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cnMAE: The Right Metric for Evaluating Solar Forecasts

As renewable penetration rises and forecasting grows more complex, traditional accuracy metrics often fall short. This white paper introduces a clearer, more reliable framework for evaluating forecast performance giving energy professionals a stronger foundation for comparing assets, assessing model quality, and improving decision-making.

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Amperon Team
December 9, 2025
15 min read

What you'll discover in this report

  • Why traditional error metrics can be misleading for renewable forecasting.
  • How capacity-normalized metrics offer clearer performance comparisons.
  • What challenges arise when evaluating accuracy across variable resources.
  • Why better measurement frameworks improve planning, operations, and risk management.

With increasing number of solar photovoltaic power plants coming online, solar forecasting is becoming more important for many players within the power and utility industry. With these growing use cases, it’s just as important to understand how accurate your forecasts are. However, any forecast is going to have a margin of predictive error.

The utility industry has several conventional metrics for assessing predictive error in load forecasting. But none of them are the right fit for the unique characteristics of solar and solar forecasting.

In this paper, we examine:

  • Evaluate several conventional metrics for assessing predictive error in load forecasting.
  • Introduce cnMAE as the better alternative.
  • Provides cnMAE comparisons and seasonal context.

Use cases

Forecast Performance Benchmarking
Compare renewable forecasting performance across assets, seasons, and conditions using a standardized, capacity-normalized metric.
Model Evaluation & Selection
Use accuracy metrics that reflect real operational variability to choose stronger forecasting models and vendors.
Operational Risk Management
Identify when and where forecasting uncertainty may impact trading, dispatch, or grid operations.
Asset Performance Assessment
Detect underperformance, equipment issues, and production anomalies more reliably with normalized accuracy measures.

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