ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Conference Paper Sequence-to-Sequence model for Building Energy Consumption Prediction
Cited 7 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Marie Kim, JongAm Jun, Nasoo Kim, YuJin Song, Cheol Sik Pyo
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1243-1245
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539597
Abstract
Gartner predicts that by 2022, 25% of utilities globally will use AI-augmented digital customer service agents to interact with customers' virtual personal assistants and home IoT (Internet of Things). Future power grids' important feature is the ability to predict the energy consumption over a different range of time spans. Temporal energy consumption prediction enables building managers to plan out the energy provision over time. Prediction makes it possible to shift energy use to off-peak periods, and makes more profitable energy purchase plans. But, building energy consumption prediction is a complex task because of many affecting factors, such as climate change trend, occupants' behaviors, and characteristics of thermal systems. In this paper, we developed a deep running model called Sequence-to-Sequence (seq2seq) model for time series prediction of energy consumption. The evaluation used the actual sensed data for three months.
KSP Keywords
Affecting factors, Building energy consumption, Change trend, Climate Change, Customer Service, Energy Consumption Prediction, Energy use, Home IoT, Internet of thing(IoT), Over time, Sequence-To-sequence model