Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
Real- World Energy Data of 200 Feeders from Low-Voltage Grids with Metadata in Germany over Two Years
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Covariate-informed zero-shot time-series foundation models beat task-specifically tuned XGBoost and random forests in aggregate on a 54-dataset energy forecasting benchmark.
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Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
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FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
Covariate-informed zero-shot time-series foundation models beat task-specifically tuned XGBoost and random forests in aggregate on a 54-dataset energy forecasting benchmark.