Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space
作者：
Highlights：
• We propose a novel deep learning model to stably predict electric energy consumption.
• The model can explain the prediction results by exploiting the latent space.
• We analyze the model with the large data collected in an actual residential house.
• We achieve the highest performance in high resolution compared with the previous works.
• We explain the variables of appliances that influence the prediction performance.
摘要
•We propose a novel deep learning model to stably predict electric energy consumption.•The model can explain the prediction results by exploiting the latent space.•We analyze the model with the large data collected in an actual residential house.•We achieve the highest performance in high resolution compared with the previous works.•We explain the variables of appliances that influence the prediction performance.
论文关键词：Deep learning,Autoencoder,Explainability,Latent space,Energy demand prediction,Time-series modeling
论文评审过程：Received 24 May 2020, Revised 30 June 2021, Accepted 30 August 2021, Available online 2 September 2021, Version of Record 6 September 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115842
