Adaptive Deep Learning Strategies for Real-time Residential Energy Demand Forecasting and Optimization

Abstract

Accurate and timely forecasting of residential energy demand is crucial for efficient energy management and grid stability in smart cities. Traditional forecasting methods often struggle to capture the complex, dynamic, and nonlinear relationships between various factors influencing residential energy consumption. The rise of deep learning techniques has enabled more powerful and adaptive models for energy demand forecasting. This research investigates the development and application of novel adaptive deep learning strategies for real-time residential energy demand forecasting and optimization. Three key contributions are made: 1) A hybrid deep learning framework that integrates recurrent neural networks, convolutional neural networks, and attention mechanisms to capture temporal, spatial, and contextual dependencies in residential energy demand data; 2) An online learning approach that continuously updates the deep learning models with new data to adapt to changes in consumer behavior and environmental conditions; 3) A multi-objective optimization model that leverages the forecasting outputs to optimize residential energy scheduling and distribution for cost savings, peak load reduction, and emissions minimization. The proposed methods are evaluated using high-resolution smart meter data from residential households. The results demonstrate significant improvements in short-term and medium-term forecasting accuracy compared to benchmark models. Optimized energy scheduling is shown to reduce peak demand by up to 18% and electricity costs by 12% for individual households. This research advances the state-of-the-art in adaptive deep learning for smart grid applications and provides a framework for intelligent residential energy management.