In Peninsular Malaysia, forecasting of electricity supply or load supply is essential at Tenaga Nasional Berhad (TNB) for effective and reliable operation and planning of power system. The load forecasting can be done by three types of forecasting which are Short Term Load Forecasting, Medium Term Load Forecasting and Long Term Load Forecasting depends on the interval of time. However, effective forecasting is difficult in view of the complicated effects on load by a weather factor and customer classes. The load pattern are influenced by the condition of weather, namely heavily rain, cloudy, thunderstorm, etc as each of these weather condition has different load behavior. This project proposes a Feed Forward Neural Network method to forecast future’s load in Peninsular Malaysia with the inclusion of weather data. The idea is to select the half hourly load data of the 7 weeks as input data and use the load data of week eighth as the target data to find the best output of the forecasting process. This project also includes the load forecasting without inclusion of weather data so that the result can be compared with the result of load forecasting with inclusion of weather data.