The airline industry is moving toward proactive risk management, which aims to identify and mitigate risks before accidents occur. However, existing methods for such efforts are limited. They rely on predefined criteria to identify risks, leaving emergent issues undetected. This paper presents a new method, cluster-based anomaly detection to detect abnormal flights, which can support domain experts in detecting anomalies and associated risks from routine airline operations. The new method, enabled by data from the flight data recorder, applies clustering techniques to detect abnormal flights of unique data patterns. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conducted using two sets of operational data consisting of 365 B777 flights and 25,519 A320 flights. The performance of cluster-based anomaly detection to detect abnormal flights was compared with those of multiple kernel anomaly detection, which is another data-driven anomaly detection algorithm in recent years, as well as with exceedance detection, which is the current method employed by the airline industry. Results showed that both cluster-based anomaly detection to detect abnormal flights and multiple kernel anomaly detection were able to identify operationally significant anomalies, surpassing the capability of exceedance detection. Cluster-based anomaly detection to detect abnormal flights performed better with continuous parameters, whereas multiple kernel anomaly detection was more sensitive toward discrete parameters.

In many parts of the world, access to basic electricity services remains a significant challenge. The status quo mode of electrification is central grid extension; however, in many areas, off-grid (OG) technologies like minigrids (MGs) and standalone (SA) systems are more suitable for promoting electricity access under cost constraints. Unfortunately, these opportunities are often overlooked due to the complexities of electrification planning, especially for large areas. Researchers have designed technoeconomic planning tools that can be scaled to cut through aspects of this complexity and be fit to address different places and contexts. This paper describes a computer-based optimization model--named the reference electrification model (REM)--which performs automatic electrification planning and is able to identify lowest cost system designs to most effectively provide desired levels of electricity access to populations of any given size. In doing so, REM determines the most suitable modes of electrification for each individual consumer by specifying whether customers should be electrified via grid extension, OG MGs, or SA systems. For each system, REM supplies detailed technical designs at the individual customer level. We have used this model in real planning activities in sub-Saharan Africa and South Asia. The description of REM's capabilities is supported by case examples. REM stands apart from other electrification planning models because of its high granularity and its capability to provide concrete plans for a wide range of geographical scales. Because of these benefits, REM has the potential to help rationalize electrification planning and expedite progress toward universal electricity access worldwide.

Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

A method has been developed to support Flight Operations Quality Assurance (FOQA) by identifying anomalous flights based on onboard-recorded flight data using cluster analysis techniques. Unlike current techniques, the method does not require pre-defined thresholds of particular parameters, but detects data patterns which differ from the majority of flights by considering all the available flight parameters. The method converts time series data from multiple flight parameters into a high dimensional data vector. Each vector captures all the available information for a single flight. Cluster analysis of the vectors is performed to identify nominal flights which are associated with large clusters and anomalous flights that do not belong to a specific cluster. The method was applied to a representative Digital Flight Fata Recorder (DFDR) dataset from an international airline. Detailed analysis was performed on takeoff and approach for 365 B777 flights. Abnormal flights were detected using the cluster technique which was able to identify anomalous behaviors including: high and low energy states, unusual pitch excursions, abnormal flap settings, high wind conditions. In addition, data clusters representing nominal conditions were also detected. Three distinct takeoff clusters were identified in the B777 data: one represented a majority of the takeoff cases, one correlated with a specific high altitude airport, one correlated with reduced power takeoffs. This initial evaluation indicates that cluster analysis is a promising approach for the identification of anomalous flights from onboard-recorded flight data.