د.عبدالواحد ادريس فضل2026-10-152026-10-15https://dspace.academy.edu.ly/handle/123456789/2648There is a growing tendency towards unbundling the electricity system. This is continually confronting the different sectors of the industry (generation, transmission, and distribution) with increasing demand on planning management and operations of the network. The operation and planning of a power utility system requires an adequate model for electric power load forecasting [1]. Forecasting load accurately plays a very important role for power energy management system [2]. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost [1].Load forecasting is one of the important concerns of power system, and it is essential for managing supply and demand of electricity. The short term load forecast (STLF) is important to predict the near future loads, since there are various factors that influence the behaviour of the consumer loads. The most important factors that may be considered are Load, Temperature, and humidity, changes with time. In this research the Method of Artificial Neural Network (ANN) will be applied for forecast the short-term load, for Libyan western electric power system, The ANN is used to learn the relationship of variables among past data. There are several parameters investigated in this research in order to solve a load forecasting problem by using back propagation-momentum learning method for different neural networks.network algorithm for Tripoli electrical network