In this paper, we propose a neural network approach to forecast AM/PM Jordan electric power load curves based on several parameters (temperature, date and the status of the day). The proposed method has an advantage of dealing with not only the nonlinear part of load curve but also with rapid temperature change of forecasted day, weekend and special day features. The proposed neural network is used to modify the load curve of a similar day by using the previous information. The suitability of the proposed approach is illustrated through an application to actual load data of Electric Power Company in Jordan. The results show an acceptable prediction for Short-Term Electrical Load Forecasting (STELF), with maximum regression factor 90%.
Load Flow or Power Flow Analysis in the power system in used to determine the power system parameters such as voltage, current, active power, and reactive power contained in the power grid. The method that has long been used in the calculation of load flow or power flow is the Newton-Raphson iteration method. As for its development, to complete the power flow study, it is carried out by implementing the Artificial Intelligence method, one of which is the Extreme Learning Machine method. This method is used in the simulation of the simple 39 Bus system calculation from IEEE. In this Extreme Learning Machine, the testing analysis is carried out with 2 inputs, 1 hidden layer, 5 neurons, and 2 outputs and the number of datasets is 39 to produce MAE and MAPE respectively 2.02 and 0.76% and with a very fast processing time of 0.010s
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