Multi-scale energy forecasting in smart households: Leveraging AI and remotely sensed climate data for sustainable energy management

Accurate energy consumption forecasting is essential for optimizing energy use and promoting sustainability in both urban environments and smart households. This study presents an AI-driven multi-scale forecasting framework that integrates global climate and solar energy datasets from NASA and ESA with smart household consumption data. Four models, Support Vector Regression, Extreme Gradient Boosting, Long Short-Term Memory, and a hybrid Support Vector Regression-Multi-Layer Perceptron (SVR-MLP), were evaluated across multiple temporal resolutions (hourly, 3-hourly, 6-hourly, 12-hourly, and daily). The hybrid SVR-MLP consistently outperformed the other approaches, while variations across households revealed distinct consumption behaviors, underscoring the influence of occupant-specific factors on forecasting accuracy. By integrating remote sensing data with AI-based models, the proposed framework offers a flexible and scalable solution for energy monitoring and optimization, supporting eco-efficiency and data-driven sustainable energy management.