A Hybrid Mode of Sequence Prediction Based on Generative Adversarial Network
| Autor: | WenXuan Huang, Heng Luo, TingFei Zhang, Hang Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Test data generation business.industry 020209 energy Mode (statistics) 02 engineering and technology Energy consumption 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Data modeling Sequence prediction 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Generative adversarial network computer 0105 earth and related environmental sciences |
| Zdroj: | 2020 IEEE 6th International Conference on Computer and Communications (ICCC). |
| DOI: | 10.1109/iccc51575.2020.9344941 |
| Popis: | Human beings nowadays spend more than 90% of the lifetime indoors, leading to the dramatic increase of energy consumption in various buildings. Therefore, research regarding the environment friendly building becomes much more popular recently in which the prediction of energy consumption is a promised method. Nevertheless, the accuracy of prediction is not sound due to insufficient samples. A novel data generation model, termed HMSP, based on the generative adversarial networks, is proposed in this paper to generate much more data robustly, depending on a small number of samples available. The prediction CV-RMSE results, adopting data from the hybrid model, reach 3.03% at best and 7.99% at worst respectively compared to the samples recorded. |
| Databáze: | OpenAIRE |
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