Electricity theft is a costly problem. This paper will be focused on Pakistan and the problem of electricity theft. We will discuss its impacts and how best to fix them through the use of technology. For this purpose, we developed a smart meter, focusing on grid modernization through economic smart meter development. This paper focuses on a study carried out with the help of PESCO. It is one of the most inefficient distribution providers. The study has evaluated commercial, industrial, rural, and urban areas, covering a total area of 15 km2. The area includes several power sinks. Previous research has been used to compare the results of this case study; this included studies of other Third World countries, such as Pakistan and South Africa. The design of, clever, innovative, intelligent meters used in this study was better than the basic digital meters and had many features compatible with the E.U., and U.S.A.’s western power market and energy infrastructure. The study also discusses the potential use of neural network-trained models and IoT (internet of things) integration with cloud computing. This can provide an alternate means of data analysis, accurate prediction, and greater user accessibility. The case study is the first ever done using smart meters on such a large scale, and the compiled data has provided insight into energy consumers and their usage. The statistics can be used to isolate the most probable cause of theft and the area or location of occurrence.

This paper focuses on the training of a deep neural network regarding danger sign detection and recognition in a substation. It involved applying the concepts of neural networks and computer vision to achieve results similar to traffic sign and number plate detection systems. The input data were captured in three distinct formats, i.e. grayscale, RGB, and YCbCr, which have been used as a base for comparison in this paper. The efficiency of the neural network was tested on a unique data set involving danger signs present in industrial and processing facilities. The data set was unique, consisting of four distinct symbols. The trained data were selected so that they would not facilitate overfitting and also would not be under fitted. The accuracy of the model varied with the input type and was tested with two distinct classifiers, CNN and SVM, and the results were compared. The model was designed to be fast and accurate, and it can be implemented on mobile devices.