Asst. Prof. Dr. Abdullahi Abdu IBRAHIM2026-05-312026-05-31https://dspace.academy.edu.ly/handle/123456789/2113Developments that enable smart cities and focus on sustainability are driving the growth of renewable energy. However, adapting renewable energy is as challenging as adapting smart cities. Before the advent of renewables, the grid consisted of a few generators, the grid, and many consumers with clearly defined roles. Between these two groups there was a connection in which the current flowed only in one direction. With the development of methods of producing electricity that can be installed in homes, such as solar B. Consumers have become consumers and producers, i.e. “production consumers”. You can use your own electricity as well as save it to the grid.Unlike other products such as electricity such as oil and food, electricity cannot be stored for the future. It is delivered to the consumer immediately after manufacture. In simple terms, a power grid is a network of power plants, power lines that supply electricity to consumers. Rapid population growth and the proliferation of mining companies make it difficult to meet the electricity needs of homes and industries. Increased demand for electricity at certain times of the day can lead to various problems, such as short circuits and transformer failures. Meeting the transportation challenges of existing networks requires predicting consumer behavior to ensure efficient transmission. In this context, the concept of Smart Grid (SG) was introduced. SG can intelligently predict the power demand and save power according to the forecast demand. SG, B. Intelligent demand forecasting and aggregation can solve a variety of existing network problems. B. Reduce the risk of short circuits and power consumption by preventing power leakage, personal injury and property damage [1]realizing its true potential with 5G and other wireless networks, big data analysis and deep learning (DL) SG. SG has many stakeholders such as smart cars, smart buildings, smart power plants and smart cities.MACHINE LEARNING TECHNIQUES