Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

Non-intrusive Appliance Load Monitoring (NALM) is an essential technique that enables the tracking of household appliances’ electricity usage, promotes eco-friendly practices, and reduces energy monitoring costs. However, NALM implementation can be challenging due to privacy concerns, particularly in real-world applications. Federated learning is a promising solution that enables load monitoring model training and sharing while ensuring data security. Nevertheless, federated learning for NALM still faces various challenges, such as limited resources, edge model personalization, and scarcity of training data. This paper proposes a practical federated learning framework for NALM that addresses these challenges. Our approach utilizes collaborative data aggregation over federated learning, cloud-based model compression through filter pruning, and personalized edge and multi-task learning model building with unsupervised transfer learning techniques. Our experimental results, conducted using real energy data, demonstrate that our proposed load monitoring model achieves highly accurate personalized energy disaggregation, making it a state-of-the-art approach for non-intrusive appliance load monitoring at the edge client. Using our federated learning-based load monitoring model, we can minimize energy consumption while maintaining high learning performance and preserving user privacy. Future research could focus on further development and research to improve the efficiency of federated learning implementation and address remaining challenges in real-world NALM applications.

Non-intrusive Appliance Load Monitoring (NALM) is an essential technique that enables tracking of household appliances' electricity usage, promoting eco-friendly practices and reducing energy monitoring costs. However, NALM implementation can be challenging due to privacy concerns, particularly in real-world applications. Federated learning is a promising solution that enables load monitoring model training and sharing while ensuring data security. Nevertheless, federated learning for NALM still faces various challenges, such as limited resources, edge model personalization, and scarcity of training data. In this paper, we propose a practical federated learning framework for NALM that addresses these challenges. Our approach utilizes collaborative data aggregation over federated learning, cloud-based model compression through filter pruning, and personalized edge and multi-task learning model building with unsupervised transfer learning techniques. Our experimental results, conducted using real energy data, demonstrate that our proposed load monitoring model achieves highly accurate personalized energy disaggregation, making it a state-of-the-art approach for non-intrusive appliance load monitoring at the edge client. By using our federated learning-based load monitoring model, we can minimize energy consumption while maintaining high learning performance and preserving user privacy. Future research could focus on further development and research to improve the efficiency of federated learning implementation and address remaining challenges in real-world NALM applications.