TY  - JOUR
T1  - Application of Artificial Neural Network (ANN) for Short-Term Load Forecasting (Case Study on National Control Centre (PHCN) Oshogbo, Osun State, Nigeria)
AU - Osofisan, P.B. AU - Nwaeke, C.N. 
JO  - Journal of Engineering and Applied Sciences
VL  - 5
IS  - 2
SP  - 78
EP  - 83
PY  - 2010
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2010.78.83
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2010.78.83
KW  - ANN
KW  -load forecasting
KW  -load demand
KW  -performance evaluation
KW  -artificial neural network
KW  -Nigeria
AB  - Load forecasting is essential for efficient decisions in power systems design and operation. It is very important to know the electric load trend or evolution in order to ensure a high planning and decision making efficiency. A capital importance is given to short-term load forecasting in this research. Artificial Neural Network (ANN) which is considered multivariable, nonlinear and non-parametrical model was used. An attempt is made to forecast the short-term hourly load for a large power system by applying the method of ANN. Weekday and monthly patterns were considered. The weekday patterns include Monday-Saturday loads while the monthly patterns include January-June loads. Historical data obtained from the National Control Center (NCC) of Power Holding Company of Nigeria (PHCN), Oshogbo was used to demonstrate the effectiveness of the proposed approach. The ANN developed has four layers: an input layer, two hidden layers and an output layer. The inputs to the ANN were the hourly load demand for the full day (24 h) while the outputs obtained is the load forecast for a given day i.e., the predicted hourly load demand for the next day. On performance evaluation, the mean absolute percentage error was found to be 2.087%. Hourly load demand was predicted for a full week with a high degree of accuracy.
ER  -