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Scalable Non-Intrusive Load Monitoring
Title
Scalable Non-Intrusive Load Monitoring
Creator
Zhuang, Mengmeng
Date
2019
Description
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.
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EVENT-BASED NONINTRUSIVE LOAD MONITORING
Title
EVENT-BASED NONINTRUSIVE LOAD MONITORING
Creator
Yan, Lei
Date
2021
Description
Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to...
Show moreNon-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. With this information, utilities can perform many tasks such as energy conservation, planning gen-eration more wisely, and demand response (DR) study. For house owners, they can un-derstand their bill more clearly and make better budget plan. For researchers, NILM sys-tem is a good foundation for energy management in buildings and can provide valuable power information for smart homes design. This dissertation aims to develop and demon-strate a complete and accurate event-based NILM system, which includes (1) an edge-cloud framework for event-based NILM, (2) an adaptive event detection method, (3) a two-stage event-based load disaggregation method; and (4) a high-resolution (50Hz) NILM dataset. Event detection is the first step in event-based NILM and it can provide deter-ministic transient information to identify appliances. However, existing methods with fixed parameters suffer from unpredictable and complicated changes in smart meter data such as long transition, high fluctuation and near-simultaneous events in both power and time domains. This dissertation presents an adaptive method to detect events based on home appliance load data with high sampling rate (>1Hz) by flexibly tuning the parame-ters according to the data being processed. The proposed method runs fast over the data stream and captures the transient process by multi-timescales searching as well. The mi-cro-timescale and macro-timescale window could deal with near-simultaneous events and long-transition events, respectively. Transient load signatures are extracted from detected events and stored in a sequential tree struct that can be used for NILM and load recon-struction, etc. Case studies on a 20Hz dataset, the LIFTED dataset of 50Hz, and the BLUED dataset of 60Hz demonstrate that the proposed method is able to work on data of different sampling rates and outperforms other methods in event detection. The ex-tracted load signatures could also improve the efficiency of NILM and help develop oth-er applications. This dissertation presents an online transient-based electrical appliance state track-ing method for NILM. The proposed Factorial Particle based Hidden Markov Model (FPHMM) method takes advantage of transient features in high-resolution data to infer states in the transient process and conducts steady state verification to rectify falsely identified appliances. The FPHMM method can overcome the common feature similarity problem in NILM by combining particle filter method and Markov Chain Monte Carlo sampling method, and by mining the intra-relationship of states inside a single appliance and the inter-relationship of states among multiple appliances. The FPHMM method is tested on the LIFTED dataset with appliance-level details and high sampling rates. Test-ing results demonstrate that the FPHMM method is effective in resolving the feature similarity issue. A modified mean shift algorithm with different levels of bandwidth is proposed as well to cluster the extracted features from event detection. Based on the clustered fea-tures, another solution is proposed to decode the states of appliance in two stages. The first stage uses Bayesian Inference Factorial HMM (BI-FHMM) solver to accelerate com-putational speed and improve accuracy by integrating the load signatures and statistical inference. The second stage then verifies and rectifies the results obtained from the first stage. Test results demonstrate that the proposed approach achieves good performance and can be applied to existing smart meters.
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