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Conference Paper
Sequence-to-Sequence model for Building Energy Consumption Prediction
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- 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
- 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