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학술대회 Explanation for Building Energy Prediction
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저자
김말희, 전종암, 송유진, 표철식
발행일
202010
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1168-1170
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289340
초록
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.
키워드
building energy consumption prediction, explanation, seq2seq model
KSP 제안 키워드
Attention mechanism, Building energy consumption, Building energy prediction, Credit rating, Data sets, Energy Consumption Prediction, Feature Importance, High accuracy, deep learning(DL), energy demand prediction, learning technology