Electricity Consumption Prediction via WaveNet+t

Abstract

Electricity consumption prediction is essential for load management to prevent shortage and excess supply. Different methods ranging from statistical methods, machine learning, and deep learning models were developed to predict electricity consumption. In this study, a probabilistic model -WaveNet+t was developed to provide the confidence interval rather than the deterministic estimate. WaveNet+t model integrates dilated causal convolutional neural networks with residual networks to extract the temporal, long/short term patterns from the time series data. The testing results based on a real dataset from 370 clients showed that WaveNet+t model has a lower CRPSs1״״ value than the benchmark models.