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Smart Grids

Demand Forecasting: Predicting the Future of Energy Use

Master the art and science of demand forecasting, the critical foundation that enables effective demand response programs and helps utilities balance supply and demand

Why Accurate Forecasting Matters

Demand forecasting is the backbone of effective demand response. Without accurate predictions of future electricity demand, utilities cannot properly plan demand response events or ensure grid reliability.

Forecasting Challenges

  • • Weather variability affects demand patterns
  • • Economic factors influence consumption
  • • Behavioral changes from DR programs
  • • Integration of distributed energy resources
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Predictive Analytics

Forecasting Methods

From simple statistical methods to advanced machine learning algorithms, explore different approaches to predicting electricity demand.

Forecasting Method Comparison

Select Forecasting Method

24-Hour Demand Forecast

MAPE: 28.9%
0255075100ForecastActual

Simple Average

Uses average of past values

Accuracy
75%
Complexity
Low
MAPE
28.9%

Economic Impact of Forecast Accuracy

See how forecasting accuracy directly affects the financial performance and effectiveness of demand response programs.

Forecast Impact Calculator

See how forecasting accuracy affects the economic value and effectiveness of demand response programs.

85%
50%

Annual Impact

Effective DR Capacity85 MW
Total Load Reduction15,513 MWh
Over-forecast Cost$548
Under-forecast Cost$1,232

Net Annual Benefit$773,846
* Assumes $50/MW benefit for load reduction and penalties for forecast errors

Forecasting Best Practices

Data Quality

  • • High-resolution smart meter data
  • • Weather and temperature data
  • • Economic indicators and trends
  • • Calendar and holiday information

Model Validation

  • • Cross-validation techniques
  • • Out-of-sample testing
  • • Rolling forecast evaluation
  • • Continuous model improvement

Uncertainty Quantification

  • • Prediction intervals
  • • Ensemble forecasting methods
  • • Scenario-based planning
  • • Risk-adjusted decision making

Continuous Improvement

  • • Regular model retraining
  • • Performance monitoring
  • • Stakeholder feedback integration
  • • Technology and methodology updates
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