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The most comprehensive Tibber price integration for Home Assistant. Get 100+ sensors with quarter-hourly precision, intelligent best/peak price period detection, price forecasts, trend analysis, volat
PluginsPython · JavaScriptGitHub @jpawlowskiOnlineOwner unconfirmed
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The most comprehensive Tibber price integration for Home Assistant. Get 100+ sensors with quarter-hourly precision, intelligent best/peak price period detection, price forecasts, trend analysis, volatility tracking, and beautiful chart visualizations - all from a single integration. Automate your energy consumption like a pro.
Taken from the repository README
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Read the full README ↗⚠️ Not affiliated with Tibber This is an independent, community-maintained custom integration for Home Assistant. It is not an official Tibber product and is not affiliated with or endorsed by Tibber AS.
The most comprehensive Tibber price integration for Home Assistant. Get 100+ sensors with quarter-hourly precision, intelligent best/peak price period detection, price forecasts, trend analysis, volatility tracking, and dashboard cards of its own - all from a single integration. Automate your energy consumption like a pro.
📚 Complete Documentation — Installation, guides, examples, and full sensor reference:
Quick Links: Installation · Sensor Reference · Dashboard Cards · ApexCharts · Automations · FAQ · Changelog
Most Tibber integrations give you a single price sensor. This one gives you a complete energy optimization toolkit:
priceInfo and priceInfoRange endpoints. All ratings and statistics are calculated locally.Prerequisites: HACS (Home Assistant Community Store) must be installed.
Or manually: Settings → Devices & Services → + Add Integration → search "Tibber Price Information & Ratings"
The integration brings its own cards. Add them from the card picker, pick your home, done.
Now Card — the price now and what to make of it: a best price period running, cheap until, the next best price. As a tile, a gauge or a clock of the coming twelve hours, with the trend in the corner. A tap opens the details, or the whole forecast card in a window.
Forecast Card — the coming hours and parts of the day like a weather forecast, in the price level's colors, with best and peak price periods underlined, and today and tomorrow on one scale. Tap any hour, part of the day or day for its details.
Chart Card — the price curve for the outlook, today, tomorrow or both days, in 15 or 60 minutes. Slide across it (or hold and drag on a phone) to read each price, its level and how far it lies from the average, in a line or a ring on top. Several styles to switch between, including the simple one of Tibber's app.
Cards inside cards — a now tile on your main dashboard opens the whole forecast with a tap and the price chart from a small icon; the editor shows each card held this way one level down, as Home Assistant's own stack cards do.
📖 Dashboard Cards → — every option, the tile feature, and how to read the cards
The integration provides 100+ entities across sensors, binary sensors, switches, and number entities. Here are the highlights — all key sensors are enabled by default:
Want this dashboard? The tibber_prices.get_dashboard_yaml action builds it for your home, ready to paste into one of your dashboards: Complete Dashboard as a Starting Point →
| Category | Highlights | Count |
|---|---|---|
| 💰 Prices | Current, next & previous interval price + rolling hour averages | 6+ |
| 📊 Statistics | Daily min/max/avg for today & tomorrow, 24h trailing & leading windows | 12+ |
| 🔮 Forecasts | Next 1h–12h average prices, price outlook & trajectory sensors | 20+ |
| 📈 Trends | Current trend direction, next trend change time & countdown | 3 |
| 📉 Volatility | Today, tomorrow, next 24h & combined volatility levels | 4 |
| 🏷️ Levels & Ratings | 5-level (API) and 3-level (computed) classification per interval, hour & day | 12+ |
| ⏰ Period Timing | Best/peak: end time, duration, remaining, progress, next start | 10+ |
| 🔌 Binary Sensors | Best price period, peak price period, tomorrow data available, API connection | 4+ |
| 🎛️ Runtime Config | Switches & numbers to adjust period detection live — no restart needed | 14 |
| 🔧 Diagnostics | Data lifecycle status, home metadata, grid info, subscription status | 15+ |
Every sensor includes rich attributes — timestamps, detailed descriptions, and context data. Enable Extended Descriptions in the integration options to get
long_descriptionandusage_tipson every entity.
📖 Complete Sensor Reference → — All entities with descriptions, attributes, and multi-language lookup
See the full automation examples guide for more recipes.
Run appliances when electricity is cheapest:
automation:
- alias: "Start Dishwasher During Best Price Period"
trigger:
- platform: state
entity_id: binary_sensor.tibber_best_price_period
to: "on"
action:
- action: switch.turn_on
target:
entity_id: switch.dishwasher
Reduce heating when prices spike above average:
automation:
- alias: "Reduce Heating During High Prices"
trigger:
- platform: numeric_state
entity_id: sensor.tibber_current_interval_price_rating
above: 20 # More than 20% above 24h average
action:
- action: climate.set_temperature
target:
entity_id: climate.living_room
data:
temperature: 19
📖 More automations → — EV charging, heat pump control, price notifications, and more
The chart card covers the price curve with nothing to install. If you use ApexCharts a lot and want your price charts to look like the others on your dashboards, generate them with a single action call — dynamic Y-axis, color-coded price levels, and multiple chart modes included. get_dashboard_yaml builds the whole dashboard with them, too (chart_card: apexcharts).
📖 Chart examples & setup → | Actions reference →
Contributions are welcome! See the Contributing Guidelines and Developer Documentation to get started.
This integration is developed with extensive AI assistance (GitHub Copilot, Claude, and other AI tools). While AI enables rapid development, it's possible that some edge cases haven't been discovered yet. If you encounter any issues, please open an issue — we'll fix them (with AI help, of course! 😊).
Quality is ensured through automated linting (Ruff), static type checking (Pyright), and real-world testing.
This project is licensed under the MIT License — see the LICENSE file for details.