MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques

Datamining has become increasingly common in both the public and private sectors. A non-technical loss is defined as any consumed energy or service which is not billed because of measurement equipment failure or ill-intentioned and fraudulent manipulation of said equipment. The detection of non-technical losses (which includes fraud detection) is a field where datamining has been applied successfully in recent times. However, the research in electrical companies is still limited, making it quite a new research topic. This paper describes a prototype for the detection of non-technical losses by means of two datamining techniques: neural networks and statistical studies. The methodologies developed were applied to two customer sets in Seville (Spain): a little town in the south (pop: 47,000) and hostelry sector. The results obtained were promising since new non-technical losses (verified by means of in-situ inspections) were detected through both methodologies with a high success rate

Detection of Non-Technical Losses in Smart Distribution Networks: a Review

With the advent of smart grids, distribution utilities have initiated a large deployment of smart meters on the premises of the consumers. The enormous amount of data obtained from the consumers and communicated to the utility give new perspectives and possibilities for various analytics-based applications. In this paper the current smart metering-based energy-theft detection schemes are reviewed and discussed according to two main distinctive categories: A) system statebased, and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT

Non-technical skills learning in healthcare through simulation education: Integrating the SECTORS learning model and Complexity theory

Background: Recent works have reported the SECTORS model for non-technical skills learning in healthcare. The TINSELS programme applied this model, together with complexity theory, to guide the design and piloting of a non-technical skills based simulation training programme in the context of medicines safety. Methods: The SECTORS model defined learning outcomes. Complexity Theory led to a simulation intervention that employed authentic multi-professional learner teams, included planned and unplanned disturbances from the norm and used a staged debrief to encourage peer observation and learning. Assessment videos of non-technical skills in each learning outcome were produced and viewed as part of a Non-Technical Skills Observation Test (NOTSOT) both pre and post intervention. Learner observations were assessed by two researchers and statistical difference investigated using a student’s t-test Results: The resultant intervention is described and available from the authors. 18 participants were recruited from a range of inter-professional groups and were split into two cohorts. There was a statistically significant improvement (P=0.0314) between the Mean (SD) scores for the NOTSOT pre course 13.9 (2.32) and post course 16.42 (3.45). Conclusions: An original, theoretically underpinned, multi-professional, simulation based training programme has been produced by the integration of the SECTORS model for non-technical skills learning the complexity theory. This pilot work suggests the resultant intervention can enhance nontechnical skills

Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.Comment: Proceedings of the Seventh IEEE Conference on Innovative Smart Grid Technologies (ISGT 2016

Evaluation of the Scrub Practitioners' List of Intraoperative Non-Technical Skills (SPLINTS) system

Peer reviewedPostprin

Muskrats on tidal marshes of Dorchester County

This bulletin reports, in a non-technical manner, investigations on the Virginia muskrat, prevalent in Maryland, from July, 1949 to June, 1951