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Accurate and reliable energy forecasting is essential for power grid operators who strive to minimize extreme forecasting errors that pose significant operational challenges and incur high intra-day trading costs. Incorporating planning information – such as anticipated user behavior, scheduled events or timetables – provides substantial contextual information to enhance forecast accuracy and reduce the occurrence of large forecasting errors. Existing approaches, however, lack the flexibility to effectively integrate both dynamic, forward-looking contextual inputs and historical data. In this work, we conceptualize forecasting as a combined forecasting-regression task, formulated as a sequence-to-sequence prediction problem, and introduce contextually-enhanced transformer models designed to leverage all contextual information effectively. We demonstrate the effectiveness of our approach through a primary case study on nationwide railway energy consumption forecasting, where integrating contextual information into transformer models, particularly timetable data, resulted in a significant average mean absolute error reduction of 26.6%. An auxiliary case study on building energy forecasting, leveraging planned office occupancy data, further illustrates the generalizability of contextually enhanced transformers, showing an average reduction of 56.3% in mean absolute error. Compared to other state-of-the-art methods, our approach consistently outperforms existing models, underscoring the value of context-aware deep learning techniques in energy forecasting applications. • Reframes day-ahead load forecasting as joint forecasting plus regression. • Contextually enhanced transformers fuse historical load with expected future planning. • Railway traction grid: operational planning context cuts MAE by 26.6% vs best baseline. • Building energy: planned occupancy context cuts MAE by 56.3%, showing generalizability. • Planning context reduces large outliers: 87.8% fewer in railway, 93.0% in buildings.