arXiv AI
From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations
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A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
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LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
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Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting
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arXiv Machine Learning
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Benchmarking Time Series Foundation Models for Load Forecasting Under Covariate Uncertainty
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Hugging Face Trending Papers
Aug 19
An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting
The paper presents an empirical benchmark of nine deep learning models for smart meter energy forecasting, evaluating them on two public datasets. It examines how historical input length, prediction horizon, and model architecture affect accuracy, finding that longer historical context improves performance up to a saturation point while accuracy declines with longer horizons. The study also compares computational cost, showing lightweight models achieve similar accuracy to heavier ones, and notes that model choice matters less across most population segments.