An Empirical Study of Ladder Network and Multitask Learning on Energy Disaggregation in Taiwan
Journal
Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
Pages
86-89
Date Issued
2018
Author(s)
Abstract
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. © 2018 IEEE.
Other Subjects
Air conditioning; Artificial intelligence; Bottles; Electric power measurement; Energy conservation; Ladder networks; Ladders; Network architecture; Project management; Signal encoding; Smart meters; Television networks; Disaggregation; Electricity service; Electricity-consumption; Empirical studies; Industrial development; Multitask learning; Recent researches; Semi-supervised; Deep learning
Type
conference paper