Artificial Neural Network-based Approach for Short-term Electricity Price Forecasting

Mfonobong A. Umoren, Umoh T. Umoh, Ye-Obong N. Udoakah


Electricity price forecasting has become an integral part of power system operation and control. This paper presents an artificial neural network (ANN), based approach for estimating short-term wholesale electricity price using past price and demand data. In other to obtain accurate model, several combination of input parameters was considered. 70% of the data sample was used for training, 15% for validation and 15% for testing. The ANN model was trained in MATLAB using Levenberg-Marquardt back propagation algorithm for forecasting the next 24 hours electricity price. The accuracy of the model was measured using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE).


Artificial Network; Electricity Market; Levenberg-Marquardt Algorithm; Price forecasting

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