An Efficient Deep Learning Approach For Predicting Household Energy Load In Smart Energy Systems

Abstract

Accurate prediction of household energy consumption is vital for the efficient operation of smart energy systems, enabling demand-side management, cost optimization, and grid stability. This paper presents an efficient deep learning-based approach for forecasting household energy load by leveraging the temporal and nonlinear patterns inherent in smart meter data. The proposed framework incorporates advanced deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to model short-term and long-term dependencies in energy usage. Key external factors including weather conditions, time of day, and occupancy patterns are integrated to enhance prediction accuracy. The model is trained and evaluated using real-world datasets, with performance measured against traditional and machine learning baselines. Results demonstrate significant improvements in forecast accuracy and computational efficiency, making the approach highly suitable for real-time smart home energy management applications. This research contributes toward intelligent energy systems by supporting proactive load balancing and sustainable energy consumption.