Forecasting of Long-Range Electrical Loads in Najaf Province Using Markov Model

Economic factors, such as the cost of electricity, which includes prices, production factors, government support, and the technical conditions of the electric sector, population growth rate, gross domestic product per capita, and urban expansion regarding the electricity demand, have an impact on electricity in the province of Najaf (as is typically the case in the provinces of Iraq). Despite government and private sector efforts to invest in this field, the current rise in demand has resulted in a deficit and a widening disparity between the two forces (supply and demand). Due to their efficiency in predicting long-term behavior, Markov chains are frequently used in various industries, including labor planning, weather forecasting, and stock market analysis. The prediction approach for the electric power field has the disadvantage that Markov chains are not used. In the present work, we propose a simple predictive tool for predicting future fitted load rate, highest load rate, and lowest required load rate. Markov chain modeling is used. In this paper, the prediction feature of Markov models was presented, where a miniature model was taken using the electrical current data of Al-Najaf Governorate for the years 2018–2022. By using Markov models, it was predicted how the results could be for the following years. The transition probability matrix, initial probability Vector, and proof were all computed. Determine the mean time to return, determine that the matrix is an ergodic Markov series, and forecast long-range electrical loads using the static distribution.