Application of fuzzy - Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting

Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzy - neuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity) in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE) of 1.22%. This confirms that fuzzy - neuro is a good tool for load forecasting.