A Deep Learning Framework for Modeling Temporal Dependencies and Hierarchies in Hourly Electricity Demand Load

Abstract

The limitations of traditional deep learning models in processing vast volumes of data and modelling complicated temporal dependencies make it difficult to effectively satisfy these objectives for short-term load forecasting (STLF). This chapter utilises deep learning, which enables the following: k-means clustering to comprehend hourly electricity demand load trend, extraction of complex features with non-linear interactions that impact electricity demand load, handling of long-term dependencies through the modelling of temporal hierarchies in the time series data via long short-term memory, and recurrent neural network to capture dependencies across time steps. The performance of the state-of-the-art hourly demand forecast models with the k-means variant is compared with that of the RNN-LSTM-CNN-copula. Furthermore, it is noted that RNN-LSTM-CNN-copula is a viable deep learning model for energy consumption forecast problems because of its capacity to learn the spatio-temporal dependencies in the hourly electrical demand load data, which results in an accurate energy demand forecast.