# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 4 Methods to Reduce Energy Losses
### Non-Technical Energy Losses
In the present study, the authors have emphasized the adoption of new technologies by putting a cost-benefit analysis at the forefront. The new technologies encompass the implementation of amorphous metal transformers, the integration of capacitor banks, and the monitoring of energy consumption measurements. The findings of this study provide a promising methodology for reducing non-technical energy losses. An examination of the issue of losses from the vantage point of the considerations reveals that, in [73], the problems that were recorded as a result of improper billing were primarily addressed, indicating a high level of energy losses. Furthermore, estimated losses due to illegal connections and meter tampering also contribute to the recording of significant amounts of energy losses in the non-technical category. The proposed method involves the installation of metering equipment at distribution transformers to facilitate the identification of illegal connections or fraudulent energy meters. The metering equipment is installed on the low-voltage side of the distribution transformers, and the data measured at this point are compared with the data collected from the energy meters at the consumers' level. This process enables the identification of specific locations where fraudulent activities are occurring. The efficacy of this method is evident in its ability to reduce losses, a critical aspect of energy management. Notably, this method is currently under analysis in India, a region that faces significant challenges due to the prevalence of energy system fraud. At present, distribution systems are confronted with a multitude of issues, largely attributable to the inaccurate measurement of end-user consumption data. In addressing this issue, [PERSON] and [PERSON] et al. [74] have developed a study grounded in data mining, with the objective of enhancing the efficacy of detecting energy fraud and consequently proposing a method for its mitigation or elimination. The proposed intelligent model, informed by expert insights, aims to enhance the accuracy of these measurements. This method integrates an ANN, statistical data, and text mining functions, collectively leveraging advanced analytical capabilities to identify non-technical energy losses. The efficacy of the proposed software has been thoroughly evaluated through a real-world database, yielding favorable outcomes in terms of loss detection and reduction, particularly in instances stemming from erroneous measurements. In a similar vein, researchers in [75] addressed the issue of non-technical losses by monitoring the energy delivered directly to transmission and distribution lines prior to its delivery to consumers, where an additional measurement is taken. Consequently, the installation of smart meters on distribution lines was anticipated to primarily detect significant voltage drops, thereby automatically identifying instances of unauthorized energy consumption. The results obtained from the study demonstrate an enhancement in the percentage of losses in a radial distribution system. The efficacy of the method that was tested and analyzed was found to be 33.6%, which is a substantial improvement when compared to the conventional methods that are employed by electricity distribution companies. A comparison of the success rate of the traditional method, which analyzes a smaller number of consumers (10%), with the success rate of the method adopted by the authors reveals a significant improvement in the process of loss limitation. The process of identifying non-technical losses in electrical distribution networks has the potential to contribute to a significant reduction in these losses. In this regard, the authors have conducted an analysis, in [76], of the use of appropriate energy metering equipment on the high-voltage side. The proposed method for analyzing consumption on the transmission side aims to enhance efficiency at the medium- and low-voltage levels.