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Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series. [ABSTRACT FROM AUTHOR]

This case series describes a possible link between perforation during gastrointestinal tract (GIT) endoscopy in cats and a specific underlying condition. GIT endoscopy is commonly performed in cats, with a total of 254 endoscopies performed at a UK referral centre from 1 January 2015 to 31 December 2019. During this period, three cases were reported to have GIT perforation in conjunction with the procedure. All three cases described were diagnosed with alimentary lymphoma of varying location and histological grade. One case suffered colonic perforation and was euthanased, while the other two underwent exploratory coeliotomy. Both surgeries were successful. Of the cats that underwent surgery, one was diagnosed with lymphoplasmocytic inflammation at the perforation site and low‐grade/small cell lymphoma distally, and subsequently treated with prednisolone and chlorambucil, while the other was diagnosed with intermediate‐grade lymphoma at the perforation site, and the owners elected for euthanasia upon diagnosis. [ABSTRACT FROM AUTHOR]

The Jucazinho reservoir was built in the State of Pernambuco, Northeastern Brazil, to water supply in a great part of the population that live in the semi-arid of Pernambuco. This reservoir controls the high part of Capibaribe river basin, area affected several actions that can compromise the reservoir water quality such as disposal of domestic sewage, industrial wastewater and agriculture with use of fertilizers. This study aimed to identify the factors that lead to water quality of the Jucazinho reservoir using a database containing information of nine years of reservoir water quality monitoring in line with a multivariate statistical technique known as Principal Component Analysis (PCA). To use this technique, it was selected two components which determine the quality of the reservoir water. The first principal component, ranging from an annual basis, explained the relationship between the development of cyanobacteria, the concentration of dissolved solids and electrical conductivity, comparing it with the variation in the dam volume, total phosphorus levels and turbidity. The second principal component, ranging from a mensal basis, explained the photosynthetic activity performed by cyanobacteria confronting with the variation in the dam volume. It observed the relationship between water quality parameters with rainfall, featuring an annual and seasonal pattern that can be used as reference to behaviour studies of this reservoir. [ABSTRACT FROM AUTHOR]

Time series pattern discovery is of great importance in a large variety of environmental and engineering applications, from supporting predictive models to helping to understand hidden underlying processes. This work develops a multiresolution time series method for extracting patterns in weather records, particular temperature data. The topic is important, as, given a warming climate, morbidity andmortality are expected to rise as heatwave frequency and intensity increase. By analysing summer temperature quantiles at different levels of coarseness, it was found that compounding models can contain a complete description of severe weather events. This new multiresolution quantile approach is developed as an extension of the symbolic aggregate approximation of the temperature time series in which quantiles are computed at every stretch of the piecewise partition. The process is iterated at different scales of the partition, and it was found to be a very useful approach for finding patterns related to both heatwave periods and intensities. The method is successfully tested using real weather records from Brazil (Recife) and the UK (London), and it was found that in both locations heatwave intensity and frequency are increasing at a substantial rate. In addition, it was found that the rate of increase in intensity of the heatwaves is far outstripping the rate of increase in mean summer temperature: by a factor of 2 in Recife and a factor of 6 in London. The approach will be of use to those looking at the impact of future climates on civil engineering, water resources, energy use, agriculture and health care, or those looking for sustained extreme events in any time series. [ABSTRACT FROM AUTHOR]

This article suggests the application of multiresolution analysis by Wavelet Transform—WT and Echo State Networks—ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine’s power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error—NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model—PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks—ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting. [ABSTRACT FROM AUTHOR]

Abstract Heat waves give rise to order of magnitude higher mortality rates than other weather-related natural disasters. Unfortunately both the severity and amplitude of heat waves are predicted to increase worldwide as a consequence of climate change. Hence, meteorological services have a growing need to identify such periods in order to set alerts, whilst researchers and industry need representative future heat waves to study risk. This paper introduces a new location-specific mortality risk focused definition of heat waves and a new mathematical framework for the creation of time series that represents them. It focuses on identifying periods when temperatures are high during the day and night, as this coincidence is strongly linked to mortality. The approach is tested using observed data from Brazil and the UK. Comparisons with previous methods demonstrate that this new approach represents a major advance that can be adopted worldwide by governments, researchers and industry. Highlight • A novel weather file for building assessment facing high temperature scenarios is proposed. • The method relies on KDD involving quantile regression ensemble and data integration. • It provides additional knowledge of inputs that affect temperature extremes. • The outcome is validated w.r.t. standards and is ready to use by industry. [ABSTRACT FROM AUTHOR]