论文标题
使用双向顺序模型和小型数据集的功能工程进行短期负载预测
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets
论文作者
论文摘要
电力负载预测使网格操作员能够最佳实施智能电网的最重要功能，例如需求响应和能源效率。在昼夜，季节性和年度规模上，从一个地区到另一个地区的电力需求概况可能会大不相同。因此，要设计一种可以在培训数据有限的情况下，可以在不同数据集上产生最佳估计的负载预测技术，这是一个巨大的挑战。本文为基于双向顺序模型的短期负载预测提供了深入的学习体系结构，并与功能工程结合使用，该模型提取了手工制作的派生功能，以帮助该模型更好地学习和预测。在提出的架构中，称为“深度衍生功能融合”（DeepDeff），将原始输入和手工制作的功能在单独的级别上进行训练，然后将它们各自的输出组合在一起以进行最终预测。在五个具有完全不同模式的国家的数据集上评估了所提出方法的功效。结果表明，所提出的技术优于现有的艺术状态。
Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency. Electricity demand profiles can vary drastically from one region to another on diurnal, seasonal and yearly scale. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge. This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models in conjunction with feature engineering that extracts the hand-crafted derived features in order to aid the model for better learning and predictions. In the proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.
