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feat: add tibber electricity price provider (#1160)
Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
GitHub
@@ -10,6 +10,7 @@ from akkudoktoreos.prediction.elecpriceenergycharts import (
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)
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from akkudoktoreos.prediction.elecpricefixed import ElecPriceFixedCommonSettings
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from akkudoktoreos.prediction.elecpriceimport import ElecPriceImportCommonSettings
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from akkudoktoreos.prediction.elecpricetibber import ElecPriceTibberCommonSettings
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def elecprice_provider_ids() -> list[str]:
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@@ -17,9 +18,14 @@ def elecprice_provider_ids() -> list[str]:
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try:
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prediction_eos = get_prediction()
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except Exception:
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# Prediction may not be initialized
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# Return at least provider used in example
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return ["ElecPriceAkkudoktor"]
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# Prediction may not be initialized. Return static built-in provider ids.
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return [
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"ElecPriceAkkudoktor",
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"ElecPriceEnergyCharts",
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"ElecPriceFixed",
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"ElecPriceImport",
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"ElecPriceTibber",
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]
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return [
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provider.provider_id()
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@@ -72,6 +78,11 @@ class ElecPriceCommonSettings(SettingsBaseModel):
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json_schema_extra={"description": "Energy Charts provider settings."},
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)
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tibber: ElecPriceTibberCommonSettings = Field(
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default_factory=ElecPriceTibberCommonSettings,
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json_schema_extra={"description": "Tibber electricity price provider settings."},
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)
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@computed_field # type: ignore[prop-decorator]
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@property
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def providers(self) -> list[str]:
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472
src/akkudoktoreos/prediction/elecpricetibber.py
Normal file
472
src/akkudoktoreos/prediction/elecpricetibber.py
Normal file
@@ -0,0 +1,472 @@
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"""Retrieves and processes electricity price forecast data from Tibber."""
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from typing import Any, List, Optional, Union
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import numpy as np
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import pandas as pd
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import requests
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from loguru import logger
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from pydantic import Field, ValidationError
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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from akkudoktoreos.config.configabc import SettingsBaseModel
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from akkudoktoreos.core.cache import cache_in_file
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from akkudoktoreos.core.pydantic import PydanticBaseModel
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from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
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from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
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TIBBER_GRAPHQL_URL = "https://api.tibber.com/v1-beta/gql"
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TIBBER_DAILY_SEASONAL_HOURS = 24 * 7
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TIBBER_WEEKLY_SEASONAL_HOURS = 24 * 35
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TIBBER_PRICE_QUERY = """
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query TibberPriceInfo {
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viewer {
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homes {
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id
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currentSubscription {
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priceInfo {
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today {
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startsAt
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total
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energy
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}
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tomorrow {
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startsAt
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total
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energy
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}
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}
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priceInfoRange(resolution: QUARTER_HOURLY, last: 672) {
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nodes {
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startsAt
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total
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energy
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}
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}
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}
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}
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}
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}
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"""
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# Same query, but requesting priceInfo (and therefore its today/tomorrow
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# fields) at quarter-hourly resolution. Tibber defines ``resolution`` on
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# Subscription.priceInfo, not on the nested PriceInfo.today/tomorrow fields.
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# Tried first; on a GraphQL schema error from an older API the provider falls
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# back to TIBBER_PRICE_QUERY.
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TIBBER_PRICE_QUERY_QUARTER_HOURLY = """
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query TibberPriceInfo {
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viewer {
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homes {
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id
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currentSubscription {
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priceInfo(resolution: QUARTER_HOURLY) {
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today {
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startsAt
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total
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energy
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}
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tomorrow {
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startsAt
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total
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energy
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}
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}
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priceInfoRange(resolution: QUARTER_HOURLY, last: 672) {
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nodes {
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startsAt
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total
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energy
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}
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}
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}
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}
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}
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}
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"""
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class ElecPriceTibberCommonSettings(SettingsBaseModel):
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"""Common settings for the Tibber electricity price provider."""
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access_token: Optional[str] = Field(
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default=None,
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json_schema_extra={
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"description": "Tibber API access token.",
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"examples": ["tibber_pat_..."],
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},
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)
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home_id: Optional[str] = Field(
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default=None,
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json_schema_extra={
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"description": (
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"Optional Tibber home id. If omitted, the first home with a subscription is used."
