KEV0143/Adaptive-forecasting-of-electricity-consumption-24-168-720-h-with-load-regime-conditioning

Health v2 · maintenance only

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1545 stars · 0 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source tool for adaptive forecasting of hourly electricity consumption over 24, 168, and 720 hour horizons, using load regime conditioning to improve prediction accuracy. It is implemented as a machine learning forecasting library for energy time series.

Use cases

• forecast hourly electricity consumption for the next day
• predict weekly (168-hour) power demand
• generate monthly (720-hour) load forecasts
• model electricity consumption under different load regimes
• build adaptive energy demand forecasting pipelines
• evaluate multi-horizon load forecasting models

When to choose

• you need multi-horizon (24/168/720h) electricity load forecasts
• your consumption patterns vary by load regime and you want conditioning on them
• you want an Apache-2.0 licensed forecasting solution for energy time series

When to avoid

• you need forecasting for domains other than electricity consumption
• you require real-time sub-hourly forecasting granularity
• you need a turnkey commercial forecasting service with support

Facets

library · maturity active

machine-learning data-science machine-learning data-science energy python cross-platform time-series-forecasting electricity-load-forecasting energy hourly-consumption regime-conditioning 24-168-720-hour-horizons time-series

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