In today's rapidly evolving energy market landscape, the accuracy of demand forecasting has never been more critical. With market participants facing unprecedented challenges from new regulatory policies, volatile economic conditions, and increasingly unpredictable weather patterns, the need for precise forecasting tools has reached new heights.

As we move deeper into the volatile summer season, Amperon is excited to share significant advancements in demand forecasting methodologies that are helping customers manage load risk and optimize wholesale participation with greater confidence.

Continuing Our Commitment to Excellence

At Amperon, our forecasting philosophy has always been guided by five core principles. An industry-leading forecast should:

1. Provide leading forecast accuracy which minimizes errors across every hour of the forecast

2. Deliver accurate predictions that do not overfit on historic data

3. Leverage best-in-class weather and market data

4. Provide interpretable and explainable forecasting results

5. Scale with market dynamics and customer demands by seamlessly integrating updated data, new data sets, and modeling techniques

Building on these principles, we've recently invested deeply in advancing our modeling techniques by implementing two improvements to our approach: meta-learning ML model and model de-biasing.

Meta-learning: A New Frontier in ML Model Optimization

One of the most exciting advancements in our forecasting toolkit is the introduction of a novel meta-learning model, which represents a significant evolution in how we derive our final weighted load predictions.  

How it works: Amperon previously deployed a dynamic weighting methodology that assigned weights to individual ML models within our hybrid regression/ML stack, based on performance over a 1–2 month historical window. While this approach was both innovative and highly accurate, Amperon has continued to advance our ML capabilities by introducing a next-generation meta-learning model. The meta-learning model is itself a machine learning model that:

• Learns from the historical performance of individual ML models (boosted trees, linear regression, etc.)

• Analyzes at least a year of past data to understand model behavior under different conditions (seasonality, weather patterns, holidays)

• Produces a direct final forecast prediction rather than simply outputting better weights

The impact: The meta-learning model leverages at least a full year of historical data, allowing it to recognize and calibrate to seasonal variations, weather anomalies, and market changes that occur across longer timeframes.  

Model De-biasing: Correcting Systematic Errors

Our data science team has developed and implemented sophisticated de-biasing techniques that significantly enhance forecast accuracy by addressing and correcting systematic errors in model predictions.

How it works: The de-biasing process analyzes recent forecast errors for specific times of the day, identifying patterns that might be missed by traditional approaches. Each model's predictions are then adjusted by a calculated de-biasing value, with the correction and updated forecast optimized using historical performance data.

The impact: This enhancement has led to measurable accuracy gains across multiple regions, particularly during challenging forecasting periods like extreme weather events and demand peaks. By systematically addressing bias in our forecasting models, we're providing customers with a more reliable foundation for their operational and financial decision-making.

Our Accuracy Improvements and Back Test Results

Based on our extensive back testing, we’re proud to share the model accuracy we can offer customers now with these improvements:  

These results demonstrate industry-leading accuracy due to more adaptive and responsive calibrations of the final forecast prediction, especially:

• Across our North American ISOs and many European TSOs

• During extreme weather events, such as the recent cold snap, and

• During unique characteristics of different time periods, such as seasonal weather patterns.

Benefits for Market Participants

These improvements translate into tangible benefits across all our customer segments:

• Financial Traders: The enhanced accuracy of our Grid Demand forecast helps optimize positions between day-ahead and real-time activity, creating opportunities for more profitable trading strategies.

• Load Serving Entities: More accurate Meter Demand and Portfolio Demand forecasts mean better load risk management, helping utilities and retailers make informed decisions about short-term positions.

• Demand Management: Predicting high-demand hours with greater precision enables more effective deployment of demand response programs, whether for Coincident Peak strategies or day-to-day demand spike management.

Looking Ahead

As the energy transition continues to accelerate, Amperon remains committed to pushing the boundaries of what's possible in energy forecasting. Our data science and engineering teams continue to research and develop new techniques that will further enhance our forecasting capabilities.

Experience the difference for yourself. Connect with us to see the power of Amperon’s forecasting.  

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