Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential. However, most existing forecasting approaches formulate the problem purely as a regression task, limiting their ability to explicitly capture stepwise directional movements and trend consistency required for operational decisions. To address this limitation, this paper proposes a trend-aware multi-task forecasting framework that decomposes forecasting outputs into directional movements and deviation magnitudes relative to the latest observation, enabling both accurate numerical prediction and interpretable trend-aware outputs. The framework adopts a task-specific dual-stream architecture and explores key design choices for integrating trend and deviation information, including hard versus probabilistic trend representations, symmetric versus asymmetric deviation modelling, and parallel versus sequential conditioning strategies. To stabilize multi-task learning and reduce manual tuning, an uncertainty-aware task weighting scheme is incorporated to automatically balance directional classification, deviation regression, and final output prediction during training. Experimental results on real-world energy datasets demonstrate that the proposed framework achieves competitive numerical accuracy compared with state-of-the-art algorithms, while consistently improving trend prediction performance with moderate computational cost. This capability is particularly beneficial in short-term energy system management, where consistent directional forecasting can provide more reliable decision support for practical operational scenarios such as market bidding, resource scheduling, and risk-aware energy management.

翻译：短期能源预测在电力市场竞价和电力系统调度等实时运行决策中具有重要作用，此类场景对数值精度与正确的方向性信号均提出要求。然而，现有预测方法大多将该问题单纯视为回归任务，难以显式捕捉运行决策所需的步进式方向变化和趋势一致性。为克服这一局限，本文提出一种趋势感知的多任务预测框架，将预测输出分解为相对于最新观测值的方向变化和偏差幅度，从而兼顾精确数值预测与可解释的趋势感知输出。该框架采用任务特定的双流架构，探索了集成趋势与偏差信息的关键设计选项，包括硬性与概率性趋势表示、对称与非对称偏差建模以及并行与顺序条件策略。为稳定多任务学习并减少人工调参，引入基于不确定性的任务权重方案，在训练过程中自动平衡方向分类、偏差回归与最终输出预测任务。在真实能源数据集上的实验结果表明，与现有先进算法相比，所提框架在保持竞争力数值精度的同时，能以适度计算开销持续提升趋势预测性能。该能力尤其在短期能源系统管理中具有重要价值——对市场竞价、资源调度及风险感知能源管理等实际运行场景，一致性的方向预测可提供更可靠的决策支持。