Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patterns. While deep learning models such as LSTMs and Transformers show promising performance, most existing studies focus on direct absolute load prediction without explicitly addressing target non-stationarity. Motivated by classical time-series differencing techniques in ARIMA models, this paper investigates a delta-based target reformulation for short-term electricity load forecasting using deep learning. Instead of directly predicting absolute load values, the proposed formulation trains models to predict the change in load between consecutive time steps, with final forecasts reconstructed using the last observed load. This aims to stabilize the learning target and reduce forecasting difficulty. Using multi-year, hourly real-world electricity load data from India, augmented with meteorological variables from the NASA POWER project and calendar features, this study evaluates LSTM and Transformer models under both formulations, benchmarking them against LightGBM. Experiments are conducted for hour-ahead and day-ahead horizons, assessing performance via Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results show that delta-based reformulation consistently improves forecasting accuracy for hour-ahead prediction across all evaluated models, yielding MAPE reductions of over 50% compared to absolute formulations. For day-ahead forecasting, delta targets specifically benefit deep sequence models (LSTM and Transformer), while LightGBM remains competitive under the absolute formulation. These findings indicate that while delta reformulation is a powerful inductive bias for neural networks, its efficacy is model- and horizon-dependent.

翻译：准确的短期电力负荷预测对于现代电力系统在天气变化、日历效应及消费模式演变等非平稳条件下的可靠和经济运行至关重要。尽管LSTM和Transformer等深度学习模型展现出良好性能，但现有研究大多聚焦于直接预测绝对负荷值，而未明确处理目标变量的非平稳性。受ARIMA模型中经典时间序列差分技术的启发，本文提出了一种基于增量的目标重构方法，用于深度学习驱动的短期电力负荷预测。该公式不直接预测绝对负荷值，而是训练模型预测连续时间步之间的负荷变化，最终预测结果通过最后一次观测的负荷重构得到。这旨在稳定学习目标并降低预测难度。本研究利用印度多年度、小时级真实电力负荷数据，结合NASA POWER项目的气象变量和日历特征，在两种公式下对LSTM和Transformer模型进行评估，并以LightGBM作为基准。实验针对小时前和日前预测时间跨度展开，采用平均绝对误差（MAE）和平均绝对百分比误差（MAPE）评估性能。结果表明：基于增量的重构方法在所有评估模型的小时前预测中均持续提高准确性，与绝对公式相比，MAPE降低超过50%。在日前预测中，增量目标专门提升深度序列模型（LSTM和Transformer）性能，而LightGBM在绝对公式下仍保持竞争力。这些发现表明，增量重构虽为神经网络提供有效的归纳偏置，但其效果取决于模型类型与预测时间跨度。