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%.