Energy consumption forecasting with deep learning

Abstract

This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption. It leverages a multidimensional time-series dataset encompassing energy consumption profiles, customer characteristics, and meteorological information. A comprehensive exploration of diverse deep learning architectures is conducted, encompassing variations of recurrent neural networks (RNNs), temporal convolutional networks (TCNs), and traditional autoregressive moving average models (ARIMA) for reference purposes. The empirical findings underscore the substantial enhancement in forecasting accuracy attributed to the inclusion of meteorological data, with the most favorable outcomes being attained through the application of time-series convolutional networks. Additionally, an in-depth investigation is conducted into the impact of input duration and prediction steps on model performance, emphasizing the pivotal role of selecting an optimal duration and number of steps to augment predictive precision. In summation, this investigation underscores the latent potential of deep learning in the domain of electricity consumption forecasting, presenting pragmatic methodologies and recommendations for household electricity consumption prediction.

Machine Learning for Pedestrian-Level Wind Comfort Analysis

“…In addition, machine learning and artificial intelligence (ML/AI)-based methods are extremely efficient and have been widely utilized in multiple disciplines to improve various aspects of daily life, for example, optimizing signal transmission in wireless networks through enhancing the efficiency of text categorization techniques or directing signals based on user categorization. Additionally, such approaches allow for the forecasting of energy efficiency and energy consumption, specifically in relation to wind energy [37][38][39][40][41][42]. They are economical to implement and do not require any specialized expertise.…”