# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses
#### 3.2.3 Energy Losses Caused by Poor Infrastructure and Resource Management
To facilitate comprehensive analysis, the authors integrated AI tools, specifically, ANNs and classification tools, along with performance metrics. The integration of machine learning (ML) models with performance metrics facilitates the association and filtration of the quality of the analyzed data, ensuring their optimization and model parameter normalization. In the present study, the authors have developed a comprehensive solution by employing a multi-layer perceptron ANN, which falls under the category of supervised learning. The data mining method also involves data such as ambient temperatures and other endogenous and exogenous data. The conclusion of the study demonstrates that the input data, which directly impact the identification of non-technical losses, have been enhanced, and the applied test results show an improvement in network performance of 6.26%. In the current era of rapid technological advancement, the performance and regression metrics of AI are being extensively utilized in various applications and research endeavors. In the context of identifying non-technical losses in power grids, researchers have developed an intelligent model in [48] to determine these losses. This model is based on LTSM (Long Short-Term Memory) and RUSBoot (Random Under-Sampling Boosting) techniques for optimization of the parameters considered. The resulting algorithm possesses the capacity to discern anomalous patterns that may emerge in the context of energy consumption and production processes. The utility of LTSM is twofold: it facilitates the detection of these anomalies and the extraction of features. The outcomes of the present study suggest that the proposed methodology effectively addresses challenges related to the management and adaptation of voluminous datasets, as well as the balancing of field data. A further strength of the present methodology lies in its comparison with other methods, which has yielded more efficient results. Another application based on the same direction was realized in [49] by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. In this application, the problem of non-technical energy losses was addressed. In their research, the authors studied the introduction of a model that integrates recurrent type units and GoogLeNet benefits to track and analyze one-dimensional electricity consumption data. The model has been meticulously designed to address potential imbalances in the data analyzed. The efficacy of the proposed model was evaluated by comparing it with other state-of-the-art classifiers, including multilayer perceptron ANN, logistic regression, and support vector machines. The present study demonstrates an accuracy ranging from 96% to 97% in receiver and data analyzer functionality. In the extant literature, the subjects are addressed in the context of energy losses and economic considerations. In [50], the authors addressed non-technical loss detection based on an ML algorithm. This paper proposes a methodology for anomaly detection, leveraging a sample of 100,000 customers and employing fuzzy systems and Boolean rules. The findings from this study can be extrapolated to the high-end industry, with a particular emphasis on the future analysis of smart meters situated at consumption sites. An alternative approach was proposed by [PERSON], [PERSON], [PERSON], and [PERSON] in [51], where they developed a smart methodology for non-technical loss detection through the integration of ML technology with conventional ANNs. These conventional networks possess the capability to extract deep features from a time series of consumption data. These data are then coded and indexed for subsequent analysis. The outcomes of this study substantiate the efficacy and dependability of the implemented method. In conclusion, the methods for detecting and localizing technical and non-technical energy losses are extensive and diverse. Researchers have applied several distinct methods for this process of identifying them.