless) sensors

Schoolteacher's House

A second building with 3 TinyTag sensors for comparison. Uses a nearby TinyTag outdoor sensor as its reference for adaptive comfort calculations.

How Data Flows

TinyTag Excel files -----+
(added manually)         |
                         v
Omnisense sensors ----> build.py ----> index.html ----> actionresearchprojects.net
(auto-fetched)           ^                              (live dashboard)
                         |
Open-Meteo API ----------+
(auto-fetched)           |
                         |
Climate indices ---------+
(auto-fetched weekly)

Automated path: GitHub Actions runs the fetch scripts on a schedule, then runs build.py --auto (which uses cached TinyTag data from sensor_snapshot.json plus fresh online data), and pushes the updated index.html.

Manual path: When new TinyTag Excel files are available, place them in the appropriate data/ subfolder and run python build.py for a full rebuild.

Adaptive Comfort

The dashboard uses the EN16798-1 standard to assess thermal comfort in naturally ventilated buildings. This works by calculating an "exponential running mean" of outdoor temperature, which represents what occupants have been experiencing recently and have adapted to.

The formula is:

running_mean(today) = 0.2 * yesterday's_mean_temp + 0.8 * running_mean(yesterday)

This gives more weight to recent days while still accounting for the past week or two. The dashboard then plots indoor temperature against this running mean, with comfort bands showing acceptable ranges.

The default comfort model is the Vellei model for high humidity (RH > 60%), which is appropriate for the tropical climate near Mkuranga.

For full technical details, see runningmean.md.

Key Technical Details

All timestamps are in East African Time (EAT, UTC+3)
The dashboard is a single self-contained HTML file with all data embedded as JSON in script tags
index.html is generated by build.py and should never be edited directly
The --auto flag lets build.py skip reading Excel files and use the cached sensor_snapshot.json instead, making automated rebuilds much faster
config.json stores user-customized logger names and is fetched by the dashboard at load time, so name changes show up without needing a rebuild

Technologies Used

Category	Tools
Languages	Python, JavaScript, HTML, CSS
Data processing	pandas, openpyxl
Visualization	Plotly.js
Hosting	GitHub Pages, actionresearchprojects.net
Automation	GitHub Actions
Data sources	Open-Meteo API, Omnisense platform, TinyTag loggers, NOAA/BoM climate data

Documentation

File	What It Covers
`UPDATE.md`	Full data update workflow including git commands
`CHANGELOG.md`	History of all changes made to the project
`runningmean.md`	Technical explanation of the adaptive comfort algorithm

License

This project is part of the Architecture for Resilient Communities Cool Buildings Programme, a research initiative focused on sustainable building design in tropical climates.