Dr. Suhail Aftab Qureshi

In a deregulated, competitive power market, utilities tend to maintain their generation reserve close to the minimum required by an independent system operator. This makes a need for an accurate instantaneous-load forecast for the coming several dozen minutes. This work is related to the Short-Term Load Forecasting (STLF) in power system operations. A three layered Feed-Forward Neural Network (FFN), trained by Resilient BackPropagation(RBP), is used. This algorithm considered more powerful as compared to the other traditional approach when the actual loads are forecasted and used as input variables. It provides more reliable forecasts, especially when the weather conditions are different from those represented in the training data. The proposed model can provide 24 hour ahead load forecasts. Separate ANNs are utilized for load forecasting of one hour to twenty-four hours ahead. The inputs to the ANN are previous day load with other factors related to it, and the output of the ANN is the predicted load for the next day for a given hour. Artificial Neural Network (ANN) in MATLAB software was used in solving the forecasting problem. The average error percentage was determined by using the Mean Absolute Percentage Error (MAPE). The result of Multi-Layer Perceptron (MLP) network model, used for one day ahead short term load forecast, shows that MLP network has a good performance and accurate prediction was achieved for this model. Its forecasting reliabilities were evaluated by computing the mean absolute error between the actual and predicted values. We were able to obtain Mean Absolute Percentage Error (MAPE) of 1.9214% which represents a high degree of accuracy.

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