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Conference Paper
Explanation for Building Energy Prediction
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- Authors
- Marie Kim, Jong-Arm Jun, YuJin Song, Cheol Sig Pyo
- Issue Date
- 2020-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1168-1170
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Explainable AI is a technology field that has recently emerged. Currently, deep learning technology shows high accuracy in many areas. However, despite its high accuracy, there are areas where technology cannot be utilized without sufficient explanation of the results. In medicine, credit rating, and criminal fields, it is difficult to apply technology in practice without sufficient explanation of the results. This paper gives an explanation of the results of the energy demand prediction model by using feature importance and attention mechanism. The model used is seq2seq model, and the dataset used the data set generated by energyPlus?꽓 based on the actual data.
- KSP Keywords
- Attention mechanism, Building Energy Prediction, Credit rating, Data sets, High accuracy, Learning Technology, deep learning(DL), energy demand prediction, feature importance, prediction model