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洞見 - Demand Response Modeling
Modeling Irrational Behavior of Residential End Users Using Non-Stationary Gaussian Processes in Demand Response for Community Battery Storage
Incorporating irrational behavior in demand response models improves revenue and efficiency in community battery storage.
Estimating Unobservable Components of Electricity Demand Response Using Inverse Optimization with Real-World Data Validation
Inverse optimization can effectively estimate hidden consumer energy flexibility behaviors (like shifting and shedding load) using only net demand data, outperforming traditional methods in forecasting and providing valuable insights for system operators and retailers.
