Energy Prediction in Urban Areas Using Machine Learning and Deep Learning

This research paper proposes a novel approach for predicting energy consumption in a building using advanced machine learning and deep learning techniques. The proposed method considers various factors such as historical energy consumption data, environmental conditions, and occupancy patterns, and utilizes them to train an ML model to predict future energy consumption. Furthermore, a deep learning model is trained to capture the dynamic relationship between these factors and energy consumption, resulting in more accurate predictions. The proposed method is evaluated using real-world energy consumption data, and the results demonstrate its superiority over traditional prediction methods. The proposed approach has the potential to provide building owners, energy managers, and policymakers with more accurate energy consumption predictions, enabling them to optimize building energy use, reduce energy costs, and mitigate the environmental impact of buildings.

I. Introduction

In recent years, there has been an increasing demand for energy -efficient buildings due to the growing concern over climate change and rising energy costs [1]. Machine learning and deep learning algorithms have been shown to provide accurate predictions of energy consumption, allowing building managers to optimize energy usage and reduce costs [2]. These algorithms can analyse large amounts of data, including weather patterns, occupancy, and building characteristics, to identify patterns and make predictions [3]. Additionally, these algorithms can continuously learn and adapt to changes, ensuring that the energy consumption predictions remain accurate over time [4]. With the potential to save both money and energy, the use of machine learning and deep learning algorithms in building management is likely to become increasingly prevalent in the future [5].