阶梯网络与多任务学习对台湾地区能量分解的实证研究

Fang-Yi Chang, Chun-An Chen, Shou-De Lin
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引用次数: 4

摘要

能量分解是一种从汇总电表估算单个电器用电量的技术。本文研究了阶梯网络[6]和基于自编码器结构的能量分解多任务学习。这种自编码器架构是由kelly和Knottenbelt在他们最近的研究工作中提出的[1]。我们将这种自编码器架构应用于台湾的高拥有率家电,空调、暖瓶器、冰箱、电视和洗衣机,并通过这五种家电评估梯子网络和多任务学习的有效性。实验数据集由信息工业研究所收集。我们期待这个项目能够推动大数据驱动的智慧能源管理在台湾的产业发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Empirical Study of Ladder Network and Multitask Learning on Energy Disaggregation in Taiwan
Energy disaggregation is a technique of estimation electricity consumption of individual appliance from an aggre-gated meter. In this paper, we study ladder network [6] and multitask learning on energy disaggregation using auto-encoder architecture. This auto-encoder architecture was proposed fromKelly and Knottenbelt in their recent research work [1]. We used this auto-encoder architecture to the high-ownership appliances, air conditioner, bottle warmer, fridge, television and washing machine, in Taiwan and evaluated the effectiveness of the ladder network and multitask learning via these five appliances. The experimental data set has collected by Institute For InformationIndustry. We expect that this project can promote the industrial development of big data-driven smart energy management inTaiwan.
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