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Abstract
Illegal access and use of electricity, known as electricity theft, represents a significant threat to the energy industry and society as a whole. This fraudulent phenomenon undermines the integrity of the electrical system, negatively affects service providers and has serious economic, social and environmental consequences. Furthermore, abnormal electricity consumption also poses significant challenges in terms of early detection of irregularities and optimization of energy consumption. In this research, the causes and motivations that drive electricity theft were analyzed. Likewise, the various methods used by offenders to manipulate energy meters and hide their fraudulent activities were examined, highlighting the need for innovative solutions to combat this problem. Of all the existing solutions, we focus on the analysis and prediction of consumption using Machine Learning techniques. The use of algorithmic machine learning models is explored as a key tool to detect and prevent electricity theft and anomalous consumption. Presenting a significant improvement in the detection of electrical energy theft.
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