Energy Prediction in Urban Areas Using Machine Learning and Deep Learning
Pradeep Kumar Kushwaha, Ajay Rana, Faiz Hassan, Sourav Singh Hada, Garima Bhardwaj, Vijay Bhutani
Abstract
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.