Briefing on day-ahead load forecasting Amber Motley, Manager Short - - PowerPoint PPT Presentation

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Apr 01, 2024 47 likes •137 views

Briefing on day-ahead load forecasting Amber Motley, Manager Short Term Forecasting Board of Governors Meeting General Session November 14, 2018 Load forecast accuracy improved 10% in 2018 CAISO Day Ahead Mean Absolute Percent Error

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Briefing on day-ahead load forecasting

Amber Motley, Manager – Short Term Forecasting Board of Governors Meeting General Session November 14, 2018

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0.0% 0.5% 1.0% 1.5% 2.0% 2.5% Q1 Q2 Q3 Q4

CAISO Day Ahead MAPE

2013 2014 2015 2016 2017

MAPE MAPE

Load forecast accuracy improved 10% in 2018

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0.0% 0.5% 1.0% 1.5% 2.0% 2.5% Q1 Q2 Q3 Q4

CAISO Day Ahead Mean Absolute Percent Error (MAPE)

2013 2014 2015 2016 2017 2018

MAPE MAPE

Forecast improvement observed in Q1 and Q2 2018

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Analysis of load forecast error looked at the following:

Effect of behind the meter resource production
Underlying temperature / weather forecast
Regional forecast granularity / micro climate effects
Machine learning model tuning parameter
Manual adjustments

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Hourly error increases in middle of day due to effects of increasing behind the meter production on mid-day load.

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Average MW behind the meter error varies significantly by Quarter in 2017.

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During Q1, Q2, and Q4 ISO forecasts are driven by cloud cover

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Quarter

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There is a difference between ground measured irradiance and satellite interpolated actuals in 2017.

Quarter

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Conclusion of analysis

Load forecast accuracy is most impacted by cloud cover

variability effects on 7,000MW of behind-the-meter capacity during Q1, Q2, and Q4.

New forecast techniques are needed to support the magnitude

and changes to behind-the-meter resource capacity.

Improved visibility into the actual aggregate behind-the-meter

production is needed to improve calibration of forecast models

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Work plan of next steps

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Task Estimated Schedule Regional Breakout End of 2018 Incorporate Behind the Meter Actuals June 30, 2019 Re-assess BTM Forecast Provider June 30, 2019 Modeling Environment TBD; working with IT Multiple Models TBD; working with IT Appropriate Blending Options TBD; working with Vendor Probabilistic Forecasting TBD working with DOE Sponsored Research Project Develop new forecasting approaches Continuously researching best practices & new techniques