Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space

Recently, many studies have exploited the potential of deep learning to forecast energy demand, but they cannot explain the results. They only analyze the simple correlations between the input and output to discover the most important input features, or they depend on the manual investigation of the latent space embedded with power demand patterns. In this paper, to overcome these shortcomings, we propose a deep autoencoder that can explain the prediction results by manipulating the latent space. It consists of 1) a power encoder that embeds power information, 2) an auxiliary encoder that embeds auxiliary information for an interpretable latent space in two dimensions, 3) a predictor that predicts power demand by using concatenated values of the latent variables extracted from the two encoders, and 4) an explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables. Several experiments on a dataset of household electric energy demand show that the proposed model not only performs better than conventional models, with a mean squared error of 0.376 in predicting electricity demand for 60 min, but also provides the capacity to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted.