DelayNet: Enhancing Temporal Feature Extraction for Electronic Consumption Forecasting with Delayed Dilated Convolution
In the face of increasing irregular temperature patterns and climate shifts, the need for
accurate power consumption prediction is becoming increasingly important to ensure a …
accurate power consumption prediction is becoming increasingly important to ensure a …
Partial Transfer Learning from Patch Transformer to Variate-Based Linear Forecasting Model.
Transformer-based time series forecasting models use patch tokens for temporal patterns
and variate tokens to learn covariates' dependencies. While patch tokens inherently facilitate …
and variate tokens to learn covariates' dependencies. While patch tokens inherently facilitate …
ChronoPatternNet: Revolutionizing Electricity Consumption Forecasting with Advanced Temporal Pattern Recognition and Efficient Computational Design
SC Lee, GH Yu, JY Kim - 디지털콘텐츠학회논문지, 2024 - dbpia.co.kr
ChronoPatternNet revolutionizes power forecasting using a unique 2D convolutional
approach for advanced temporal pattern recognition. The'chronocycle'hyperparameter …
approach for advanced temporal pattern recognition. The'chronocycle'hyperparameter …
Electricity consumption forecasting with Transformer models
A Gravrok - 2023 - ntnuopen.ntnu.no
Denne studien presenterer en omfattende evaluering av forskjellige maskinlærings-og dyp
læringsmodeller, med et spesielt fokus på Transformer-modeller, for korttidsprognoser for …
læringsmodeller, med et spesielt fokus på Transformer-modeller, for korttidsprognoser for …