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),
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"examples": ["00000000-0000-0000-0000-000000000000"],
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},
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)
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class TibberPricePoint(PydanticBaseModel):
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"""Single Tibber price point."""
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startsAt: str
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total: float
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energy: Optional[float] = None
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class TibberPriceConnection(PydanticBaseModel):
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"""Tibber connection for historical price nodes."""
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nodes: List[TibberPricePoint] = Field(default_factory=list)
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class TibberPriceInfo(PydanticBaseModel):
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"""Tibber price info for today and tomorrow."""
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today: List[TibberPricePoint] = Field(default_factory=list)
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tomorrow: List[TibberPricePoint] = Field(default_factory=list)
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class TibberSubscription(PydanticBaseModel):
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"""Tibber subscription data."""
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priceInfo: Optional[TibberPriceInfo] = None
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priceInfoRange: Optional[TibberPriceConnection] = None
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class TibberHome(PydanticBaseModel):
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"""Tibber home data."""
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id: str
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currentSubscription: Optional[TibberSubscription] = None
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class TibberViewer(PydanticBaseModel):
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"""Tibber viewer data."""
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homes: List[TibberHome] = Field(default_factory=list)
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class TibberData(PydanticBaseModel):
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"""Tibber GraphQL data payload."""
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viewer: TibberViewer
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class TibberGraphQLError(PydanticBaseModel):
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"""Tibber GraphQL error item."""
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message: str
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class TibberGraphQLResponse(PydanticBaseModel):
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"""Tibber GraphQL response payload."""
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data: Optional[TibberData] = None
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errors: Optional[List[TibberGraphQLError]] = None
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class ElecPriceTibber(ElecPriceProvider):
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"""Fetch and process electricity price forecast data from Tibber."""
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@classmethod
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def provider_id(cls) -> str:
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"""Return the unique identifier for the Tibber provider."""
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return "ElecPriceTibber"
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def historic_hours_min(self) -> int:
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"""Keep enough history for weekly seasonal price extrapolation."""
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return 24 * 35
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@classmethod
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def _validate_data(cls, json_str: Union[bytes, Any]) -> TibberGraphQLResponse:
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"""Validate Tibber GraphQL response data."""
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try:
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tibber_data = TibberGraphQLResponse.model_validate_json(json_str)
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except ValidationError as e:
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error_msg = ""
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for error in e.errors():
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field = " -> ".join(str(x) for x in error["loc"])
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message = error["msg"]
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error_type = error["type"]
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error_msg += f"Field: {field}\nError: {message}\nType: {error_type}\n"
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logger.error(f"Tibber schema change: {error_msg}")
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raise ValueError(error_msg)
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if tibber_data.errors:
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error_msg = "; ".join(error.message for error in tibber_data.errors)
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error_msg = f"Tibber GraphQL error: {error_msg}"
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logger.error(error_msg)
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raise ValueError(error_msg)
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return tibber_data
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def _api_price_counts(self, response: TibberGraphQLResponse) -> tuple[int, int, int]:
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"""Return Tibber API price counts for history, today, and tomorrow."""
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home = self._select_home(response)
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subscription = home.currentSubscription
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if subscription is None:
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raise ValueError("Tibber home has no current subscription")
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history_count = 0
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today_count = 0
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tomorrow_count = 0
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if subscription.priceInfoRange is not None:
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history_count = len(subscription.priceInfoRange.nodes)
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if subscription.priceInfo is not None:
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today_count = len(subscription.priceInfo.today)
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tomorrow_count = len(subscription.priceInfo.tomorrow)
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return history_count, today_count, tomorrow_count
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@cache_in_file(with_ttl="1 hour")
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def _request_forecast(self) -> TibberGraphQLResponse:
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"""Fetch electricity price data from the Tibber GraphQL API."""
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access_token = self.config.elecprice.tibber.access_token
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if not access_token:
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raise ValueError("Tibber access_token is required")
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# Prefer quarter-hourly today/tomorrow prices; fall back to the hourly
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# query when the Tibber API rejects the resolution argument. Tibber
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# signals schema errors either as HTTP 400 or as HTTP 200 with an
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# "errors" array, so both must route to the fallback (raise_for_status
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# must NOT run before the fallback check).
