论文标题
基于混合策略的短期载荷预测，使用温暖启动梯度树的增强
Short-term Load Forecasting Based on Hybrid Strategy Using Warm-start Gradient Tree Boosting
论文作者
论文摘要
提出了一种基于深度学习的混合策略，用于短期负载预测。该策略提出了一种新型的基于树的合奏方法温暖启动梯度树的增强（WGTB）。当前的策略要么是单一类型的集成式代码，因此无法利用不同推理模型的统计优势。或者，他们只是简单地总结了完全不同的推理模型的输出，这不会最大程度地发挥合奏的潜力。受到偏见变化权衡的启发，WGTB提出并根据不同的推断模型在准确性，波动性和线性性方面的巨大差异进行量身定制。完整的策略整合了不同能力的四个不同推论模型。然后，WGTB通过温暖的启动和装袋和提升的混合物组合其输出，从而同时降低偏见和差异。它在中国国家网格公司的两个实际数据集中通过小时解决方案进行了验证。结果证明了提出的策略的有效性，该策略杂交了低偏置和低变义推断模型的统计强度。
A deep-learning-based hybrid strategy for short-term load forecasting is presented. The strategy proposes a novel tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of the statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. Inspired by the bias-variance trade-off, WGTB is proposed and tailored to the great disparity among different inference models on accuracy, volatility and linearity. The complete strategy integrates four different inference models of different capacities. WGTB then ensembles their outputs by a warm-start and a hybrid of bagging and boosting, which lowers bias and variance concurrently. It is validated on two real datasets from State Grid Corporation of China of hourly resolution. The result demonstrates the effectiveness of the proposed strategy that hybridizes the statistical strengths of both low-bias and low-variance inference models.
