Load Monitoring (LM) is a fundamental step to implement sustainable energy conservation. LM includes Intrusive LM (ILM) and Non-Intrusive LM ... Show moreLoad Monitoring (LM) is a fundamental step to implement sustainable energy conservation. LM includes Intrusive LM (ILM) and Non-Intrusive LM (NILM). Real time feedback and informed advice to customers obtained from refined energy consumption can greatly improve energy efficiency towards sustainable energy conservation. Compared with intrusive approaches, non-intrusive approaches enjoy low cost, easy installation, and promising scalable commercialization potentials via elaborated data obtained from NILM. However, large-scale NILM deployments are facing challenges mainly including theoretical research and innovative applications. For theoretical research, there is still no generalized model to distinguish multiple-mode appliances, similar, or unknown appliances, and there is still no universal performance metrics to evaluate various NILM algorithms, especially for some unsupervised algorithms. For innovative applications, cost and user engagement are the two most important factors to limit the scalability of NILM. Scalable NILM refers to load disaggregation model that can be generalized and that has various application scenarios in a large-scale deployment. With the main objective of achieving scalable NILM, we focus on a semi-supervised generalized load disaggregation model and innovative applications including Proactive Demand Response (PDR) and energy information recommendation for enabling action towards sustainable energy conservation. Furthermore, in order to achieve sustainable energy conservation, we develop scalable NILM system and propose a user-centered comprehensive application platform Energy (ABC)2 to seek solutions from technology aspect and user engagement. On one hand, we propose an innovative virtual closed loop control concept model with human behaviors as virtual feedback controller and apply Deep Reinforcement Learning (DRL) approaches into DR decision management and personalized energy aware recommendation towards sustainable energy conservation. On the other hand, we develop and implement NILM deployment in China and propose an innovative idea on user engagement and data sharing solution business model, namely Energy Data Sharing Platform (EDSP), and design a scheme to strengthen the scalability of NILM towards a sustainable energy future. Show less
