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This paper aims to detect abnormal energy uses which relate to undetected consumption, thefts, measurement errors, etc. The detection of irregular power consumption, with variation in irregularities, helps the electric utilities in planning and making strategies to transfer reliable and efficient electricity from generators to the end-users. Abnormal peak load demand is a kind of aberration that needs to be detected.

This paper proposes a Density-Based Micro Spatial Clustering of Applications with Noise (DBMSCAN) clustering algorithm, which is implemented for identification of ranked irregular electricity consumption and occurrence of peak and valley loads. In the proposed algorithm, two parameters, a and ß, are introduced, and, on tuning of these parameters, after setting of global parameters, a varied number of micro-clusters and ranked irregular consumptions, respectively, are obtained. An approach is incorporated with the introduction of a new term Irregularity Variance in the suggested algorithm to find variation in the irregular consumptions according to anomalous behaviors.

No set of global parameters in DBSCAN is found in clustering of load pattern data of a practical system as the data. The proposed DBMSCAN approach finds clustering results and ranked irregular consumption such as different types of abnormal peak demands, sudden change in the demand, nearly zero demand, etc. with computational ease without any iterative control method.

The DBMSCAN can be applied on any data set to find ranked outliers. It is an unsupervised approach of clustering technique to find the clustering results and ranked irregular consumptions while focusing on the analysis of and variations in anomalous behaviors in electricity consumption.

The purpose of the study was to develop the forming methodology for FML laminates with complex shapes, based on aluminium and epoxy-glass composite.

The subject of research encompassed Al/GFRP fibre metal laminates. Autoclave process has been selected for FML profiles production. The manufacturing process was followed by quality analysis for laminates produced.

The achievement of high stability and dimensional tolerance of thin-walled FML laminates is ensured by developed technology. The values of selected sections angles are significantly limited as a result of forming of FML laminates through the components performing. Failure to adhere to technological recommendations and to high regime of developer technology may lead to the occurrence of defects in FML.

Thin-walled composite structures could be applied in light-weight constructions, such as aircraft structures, which must meet rigorous requirements with regard to operation under complex load. The development of this type of technology may contribute to increased importance of FML sections in research area and finally to increased scope of their applications.

The production of thin-walled FML profiles with complex geometry, which would be characterized by dimensional stability and repeatable structural quality free of defects, is associated with many problems. No studies have been published so far on an effective forming process for FML laminates with complex shapes. Developed methodology has been verified through quality evaluation of produced profiles by means of non-destructive and destructive methods. The development of this type of technology may contribute to increased importance of FML, e.g. in aerospace technology.

Performance improvement for requirements planning systems is an issue which receives wide interest. Many programming approaches have been proposed to improve material requirements planning procedures. However, most of them appear to be too complex for large manufacturing problems. Alternatively it might be promising to explore the integration of materials requirements planning and capacity requirements planning. Several simple heuristics for integrated requirements planning systems are suggested. Several heuristics are offered to balance the load in the systems and several procedures presented to adjust the planned requirements so that the system will execute more smoothly. An industrial example supports the adequacy of the general concepts provided in this research. Results are presented which demonstrate the adequacy of these heuristics, and illustrate the ease of implementing the procedures into any MRP system.

The purpose of this paper is to estimate probabilistic and regional importance sensitivities of fatigue life, with respect to the laser peening (LP) parameters applied to a Titanium turbine disk.

The sensitivities were calculated from Monte Carlo (MC) analysis of 21,000 simulations and probabilistic sensitivity methods.

The probabilistic sensitivity results indicate that the peak pressure and the mid‐span are the most important variables. The regional importance sensitivity results indicate that probability of failure is the most sensitive to the left tail of peak pressure and middle region of mid‐span and the fatigue life mean is the most sensitive to the left tails of the peak pressure and the mid‐span.

The sensitivity results of this research indicate that more time and energy should be focused on managing peak pressure and mid‐span, as compared to the remaining variables, to design and improve the laser peening process.

The paper presents four sensitivity analysis approaches which were formulated and employed to estimate fatigue life sensitivities with respect to the LP variables.

The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters.

A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy.

The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time.

This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

The loading and power variations in the power system, especially for the peak hours have abundant concussion on the loading patterns of the open access transmission system. During such unconditional state of loading the transmission line parameters and the line voltages show a substandard profile, which depicts exaction of congestion management of the power line in such events. The purpose of this paper is to present an uncomplicated and economical model for congestion management using flexible AC transmission system (FACTS) devices.

The approach desires a two-step procedure, first by optimal placement of thyristor controlled series capacitor (TCSC) and static VAR compensator (SVC) as FACTS devices in the network; second tuning the control parameters to their optimized values. The optimal location and tuning of TCSC and SVC represents a hectic optimization problem, due to its multi-objective and constrained nature. Hence, a reassuring heuristic optimization algorithm inspired by behavior of cat and firefly is employed to find the optimal placement and tuning of TCSC and SVC.

The effectiveness of the proposed model is tested through simulation on standard IEEE 14-bus system. The proposed approach proves to be better than the earlier existing approaches in the literature.

With the completed simulation and results, it is proved that the proposed scheme has reduced the congestion in line, thereby increasing the voltage stability along with improved loading capability for the congested lines.

The usefulness of the proposed scheme is justified with the computed results, giving convenience for implementation to any practical transmission network.

This paper fulfills an identified need to study exaction of congestion management of the power line.

The important factors, which should be considered in the design of a hybrid system of photovoltaic and wind energy are discussed in this study. The current load demand for electricity, as well as the load profile of solar radiation and wind power of the specified region chosen in Iran, is the basis of design and optimization in this study. Hybrid optimization model for electric renewable (HOMER) software was used to simulate and optimize hybrid energy system technically and economically.

HOMER software was used to simulate and optimize hybrid energy system technically and economically.

The maximum radiation intensity for the study area is 7.95 kwh/m²/day for July and the maximum wind speed for the study area is 11.02 m/s for January.

This research is the result of the original studies.

A survey recently completed by the Computer Security Institute (CSI) and the Federal Bureau of Investigation (FBI) revealed that corporations, banks, and governments all face a growing threat from computer crime, and in particular computer hacking. Computer hacking activities caused well over US$100 million in losses last year in the USA and the trend toward professional computer crime, such as computer hacking, is on the rise. Different methods are currently used to control the computer crime problem, for example, by controling access to and from a network by implementing a firewall. As the survey highlighted, most of these methods are insufficient. New means and ways which will minimise and control the hacking problem must therefore continuously be researched and defined. Proposes a method, using trend analysis, that could be utilized to minimise and control the hacking problem in an organisation.

The purpose of this paper is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The paper also presents principal component analysis (PCA) and local outlier factor methods for detecting anomalous rail wear occurrences using field measurement data.

The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructure maintenance. The diagnostics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for nine sharp curves on the heavy haul line in Sweden.

The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T² and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations.

A practical implication of this paper is that the framework and the diagnostic tool can be considered as an integral part of e-maintenance solution. It can be easily adapted as online or on-board maintenance analytic tool with data from automated vehicle-based measurement system.

This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.

To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.