Forecasting Energy Consumption Using Hybrid CNN and LSTM Auto-Encoder Network with Hyperband Optimization

Abstract

Abstract: Power consumption in the modern world has significantly increased due to the rapid growth of the human population and technological advancement. The precise forecasting of rising electricity usage is a requirement for planning strategies, boosting profits, cutting down on power waste, and ensuring the energy demand management system runs steadily. Modern developments in the area of electricity consumption prediction offer great techniques for capturing chaotic trends in energy consumption and even surpassing forecasting models that have been around for a long time. But to increase prediction accuracy, some significant drawbacks in the current consumption prediction models must be addressed. In this paper, we present a hybrid deep learning design-based Auto-Encoder (AE) network model comprising of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for multi-step forecasting. The input dataset is de-noised within the framework using Fast Fourier Transform (FFT) and outliers are removed using Local Outlier Factor (LOF) algorithm, before learning in an unsupervised manner using the auto-encoder network. And, hyperband optimization is applied for tuning the hyperparameters. The outcomes of the numerical experiments demonstrate that the proposed architecture yields excellent prediction capabilities and great potential for generalization.