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    - electricity-consumption-forecasting
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title: Electricity Consumption Forecasting
description: Use case of applying probabilistic time-series forecasting to predict electricity demand
tags:
  - use-case
  - energy
  - time-series
timestamp: "2026-06-19T17:43:25.894Z"
---

---

title: Electricity Consumption Forecasting
summary: A common use case for time-series forecasting where future electricity demand is predicted from historical consumption data, demonstrated with DeepAR.
sources:
  - classic-machine-learning-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T10:56:46.831Z"
updatedAt: "2026-06-19T14:11:42.560Z"
tags:
  - time-series
  - forecasting
  - energy
aliases:
  - electricity-consumption-forecasting
  - ECF
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# Electricity Consumption Forecasting

**Electricity consumption forecasting** is a classic [time-series forecasting](/concepts/multi-series-forecasting.md) problem that predicts future electricity demand based on historical usage patterns. Accurate forecasts are critical for utilities to balance supply and demand, plan grid operations, and manage energy costs.

One end-to-end implementation is documented in a Databricks notebook that uses the [GluonTS](/concepts/gluonts.md) probabilistic time-series library with the [DeepAR](/concepts/deepar.md) model on a [serverless GPU cluster](/concepts/databricks-serverless-gpu-cluster.md). The notebook covers the full pipeline: data ingestion, resampling, model training, prediction, visualization, and evaluation. ^[classic-machine-learning-databricks-on-aws.md]

## Notebook Workflow

The notebook demonstrates the following steps:

- **Data ingestion** – Loading historical electricity consumption data.
- **Resampling** – Converting raw observations to a regular time interval suitable for modeling.
- **Model training** – Training a DeepAR neural network on the resampled data.
- **Prediction** – Generating probabilistic forecasts for future time steps.
- **Visualization** – Plotting actual versus predicted values to assess model quality.
- **Evaluation** – Computing forecast error metrics.

All steps run on a serverless GPU cluster, enabling scalable training without manual infrastructure management. ^[classic-machine-learning-databricks-on-aws.md]

## Benefits of Probabilistic Forecasting

DeepAR produces [probabilistic forecasting|probabilistic forecasts](/concepts/probabilistic-time-series-forecasting.md), which quantify prediction uncertainty. Unlike point forecasts, probabilistic outputs provide confidence intervals that help grid operators with risk management and resource planning. ^[classic-machine-learning-databricks-on-aws.md]

## Related Concepts

- Time series forecasting
- [GluonTS](/concepts/gluonts.md)
- [DeepAR](/concepts/deepar.md)
- Probabilistic forecasting
- Automatic MLflow tracking

## Sources

- classic-machine-learning-databricks-on-aws.md

# Citations

1. [classic-machine-learning-databricks-on-aws.md](/references/classic-machine-learning-databricks-on-aws-838aa327.md)