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response = None
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queries = (TIBBER_PRICE_QUERY_QUARTER_HOURLY, TIBBER_PRICE_QUERY)
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for attempt, query in enumerate(queries, start=1):
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response = requests.post(
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TIBBER_GRAPHQL_URL,
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json={"query": query},
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headers={
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"Authorization": f"Bearer {access_token}",
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"Content-Type": "application/json",
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},
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timeout=30,
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)
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logger.debug(f"Response from Tibber GraphQL API: {response}")
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if response.ok and b'"errors"' not in response.content:
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break
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if attempt < len(queries):
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logger.info(
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"Tibber rejected the quarter-hourly priceInfo query "
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"(HTTP {}): {} - falling back to hourly today/tomorrow prices.",
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response.status_code,
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response.text[:300],
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)
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else:
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# Final (hourly) attempt failed for real - surface the error.
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response.raise_for_status()
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if response is None: # pragma: no cover - the query tuple is never empty
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raise RuntimeError("No Tibber GraphQL query was attempted")
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tibber_data = self._validate_data(response.content)
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self.update_datetime = to_datetime(in_timezone=self.config.general.timezone)
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return tibber_data
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def _select_home(self, response: TibberGraphQLResponse) -> TibberHome:
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"""Select the configured Tibber home from a GraphQL response."""
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if response.data is None:
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raise ValueError("Tibber response does not contain data")
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home_id = self.config.elecprice.tibber.home_id
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if home_id:
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for home in response.data.viewer.homes:
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if home.id == home_id:
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if home.currentSubscription is None:
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raise ValueError(f"Tibber home '{home_id}' has no current subscription")
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return home
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raise ValueError("Tibber home_id not found")
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for home in response.data.viewer.homes:
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if home.currentSubscription is not None:
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return home
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raise ValueError("No Tibber home with a current subscription found")
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def _parse_data(self, response: TibberGraphQLResponse) -> pd.Series:
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"""Parse Tibber prices into EOS market prices in EUR/Wh."""
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home = self._select_home(response)
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subscription = home.currentSubscription
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if subscription is None:
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raise ValueError("Tibber home has no current subscription")
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points: list[TibberPricePoint] = []
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if subscription.priceInfoRange is not None:
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points.extend(subscription.priceInfoRange.nodes)
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if subscription.priceInfo is not None:
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points.extend(subscription.priceInfo.today)
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points.extend(subscription.priceInfo.tomorrow)
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if not subscription.priceInfo.tomorrow:
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logger.warning("Tibber tomorrow prices not available yet")
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if not points:
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raise ValueError("Tibber response contains no price points")
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series_data = pd.Series(dtype=float)
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for point in points:
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orig_datetime = to_datetime(point.startsAt, in_timezone=self.config.general.timezone)
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series_data.at[orig_datetime] = point.total / 1000.0
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return series_data.sort_index()
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def _normalize_series(self, series: pd.Series) -> pd.Series:
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"""Normalize Tibber prices while preserving their native resolution.
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The Tibber API delivers either hourly or quarter-hourly prices. EOS resamples
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the stored records onto the optimization grid on demand (``key_to_array``), so
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the provider must keep the native step size (e.g. 15 minutes) instead of
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pre-aggregating to hourly values. Duplicate timestamps are collapsed (mean) and
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the series is sorted, but the resolution is left untouched.
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"""
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if series.empty:
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return series
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series = series.sort_index()
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series.index = pd.to_datetime([to_datetime(index).isoformat() for index in series.index])
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series = series.groupby(level=0).mean().sort_index()
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return series.dropna()
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def _resolution_seconds(self, series: pd.Series) -> int:
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"""Infer the native slot size in seconds from the series timestamps.
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Uses the median of the timestamp differences so that a single outlier gap does
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not distort the result. Falls back to hourly (3600 s) when fewer than two
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timestamps are available.
