用正弦编码提升电力需求预测准确率，效果显著。
Temporal Encoding Strategies for Energy Time Series Prediction
- 对时间序列中的周期性特征使用正弦编码，捕捉波动规律。
- RMSE降低12.6%（0.5497→0.4802），R²提升7.8%（0.7530→0.8118）。
- 适合智能电网实时预测，兼顾精度与计算效率。
在现代电力系统中，能源消耗预测对于维持电网稳定和资源分配至关重要，有助于减少能源浪费并避免电网过载。尽管已有诸多能源优化研究，但往往未能充分应对实时波动和能源消耗的周期性特征。本文提出一种新方法，通过在时间序列数据的周期性特征上应用正弦编码，提升预测模型的准确性。在能源需求数据集上，对多种统计与集成机器学习模型进行训练，并与传统编码方法对比。结果表明，使用该编码后，均方根误差（RMSE）从0.5497降至0.4802（改善12.6%），决定系数R²从0.7530升至0.8118（提升7.8%），说明该方法更有效捕捉时间模式的周期性。所提方法显著提升预测精度且保持计算高效，适用于智能电网的实时应用。
原文摘要 · Abstract (English)
In contemporary power systems, energy consumption prediction plays a crucial role in maintaining grid stability and resource allocation enabling power companies to minimize energy waste and avoid overloading the grid. While there are several research works on energy optimization, they often fail to address the complexities of real-time fluctuations and the cyclic pattern of energy consumption. This work proposes a novel approach to enhance the accuracy of predictive models by employing sinusoidal encoding on periodic features of time-series data. To demonstrate the increase in performance, several statistical and ensemble machine learning models were trained on an energy demand dataset, using the proposed sinusoidal encoding. The performance of these models was then benchmarked against identical models trained on traditional encoding methods. The results demonstrated a 12.6% improvement of Root Mean Squared Error (from 0.5497 to 0.4802) and a 7.8% increase in the R^2 score (from 0.7530 to 0.8118), indicating that the proposed encoding better captures the cyclic nature of temporal patterns than traditional methods. The proposed methodology significantly improves prediction accuracy while maintaining computational efficiency, making it suitable for real-time applications in smart grid systems.
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