No abstract

The electrical load is affected by the weather conditions in many countries as well as in Iraq. The weather-sensitive electrical load is, usually, divided into two components, a weather-sensitive component and a weather-insensitive component (baseload). The impact of the weather-sensitive component includes the summer and winter periods, without distinguishing between them. The characteristics and specifications of this component differ in summer and winter due to the different loads in the seasons, so it is best to separate these two components into two independent components. The research provides a method for separating the weather-sensitive electrical load into three components, the summer component, the winter component, and the base component. The artificial neural network was used to predict the weather-sensitive electrical load using the MATLAB R17a software. Weather data and loads were used for one year for Mosul City. The performance of the artificial neural network was evaluated using the squared error rate and the mean absolute error ratio. The results indicate the accuracy of the prediction model used in the research.