A new paper benchmarks nine deep learning models for forecasting energy consumption using smart meter data. The study found that while extending historical input improves accuracy up to a point, prediction horizon significantly degrades performance. Lightweight deep learning architectures offer comparable accuracy to more complex models at a lower computational cost, with architectural differences becoming more pronounced for longer forecasting horizons and heterogeneous datasets. AI
IMPACT Provides insights into optimal deep learning architectures and data handling for energy consumption forecasting, potentially guiding future research and application in smart grid management.
RANK_REASON The cluster contains an academic paper detailing a benchmark of deep learning models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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- computational complexity
- deep learning
- energy consumption
- multilayer perceptron
- power engineering
- Smart Meters Act 2018
- Time Series Forecasting
- Transformer++
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