Leveraging Deep Neural Networks for Accurate and Robust Residential Energy Demand Forecasting

Abstract

Accurate and robust forecasting of residential energy demand is of paramount importance for efficient energy grid management, effective demand-side response strategies, and sustainable energy planning. Traditional statistical and machine learning models have shown limitations in capturing the complex, nonlinear, and dynamic relationships inherent in residential energy consumption patterns. This research investigates the application of deep neural networks (DNNs) to address the challenges of residential energy demand forecasting. We propose a comprehensive DNN-based framework that leverages a multi-layered architecture to learn intricate features from a diverse set of input variables, including household characteristics, weather data, and temporal information. The model is trained and evaluated on a large-scale dataset collected from residential households, covering multiple geographic regions and time periods. Our results demonstrate that the DNN model significantly outperforms conventional forecasting approaches, such as linear regression, decision trees, and shallow neural networks, in terms of accuracy, robustness, and generalization capabilities. The DNN model achieves up to 25% improvement in forecasting accuracy compared to benchmark methods, while also exhibiting greater resilience to missing data and changes in input distributions. Furthermore, we conduct in-depth analyses to understand the key drivers of residential energy demand and the relative importance of different input features. The findings provide valuable insights for energy policymakers, utility companies, and homeowners to develop targeted strategies for energy conservation and demand-side management. This research advances the state-of-the-art in residential energy demand forecasting by leveraging the powerful representational learning capabilities of deep neural networks. The proposed framework can be readily adapted and deployed in real-world applications, contributing to the optimization of energy systems and the promotion of sustainable energy practices.