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Robust Load Identification Algorithms under Varying VoltagesAuthor: Raghunath Reddy Date: 2022-06-21 Report no: IIIT/TH/2022/98 Advisor:Vishal Garg,Vikram Pudi AbstractThe rise in energy demand is a major cause of global warming and climate change. The U.S. Energy Information Administration (EIA) projects that the world energy consumption will grow by nearly 50% and the energy consumed in the building sector will increase by around 65% by 2050. There are two crucial sustainability measures, improving energy efficiency and reducing energy consumption in the building sector. To assist in this, Appliance Load Monitoring (ALM) is essential for effective load management because it determines the energy consumption and operating states of individual appliances. Energy feedback information can help reduce consumption by 5% to 15%. Compared to a single aggregate consumption feedback, a detailed individual appliance consumption feedback is more beneficial. Most electrical appliances do not have the capabilities to monitor their individual energy consumption details. To estimate this energy consumption for appliances, the two most widely used load monitoring approaches are Intrusive Load Monitoring (ILM) and Non-Intrusive Load Monitoring (NILM). ILM makes use of distributed sensing using smart power strips or power sockets to monitor individual appliances. Such kind of installation requires entering inside a house and therefore, ILM is intrusive in nature. NILM or load disaggregation is an approach to estimate individual appliance energy consumption from aggregate load measurement obtained from a single measurement point. Features extracted from electrical signals are used for automatic appliance identification. Researchers in the past have published machine learning-based ILM and NILM techniques. However, these machine learningbased ILM/NILM techniques are sensitive to noise, and their performance degrades in the presence of variations in data due to noise, sensor drift and source voltage fluctuations. The observations made by ESMI (Electrical Supply Monitoring Initiate) of Prayas Group show that grid voltages fluctuate approximately between 210 to 250 V in some regions of India. Varying voltages lead to changes in appliance power consumption patterns. Appliance identification models may not recognize appliances correctly because of their modified consumption patterns. The ILM and NILM approaches have to be tested in these varying operating conditions. There is a need to develop a low-cost, efficient and accurate load monitoring solution. This thesis investigates the robustness of supervised load identification techniques for improving ILM and NILM approaches. The first part of the thesis provides techniques to improve the ILM approach, especially to monitor plug loads. Plug loads are the appliances that are plugged in AC sockets. Plug loads account for 20% to 30% of building energy consumption, and there is an increasing trend in plug load consumption. The energy performance of buildings can be improved by effective plug load monitoring and control. A plug load monitoring field study is conducted in an academic institute to gain insights into plug load usage and energy consumption patterns. Large scale deployment of such load monitoring solutions is costly. Without load-sensing capabilities, it isn’t easy to track some plug loads that keep changing locations. Smart plug strips or smart sockets with plugged-in load identification capability are helpful for monitoring loads automatically. Smart strips or sockets record electrical measurements using an energy metering sensor. The extracted features or load signatures are used to train machine learning models for load identification. Comparative performance analysis of these load identification techniques is provided. The results show that a simple algorithm like KNN on low-frequency data of two minutes provides excellent identification accuracy. However, the identification models had problems in the presence of voltage variations. A novel Regression-based Nearest Neighbour (RBNN) Classifier is developed for robust plug load identification under varying voltages. Regression technique is used to recognize the behavior of plug loads based on their energy consumption signature and then use identified behavior for classification. The proposed algorithm is evaluated against the traditional classification algorithm on a dataset of 70 plug loads operating in varying voltage conditions. Experimental results show that the proposed algorithm performs better than standard classifiers in most of the cases. Also, a prototype smart power strip with the proposed load identification technique is designed. The remaining part of this thesis describes ways to improve event-based NILM techniques. An event-based NILM approach identifies ON or OFF events and extracts features to build appliance identification models. A study of event-based NILM algorithms under the influence of varying voltages is performed. Feature extraction and feature selection techniques are developed to reduce the impact of voltage variation. Appliance load identification methods in NILM generally use either steady-state or transient features for load identification. These are complementary features, hence a hybrid combination would result in an improved appliance signature. A feature fusion-based NILM technique for appliance identification is developed. Low dimensional hybrid features for appliance identification technique using Naive Bayes, K-Nearest Neighbor, Decision Trees, and Random Forest classifiers are developed. The proposed NILM methodology is evaluated for robustness in changing environments. Experimental results show that the proposed feature fusion-based algorithms are more robust and outperform steadystate and transient feature-based algorithms by at least 9% and 15%, respectively Full thesis: pdf Centre for IT in Building Science |
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