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"""
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if len(series) < 2:
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return 3600
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deltas = pd.DatetimeIndex(series.index).to_series().diff().dropna()
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if deltas.empty:
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return 3600
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resolution = int(round(deltas.dt.total_seconds().median()))
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return resolution if resolution > 0 else 3600
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def _cap_outliers(self, data: np.ndarray, sigma: int = 2) -> np.ndarray:
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mean = data.mean()
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std = data.std()
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lower_bound = mean - sigma * std
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upper_bound = mean + sigma * std
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capped_data = data.clip(min=lower_bound, max=upper_bound)
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return capped_data
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def _predict_ets(self, history: np.ndarray, seasonal_periods: int, hours: int) -> np.ndarray:
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clean_history = self._cap_outliers(history)
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model = ExponentialSmoothing(
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clean_history, seasonal="add", seasonal_periods=seasonal_periods
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).fit()
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return model.forecast(hours)
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def _predict_median(self, history: np.ndarray, hours: int) -> np.ndarray:
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clean_history = self._cap_outliers(history)
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return np.full(hours, np.median(clean_history))
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def _predict_missing_prices(
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self, history: np.ndarray, slots: int, slots_per_hour: int
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) -> np.ndarray:
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"""Forecast missing future prices from the available history.
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Works on the native resolution of the series: ``slots_per_hour`` scales the
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hour-based seasonal windows into slot counts, so the seasonal periods and
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history thresholds stay correct at both hourly (``slots_per_hour == 1``) and
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quarter-hourly (``slots_per_hour == 4``) resolution.
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"""
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numeric_history = np.asarray(history, dtype=float)
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numeric_history = numeric_history[np.isfinite(numeric_history)]
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history_slots = len(numeric_history)
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weekly_seasonal_slots = TIBBER_WEEKLY_SEASONAL_HOURS * slots_per_hour
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daily_seasonal_slots = TIBBER_DAILY_SEASONAL_HOURS * slots_per_hour
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if history_slots > weekly_seasonal_slots:
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logger.info(
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"Using weekly seasonal ETS forecast for Tibber electricity prices "
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"with {} historical values.",
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history_slots,
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)
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return self._predict_ets(
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numeric_history, seasonal_periods=168 * slots_per_hour, hours=slots
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)
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if history_slots > daily_seasonal_slots:
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logger.info(
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"Using daily seasonal ETS forecast for Tibber electricity prices "
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"with {} historical values.",
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history_slots,
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)
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return self._predict_ets(
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numeric_history, seasonal_periods=24 * slots_per_hour, hours=slots
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)
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if history_slots > 0:
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logger.warning(
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"Using median fallback for Tibber electricity prices because only {} "
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"historical values are available.",
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history_slots,
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)
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return self._predict_median(numeric_history, hours=slots)
