An Effective Prediction of Power Consumption Using Deep Learning Models

Abstract

Effective prediction of electric power consumption is vital for accurate energy management systems, particularly in growing urban regions. Conventional statistical techniques usually fail to capture the complicated patterns and non-linear tendencies in time-series energy data. The development of deep learning models has demonstrated encouraging findings in a variety of prediction tasks. Therefore, this paper concentrates on the application of diverse deep learning models, comprising One-dimensional Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and a hybrid model for power consumption prediction based on a dataset gathered from January 1, 2017, until January 1, 2018, in Tetouan, Morocco. These applied models were assessed utilizing diverse robust assessment metrics, and the findings demonstrated that the hybrid model (One-dimensional CNN with LSTM) reached superior performance with a Mean Squared Error (MSE) of 0.42548, Root-MSE of 0.65228, Mean Absolute Error (MAE) of 0.54395, and Median-AE of 0.51401, surpassing the other standalone and relevant models. The attained findings emphasize the merits of incorporating recurrent and convolutional structures to obtain more effective and accurate predictions of energy consumption time series