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NON-INTRUSIVE LOAD MONITORING IN RESIDENTIAL BUILDING
Title
NON-INTRUSIVE LOAD MONITORING IN RESIDENTIAL BUILDING
Creator
Lu, Mengqi
Date
2019
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 do many tasks such as energy conservation, planning generation more wisely, and demand response (DR) study. For house owners, they can understand their bill more clearly and make monthly budget plan. For researchers, NILM system is a good way to do the energy management in buildings and help to provide power information for smart homes design. Thus, an increasing number of new algorithms have been developed in recent years. In these algorithms, researchers either use existing public datasets or collect their own data which causes such problems as insufficiency of electrical parameters, missing of ground-truth data, absence of many appliances, and lack of appliance information. To solve these problems, this dissertation presents a model-based platform for NILM system development, namely Functional Intrusive Load Monitor (FILM). By using this platform, the state transitions and activities of all the involved appliances can be preset by researchers, and multiple electrical parameters such as harmonics and power factor can be monitored or calculated. This platform will help researchers save the time of collecting experimental data, utilize precise control of individual appliance activities, and develop load signatures of devices. Moreover, event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This dissertation also presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets.Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.
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RESIDENTIAL LOAD DATA COMPRESSION AND LOAD DISAGGREGATION
Title
RESIDENTIAL LOAD DATA COMPRESSION AND LOAD DISAGGREGATION
Creator
Xu, Runnan
Date
2021
Description
Non-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total electricity consumption of a household into...
Show moreNon-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total electricity consumption of a household into the single appliance information. For the customer side, users can change their consumption habit and save more electricity. For the utility, generation scheduling will be more accurate, efficient, and secure. Furthermore, energy management system, demand response and fault diagnosis will benefit from the real time information provided by the NILM. This dissertation first proposes a data compressed method suitable for the NILM data. Then a real time disaggregation based on the Kalman filter is proposed to obtain the appliance state information. A model-free lossless data compression method for time series in smart grids (SGs), namely, Lossless Coding considering Precision (LCP) method is proposed. The LCP method encodes the current datapoint only using the immediate previous datapoint by differential coding, XOR coding, and variable length coding and transmits the encoded data once generated. It does not use the dynamics (e.g., many previous datapoints) or prior knowledge (e.g., mathematical models) of the time series. It considers the patterns, potential applications, and associated precision to preprocess the time series and especially suits high-resolution time series with long steady periods. The LCP method features low-latency and generalizability which enables real-time data communication for different time-critical tasks. Sub-metered load profiles in REDD dataset, high-resolution LIFTED dataset, AMPds dataset and PMU dataset are used to evaluate the performance of the LCP method. The results show that the LCP method demonstrates high compression ratio, low latency, and low complexity compared to state-of-the-art Resumable Data Com-pression (RDC) method, DEFLATE based on LZ77 & Huffman coding, and Lempel-Ziv-Markov Chain Algorithm (LZMA). An online method based on the transient features of individual appliances and system steady-state characteristics is proposed to estimate the appliances’ working states. It determines the number of states for each appliance via Density-based Spatial Clustering of Applications with Noise (DBSCAN) method and models the transition relationship among different states. The states of working appliances are identified from aggregated power signals by implementing the Kalman filtering method into the Factorial Hidden Markov Model (FHMM) and by the verification of system states which are the combination of working states of individual appliances. The proposed method is event based and the use of transient features extracted from event detection could achieve fast state inference and is suitable for online load disaggregation. The proposed method is tested on high-resolution dataset such as LIFTED and outperforms other related methods, including Segment-wise Integer Quadratic Constraint Programming (SIQCP), Combinatorial Optimization (CO), and the exact FHMM (FHMM_EXACT), in terms of accuracy, f1 score, and computational time.
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