Towards Supply-following Loads: Lessons from Wind Prediction

1. Effective supply-following/load-sculpting power power wind Storage ≡ Supply prediction pgrid p′grid ∫ = E ∫ = E time time Implicit storage (thermal mass, deferrable work) Explicit storage (better batteries) ` Towards Supply-following Loads: Lessons from Wind Prediction Mike He, Achintya Madduri, Yanpei Chen, Rean Griffith, Ken Lutz, Seth Sanders, Randy Katz UC Berkeley Overview Prediction horizon matters: Use metrics beyond RMSE and MAE • Integrating renewables into energy portfolios is challenging • High variability • High misprediction rate even with a “good” model • The good news • It’s ok to be wrong • The prediction horizon matters (short vs. longer term) • We can deal with the variability via load-sculpting Figure 1: Wind power trace from a large power plant in the Midwest Goal: Integrating renewables into energy portfolios Load sculpting controllers: Combining predictors & implicit storage • Managing HVAC heating/cooling cycles using optimal control • Use the home thermal model from jronsim (Java Residential Occupied Neighborhood Simulator) • Lookahead controller within 5% of an oracle using a perfect wind predictor Perfect prediction is not a silver bullet. We need storage! Conclusions and future work • Integrating renewables like wind into energy portfolios is challenging • High variability • To evaluate predictors we need to look at additional quality metrics beyond RMSE and MAE • Prediction horizon and prediction error distribution matters • We can deal with the variability using simple predictors and implicit storage Acknowledgments • We would like to thank: • the National Renewable Energy Laboratory (NREL) for providing wind data • Albert Goto (UC Berkeley) for providing our compute infrastructure