Contextually-enhanced transformers integrating timetable and occupancy data achieve 26.6% and 56.3% average MAE reductions in railway and building energy forecasting respectively, outperforming prior methods.
Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network
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Heterogeneous Graph Attention Networks model intra-domain and inter-domain sensor relationships for short-term state forecasting in multi-domain power systems, outperforming baselines by 35.5% NRMSE on a hydroelectric case study.
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Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models
Contextually-enhanced transformers integrating timetable and occupancy data achieve 26.6% and 56.3% average MAE reductions in railway and building energy forecasting respectively, outperforming prior methods.
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Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study
Heterogeneous Graph Attention Networks model intra-domain and inter-domain sensor relationships for short-term state forecasting in multi-domain power systems, outperforming baselines by 35.5% NRMSE on a hydroelectric case study.