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logger.error("No data available for prediction")
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raise ValueError("No data available")
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async def _update_data(self, force_update: Optional[bool] = False) -> None:
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"""Update Tibber price data and extrapolate missing future prices."""
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tibber_data = self._request_forecast(force_update=force_update) # type: ignore
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if not self.ems_start_datetime:
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raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
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api_history_count, api_today_count, api_tomorrow_count = self._api_price_counts(tibber_data)
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series_data = self._normalize_series(self._parse_data(tibber_data))
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if series_data.empty:
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raise ValueError("Tibber response contains no usable price points")
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resolution_seconds = self._resolution_seconds(series_data)
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slots_per_hour = round(3600 / resolution_seconds)
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highest_orig_datetime = to_datetime(series_data.index.max())
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await self.key_from_series("elecprice_marketprice_wh", series_data)
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history = await self.key_to_array(
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key="elecprice_marketprice_wh",
|
||||
end_datetime=highest_orig_datetime,
|
||||
interval=to_duration(f"{resolution_seconds} seconds"),
|
||||
fill_method="linear",
|
||||
)
|
||||
|
||||
if not highest_orig_datetime:
|
||||
error_msg = f"Highest original datetime not available: {highest_orig_datetime}"
|
||||
logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
covered_slots = (
|
||||
highest_orig_datetime - self.ems_start_datetime
|
||||
).total_seconds() // resolution_seconds
|
||||
needed_slots = int(self.config.prediction.hours * slots_per_hour - covered_slots)
|
||||
|
||||
if needed_slots <= 0:
|
||||
logger.warning(
|
||||
"No prediction needed. "
|
||||
f"needed_slots={needed_slots}, "
|
||||
f"hours={self.config.prediction.hours}, "
|
||||
f"slots_per_hour={slots_per_hour}, "
|
||||
f"highest_orig_datetime={highest_orig_datetime}, "
|
||||
f"start_datetime={self.ems_start_datetime}"
|
||||
)
|
||||
return
|
||||
|
||||
logger.info(
|
||||
"Tibber electricity price input: api_history={}, api_today={}, "
|
||||
"api_tomorrow={}, resolution_seconds={}, combined_history_slots={}, "
|
||||
"needed_forecast_slots={}.",
|
||||
api_history_count,
|
||||
api_today_count,
|
||||
api_tomorrow_count,
|
||||
resolution_seconds,
|
||||
len(history),
|
||||
needed_slots,
|
||||
)
|
||||
prediction = self._predict_missing_prices(
|
||||
history, slots=needed_slots, slots_per_hour=slots_per_hour
|
||||
)
|
||||
|
||||
prediction_series = pd.Series(
|
||||
data=prediction,
|
||||
index=[
|
||||
highest_orig_datetime + to_duration(f"{(i + 1) * resolution_seconds} seconds")
|
||||
for i in range(len(prediction))
|
||||
],
|
||||
)
|
||||
await self.key_from_series("elecprice_marketprice_wh", prediction_series)
|
||||
@@ -35,6 +35,7 @@ from akkudoktoreos.prediction.elecpriceakkudoktor import ElecPriceAkkudoktor
|
||||
from akkudoktoreos.prediction.elecpriceenergycharts import ElecPriceEnergyCharts
|
||||
from akkudoktoreos.prediction.elecpricefixed import ElecPriceFixed
|
||||
from akkudoktoreos.prediction.elecpriceimport import ElecPriceImport
|
||||
from akkudoktoreos.prediction.elecpricetibber import ElecPriceTibber
|
||||
from akkudoktoreos.prediction.feedintarifffixed import FeedInTariffFixed
|
||||
from akkudoktoreos.prediction.feedintariffimport import FeedInTariffImport
|
||||
from akkudoktoreos.prediction.loadakkudoktor import (
|
||||
@@ -79,6 +80,7 @@ elecprice_akkudoktor = ElecPriceAkkudoktor()
|
||||
elecprice_energy_charts = ElecPriceEnergyCharts()
|
||||
elecprice_fixed = ElecPriceFixed()
|
||||
elecprice_import = ElecPriceImport()
|
||||
elecprice_tibber = ElecPriceTibber()
|
||||
feedintariff_fixed = FeedInTariffFixed()
|
||||
feedintariff_import = FeedInTariffImport()
|
||||
loadforecast_akkudoktor = LoadAkkudoktor()
|
||||
@@ -103,6 +105,7 @@ def prediction_providers() -> list[
|
||||
ElecPriceEnergyCharts,
|
||||
ElecPriceFixed,
|
||||
ElecPriceImport,
|
||||
ElecPriceTibber,
|
||||
FeedInTariffFixed,
|
||||
FeedInTariffImport,
|
||||
LoadAkkudoktor,
|
||||
@@ -130,6 +133,7 @@ def prediction_providers() -> list[
|
||||
elecprice_energy_charts, \
|
||||
elecprice_fixed, \
|
||||
elecprice_import, \
|
||||
elecprice_tibber, \
|
||||
feedintariff_fixed, \
|
||||
feedintariff_import, \
|
||||
loadforecast_akkudoktor, \
|
||||
@@ -153,6 +157,7 @@ def prediction_providers() -> list[
|
||||
elecprice_energy_charts,
|
||||
elecprice_fixed,
|
||||
elecprice_import,
|
||||
elecprice_tibber,
|
||||
feedintariff_fixed,
|
||||
feedintariff_import,
|
||||
loadforecast_akkudoktor,
|
||||
@@ -181,6 +186,7 @@ class Prediction(PredictionContainer):
|
||||
ElecPriceEnergyCharts,
|
||||
ElecPriceFixed,
|
||||
ElecPriceImport,
|
||||
ElecPriceTibber,
|
||||
FeedInTariffFixed,
|
||||
FeedInTariffImport,
|
||||
LoadAkkudoktor,
|
||||
|
||||
Reference in New Issue
Block a user