NILM Applications: Literature review of learning approaches, recent developments and challenges

Characterizing energy flexibility of buildings with electric vehicles and shiftable appliances on single building level and aggregated level

A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption of appliances by analyzing the aggregated consumption signal. The large number of appliances and the presence of appliances with close consumption values are known to limit the performance of event-based NILM methods. To tackle these challenges, one could enhance the feature space which in turn results in extra hardware costs, installation complexity, and concerns regarding the consumer’s privacy. This has led to the emergence of an alternative approach, namely semi-intrusive load monitoring, where appliances are partitioned into blocks and the consumption of each block is monitored via separate power smart meters. While a greater number of smart meters can result in more accurate disaggregation, it increases the monetary cost of load monitoring, indicating a trade-off that represents an important gap in this field. In this paper, we take a comprehensive approach to close this gap by establishing a so-called notion of “disaggregation difficulty metric (DDM),” which quantifies how difficult it is to monitor the events of any given group of appliances based on both their power values and the consumer’s usage behavior. Thus, DDM in essence quantifies how much is expected to be gained in terms of disaggregation accuracy of a generic event-based algorithm by installing smart meters on the blocks of any partition of the appliances. Experimental results based on the REDD dataset illustrate the practicality of the proposed approach in addressing the aforementioned trade-off. 

Quantification of Disaggregation Difficulty With Respect to the Number of Smart Meters

Refined measurement of electricity consumption information on the residential side is an important foundation for achieving demand-side management and promoting smart grid construction. In order to obtain highly accurate appliancelevel energy consumption data, this paper proposes a load identification method based on feature extraction of change-point and XGBoost classifier. Firstly, the event detection is performed by using secondary window determination to determine whether an event has occurred based on changes in power statistics; then, according to the detection results, comprehensively extract the transient-state and steady-state features of the electrical signal in the window; lastly, it is input into the XGBoost model for load energy decomposition. In the case validation phase, the PLAID dataset is compared with five benchmark algorithms, and the results show the effectiveness and superiority of the method proposed in this paper for resident-side load identification.

Non-Intrusive Load Monitoring Based on Feature Extraction of Change-point and XGBoost Classifier

The safe and reliable operation of the power system is an important guarantee for the steady and rapid development of social economy. The safe operation of transformer equipment is the basis for the stability and reliability of the power system, and it is of great significance to the whole system to take effective measures to make accurate predictions of abnormal conditions or faults inside the transformer. Therefore, this paper proposes a data-driven fault prediction method for oil-filled transformers, combining association rules, gray prediction model and random forest algorithm to achieve fault mode and location prediction through four stages: feature parameter sets selection, fine-grained feature parameters selection, feature parameters trend prediction and fault classification. Based on the actual data collected, the accuracy and effectiveness of the method are verified by two groups of experiments, which is beneficial to the practical application of engineering, and to a certain extent improves the automation level of equipment maintenance.

Junxingxu Chen

Papers

Non-Intrusive Load Monitoring Based on Feature Extraction of Change-point and XGBoost Classifier

A Data-Driven Fault Prediction Method for Power Transformers