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Energy — Predictive Model Training for Energy Consumption

Free

This DAG trains predictive models using energy consumption data to enhance forecasting accuracy. It includes model evaluation and selection processes, ensuring robust performance metrics and recovery mechanisms.

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Overview

The purpose of this DAG is to train predictive models that forecast energy consumption based on historical data. It ingests various data sources, including real-time energy usage logs, weather data, and historical consumption patterns. The ingestion pipeline is designed to handle large volumes of data efficiently, ensuring timely processing for model training. The processing steps include data cleaning, feature engineering, model training, evaluation, and model selection. Quality controls are im

The purpose of this DAG is to train predictive models that forecast energy consumption based on historical data. It ingests various data sources, including real-time energy usage logs, weather data, and historical consumption patterns. The ingestion pipeline is designed to handle large volumes of data efficiently, ensuring timely processing for model training. The processing steps include data cleaning, feature engineering, model training, evaluation, and model selection. Quality controls are implemented at each step, particularly during model evaluation, where key performance indicators (KPIs) such as model accuracy and training duration are monitored. In the event of a training failure, a recovery mechanism triggers a re-training process to ensure model reliability. The outputs of this DAG include trained predictive models, performance reports, and visualizations of model accuracy over time. By leveraging these predictive models, businesses in the energy sector can optimize resource allocation, improve demand forecasting, and enhance overall operational efficiency, leading to significant cost savings and improved service delivery.

Part of the Scientific ML & Discovery solution for the Energy industry.

Use cases

  • Improved forecasting accuracy leads to better resource management
  • Enhanced operational efficiency reduces energy waste
  • Cost savings through optimized energy distribution
  • Informed decision-making based on reliable predictive insights
  • Increased customer satisfaction through better service reliability

Technical Specifications

Inputs

  • • Real-time energy usage logs
  • • Historical energy consumption data
  • • Weather data for predictive modeling
  • • Grid demand data
  • • Customer usage patterns

Outputs

  • • Trained predictive models for energy consumption
  • • Model performance evaluation reports
  • • Visualizations of prediction accuracy
  • • Recommendations for energy management strategies
  • • Alerts for model retraining needs

Processing Steps

  1. 1. Ingest real-time energy usage and historical data
  2. 2. Clean and preprocess the data for analysis
  3. 3. Perform feature engineering to enhance model inputs
  4. 4. Train multiple predictive models using selected algorithms
  5. 5. Evaluate models based on accuracy and performance metrics
  6. 6. Select the best-performing model for deployment
  7. 7. Generate reports and visualizations for stakeholders

Additional Information

DAG ID

WK-0817

Last Updated

2025-06-04

Downloads

5

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