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. The research provides a method for separating the weather-sensitive electrical load into five components. and aims to prove the efficiency of the five-component load Forecasting model. 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 mean squared error and the mean absolute percentage error. The results indicate the accuracy of the prediction model used, MAPE equal to 0.0402.
The residential electrical load in the city of Mosul as well as in most of cities in Iraq, is the major problem for the administration of electricity distribution. Since this kind of load is increasing drastically compared with other loads such as industrial, agricultural tourism and others which are declining for the last two decades due to unstable condition of the county. The residential electrical load components must be determined to solve the problems resulting from the significant increase in this load. This research aims to conduct a field survey to find out and identify the components of the residential electrical load ratios and qualitative change in the months of the year. The survey was conducted in the city of Mosul in northern Iraq. T he results were analyzed, and a number of recommendations were given to rationalize consumption.