Researchers have conducted an empirical benchmark of nine deep learning models for smart meter energy forecasting, evaluating their performance on two public datasets. The study found that while extending historical input data improves accuracy up to a certain point, accuracy decreases with longer prediction horizons. Deep learning models generally outperformed classical methods, with lightweight architectures offering comparable performance at a lower computational cost. The effectiveness of architectural differences was more pronounced with longer forecasting horizons and on more varied datasets. AI
IMPACT Provides insights into optimal model architectures and data usage for energy forecasting, potentially improving efficiency and cost management in power systems.
RANK_REASON The cluster describes an academic paper presenting an empirical benchmark of deep learning models for a specific task (energy forecasting).
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- computational complexity
- deep learning
- energy consumption
- multilayer perceptron
- power engineering
- Smart Meters Act 2018
- Time Series Forecasting
- Transformer++
- alphaXiv
- arXiv
- Behnaz Kavoosighafi
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
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