论文标题
堆叠式助推器网络体系结构，用于建筑物中的短期负载预测
Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings
论文作者
论文摘要
本文介绍了一种新颖的深度学习体系结构，用于对建筑能源负载的短期负载预测。该体系结构基于一个简单的基础学习者和多个增强系统，这些系统被建模为单个深神经网络。该体系结构将原始的多元时间序列转换为多个级联单变量时间序列。加上稀疏的交互，参数共享和模棱两可的表示，这种方法使得能够与过度拟合，同时仍可以通过深层网络体系结构来实现良好的表现能力。该体系结构是通过芬兰办公大楼的能源数据进行的几个短期负载预测任务评估的。所提出的架构在所有任务中都优于最先进的负载预测模型。
This paper presents a novel deep learning architecture for short term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.
