Machine learning for end-use electrical energy monitoring

Abstract

Promoting end-users awareness of their usage and consumption of energy is one of the main measures towards achieving energy efficiency in buildings, which is one of the main targets in climate-aware energy transition programs. End-use energy disaggregation and monitoring is a practical and efficient approach towards achieving the targeted awareness of energy users by providing them with real-time fine-grained feedback about their own usage of energy. In this work, we address the case of electrical energy and the problem of end-use load monitoring and disaggregation in a variety of machine learning paradigms. This work starts from unsupervised energy disaggregation based on simple constraints and assumptions without the need for labeled training data. We then study and propose semi-supervised disaggregation approaches that learn from labeled observations, but are also capable of compensating for the scarcity of labeled data by leveraging unlabeled measurements. Finally, we propose a generic neural architecture for data-driven disaggregation upon availability of an abundance of training data. Results from this work not only assert the feasibility of end-use energy disaggregation, but also propose efficient models that adapt to the availability of labeled data, and are capable of monitoring different categories of end-use loads.