# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 4 Methods to Reduce Energy Losses
### Non-Technical Energy Losses
The integration of AI algorithms into various domains has become a common practice, driven by advancements in technology. In this context, [PERSON] integrated, in [87], AI algorithms within the energy sector to protect the electrical sector from possible fraud. To this end, he developed a complex AI algorithm based on the analysis of large data sets to detect fraudulent activities. Despite the implementation complexity of this algorithm, its real-time responsiveness and accuracy make it a reliable method for real-world application. Consequently, a combination of AI and ML algorithms was developed to determine proactive measures to prevent illegal behavior. Non-technical losses are frequently addressed in specialized works, as their reduction signifies a substantial enhancement in the functionality of power networks. Consequently, the present paper [88] explores strategies for mitigating errors stemming from erroneous billing, constituting a subcategory of non-technical losses. Therefore, numerous energy supply companies prioritize the enhancement of billing processes, despite the complexity of tracking these processes. In such contexts, the analysis of qualitative and quantitative indicators within dynamic data modeling emerges as a pivotal approach. In the present study, a methodology was proposed to analyze potential errors arising from the measurement and manipulation of dynamic data. To this end, an intelligent System Dynamics model has been implemented to thoroughly analyze the behavior of the indicators, thereby illuminating the operational status of the invoice processing system. Consequently, this billing process improvement methodology was evaluated in conjunction with other methodologies, including Safety System Analysis, to assess the comprehensive billing operating system and formulate a comprehensive management plan. In their research [89], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], [PERSON] similarly analyzed the electricity billing process and implemented a method to analyze the whole process so that possible errors can be identified and eliminated in a timely manner. The identified billing errors are attributed to erroneous readings recorded by the personnel of electricity companies during field visits to take meter readings. Additionally, the presence of misprinted bills has the potential to adversely impact the escalation of non-technical losses within electricity networks. The objective of this study was twofold: first, to reduce the number of inconsistencies caused by incorrect readings, and, second, to optimize the reduction in the number of erroneous bills sent to customers. The proposed solution to meet this objective entailed the integration of an intelligent method for analyzing customers' seasonal behavior using Empirical Bayes, which was based on a sample of 35,704,489 measurements taken from approximately 1.3 million different customers. The results obtained from this analysis demonstrate a substantial improvement in terms of reducing the number of erroneous invoices sent to customers, without in any way harming the reputation of the company under analysis. Illegal tampering with meters to measure the amount of electricity used is also a common problem, especially in regions with a poor population. In [90], this method of energy theft is analyzed in India, where financial crises due to lack of income are prevalent. Notably, as financial distress intensifies, the prevalence of fraudulent activities has escalated, with meter tampering emerging as a prevalent method of theft. To address this issue, a proposed solution involves replacing existing meters with new meters that possess advanced features, including built-in anti-tampering mechanisms. Furthermore, the integration of these advanced meters into existing electricity networks in regions grappling with energy fraud has been investigated to mitigate the occurrence of such illicit activities. Another impact study was conducted by [PERSON] and [PERSON] et al.