The power system is changing. Classic methodologies applied for planning the distribution system should be adapted in an environment of Smart Grids. In this sense, an accurate forecast of energy demand will be essential to the utilities in this new grid model, being prepared for insertion of electric vehicles (EV), distributed generation (DG) and strategies on the demand side management (DSM). Therefore, this paper aims to evaluate and discuss the main challenges that energy companies in the Brazilian electric sector will face in the coming years. The impact of new variables in load forecast are presented as well as forecasting methodologies that have been studied and applied, aiming to identify establish guidelines to the utilities in their future studies of load forecast and system planning.
Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft and fraud. NTLs can be characterized as outliers in historical data series. The use of computational tools to identify outliers is the subject of research around the world, and in this context, artificial neural networks (ANN) are applicable. ANNs are machine learning models that learn through experience, and their performance is associated with the quality of the training data together with the optimization of the model’s architecture and hyperparameters. This article proposes a complete solution (end-to-end) using the ANN multilayer perceptron (MLP) model with supervised classification learning. For this, data mining concepts are applied to exogenous data, specifically the ambient temperature, and endogenous data from energy companies. The association of these data results in the improvement of the model’s input data that impact the identification of consumer units with NTLs. The test results show the importance of combining exogenous and endogenous data, which obtained a 0.0213 improvement in ROC-AUC and a 6.26% recall (1).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.