How to Use Time‑Series Data for Home Brewing and BBQ Temperature Monitoring

Overview

This guide describes how to build a low‑cost, open‑source temperature‑monitoring system for home brewing and barbecue using a Raspberry Pi, a temperature sensor, MQTT, a time‑series database, and a visualization dashboard. The system continuously records temperature, stores the data as a time series, visualizes trends, and generates automated alerts (e.g., via Slack) when predefined thresholds are crossed.

Hardware configuration

Raspberry Pi (any model with GPIO and network access) serves as the central controller.

Digital temperature sensor (e.g., DS18B20) is wired to the Pi’s GPIO pins.

A network camera captures images of the temperature display; images are transferred to the Pi and stored alongside sensor readings.

Software stack

Time‑series database (such as InfluxDB) installed on the Pi to store temperature values and image metadata.

MQTT broker (e.g., Mosquitto) runs on the Pi or on a nearby server. Sensor data is published to a topic like home/brew/temperature.

Python script reads the sensor via the w1thermsensor library (or equivalent), formats the reading as JSON, and publishes it to MQTT:

import paho.mqtt.client as mqtt
import json
# read sensor value
payload = json.dumps({"temp": temperature, "timestamp": time.time()})
client.publish("home/brew/temperature", payload)

Data ingestion can be performed by Telegraf (configured with an [[inputs.mqtt_consumer]] plugin) or by a custom subscriber that writes MQTT payloads into the time‑series database.

Dashboard (Grafana) connects to the database, displays temperature over time, and provides a panel for recent camera images.

Alert rules are defined in the database or Grafana to trigger Slack notifications when temperature falls below 18 °C, rises above 21 °C, or exceeds 23 °C. Example alert condition in InfluxDB Flux:

from(bucket:"brew")
  |> range(start: -5m)
  |> filter(fn: (r) => r._measurement == "temperature")
  |> filter(fn: (r) => r._value < 18 or r._value > 21)
  |> alert()

Barbecue / smoker integration

For a smoker, the same architecture is used with additional components:

Commercial temperature probes (up to six) connect to a Zigbee gateway.

The Zigbee gateway forwards measurements to an MQTT broker.

FireBoard, a cloud‑based smart‑food‑temperature service, can be used as an alternative data source. FireBoard exposes an HTTP API; a lightweight proxy server pulls the API data and writes it into the same time‑series database.

Alert thresholds are adjusted for cooking ranges (e.g., notify when the internal meat temperature stalls).

Alert workflow

When a threshold condition is met, the alert engine sends a message to a Slack webhook URL. The same mechanism can be extended to SMS or email by configuring additional webhook endpoints. For example, a “temperature stall” alert can be sent as an instant push to a mobile device, prompting the user to wrap the meat in foil.

Future enhancements

Implement predictive modeling (e.g., linear regression on recent temperature data) to estimate total cooking time.

Enrich visualizations with custom color schemes to highlight normal, warning, and critical ranges.

Integrate additional sensors such as digital hydrometers or density meters for fermentation monitoring.