Deep Learning Applications for Residential Energy Demand Forecasting

Abstract

The global paradigm shift toward energy efficiency and sustainable living necessitates innovative approaches to energy management, particularly within residential buildings which contribute substantially to overall energy consumption. This study unveils a cutting-edge methodology employing deep learning models to predict residential energy demand with remarkable accuracy. Through the application of advanced architectures such as Recurrent Neural Networks and Long Short-Term Memory networks, the research harnesses the power of extensive datasets, extracting patterns pivotal for energy forecasting. The process entails meticulous data preparation, involving cleaning, feature engineering, and normalization, thus creating a robust model that accurately captures the intricate dynamics of energy use. The effectiveness of the deep learning approach is evidenced by its substantial performance metrics. It exhibits the potential to aid homeowners and policy makers in making informed decisions that lead to energy conservation and cost savings. While the findings are promising, the study acknowledges ongoing challenges and sets a future research agenda that includes scaling models to larger datasets, integrating renewable energy forecasting, and addressing data privacy concerns, ultimately advancing smart and sustainable energy systems.