The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios

The impact of climate change on the habitat suitability for large brown trout (Salmo trutta L.) was studied in a segment of the Cabriel River (Iberian Peninsula). The future flow and water temperature patterns were simulated at a daily time step with M5 models' trees (NSE of 0.78 and 0.97 respectively) for two short-term scenarios (2011 2040) under the representative concentration pathways (RCP 4.5 and 8.5). An ensemble of five strongly regularized machine learning techniques (generalized additive models, multilayer perceptron ensembles, random forests, support vector machines and fuzzy rule base systems) was used to model the microhabitat suitability (depth, velocity and substrate) during summertime and to evaluate several flows simulated with River2D©. The simulated flow rate and water temperature were combined with the microhabitat assessment to infer bivariate habitat duration curves (BHDCs) under historical conditions and climate change scenarios using either the weighted usable area (WUA) or the Boolean-based suitable area (SA). The forecasts for both scenarios jointly predicted a significant reduction in the flow rate and an increase in water temperature (mean rate of change of ca. −25% and +4% respectively). The five techniques converged on the modelled suitability and habitat preferences; large brown trout selected relatively high flow velocity, large depth and coarse substrate. However, the model developed with support vector machines presented a significantly trimmed output range (max.: 0.38), and thus its predictions were banned from the WUA-based analyses. The BHDCs based on the WUA and the SA broadly matched, indicating an increase in the number of days with less suitable habitat available (WUA and SA) and/or with higher water temperature (trout will endure impoverished environmental conditions ca. 82% of the days). Finally, our results suggested the potential extirpation of the species from the study site during short time spans.The study has been partially funded by the IMPADAPT project (CGL2013-48424-C2-1-R) - Spanish MINECO (Ministerio de Economia y Competitividad) - and FEDER funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). We are grateful to the colleagues who worked in the field and in the preliminary data analyses, especially Juan Diego Alcaraz-Henandez, David Argibay, Aina Hernandez and Marta Bargay. Thanks to Matthew J. Cashman for the academic review of English. Finally, the authors would also to thank the Direccion General del Agua and INFRAECO for the cession of the trout data. The authors thank AEMET and UC by the data provided for this work (dataset Spain02).Muñoz Mas, R.; López Nicolás, AF.; Martinez-Capel, F.; Pulido-Velazquez, M. (2016). Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios. Science of the Total Environment. 544:686-700. https://doi.org/10.1016/j.scitotenv.2015.11.14768670054

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization

A dynamic weighted RBF-based ensemble for prediction of time series data from nuclear components

International audienceIn this paper, an ensemble approach is proposed for prediction of time series data based on a Support Vector Regression (SVR) algorithm with RBF loss function. We propose a strategy to build diverse sub-models of the ensemble based on the Feature Vector Selection (FVS) method of Baudat & Anouar (2003), which decreases the computational burden and keeps the generalization performance of the model. A simple but effective strategy is used to calculate the weights of each data point for different sub-models built with RBF-SVR. A real case study on a power production component is presented. Comparisons with results given by the best single SVR model and a fixed-weights ensemble prove the robustness and accuracy of the proposed ensemble approach

Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM). The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA). The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well as the other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects