A Deep Learning-Based Framework for Smart Home Energy Consumption Forecasting

Abstract

The global transition toward sustainable energy systems and the proliferation of smart home technologies have made efficient energy management a critical objective. Central to this goal is the accurate forecasting of residential energy consumption, which empowers both consumers and utilities to optimize energy use, reduce costs, and enhance grid stability. This paper proposes a comprehensive, deep learning-based framework for energy consumption forecasting in smart homes, designed to overcome the inherent challenges of highly variable, non-linear, and privacy-sensitive data. The methodology involves a comparative analysis of established forecasting models, including traditional statistical methods like ARIMA and cutting-edge deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Network-LSTM (CNN-LSTM) hybrids. The framework incorporates a robust data pipeline, encompassing meticulous data preprocessing, domain-specific feature engineering, and rigorous model evaluation using a multi-metric approach. The analysis synthesizes findings from recent studies, demonstrating that advanced deep learning models consistently outperform traditional methods by effectively capturing complex temporal patterns and dependencies in energy usage data. The report also provides a nuanced examination of the trade-offs between model complexity, computational demands, and predictive accuracy, revealing that simpler models, such as K-Nearest Neighbors (KNN), can be a competitive and robust alternative in data-scarce environments. This research underscores the vital role of predictive analytics in enabling dynamic demand-side management and ensuring the effective integration of renewable energy sources. It also highlights the growing importance of privacy-preserving techniques like Federated Learning to foster a more secure and sustainable energy future.