The purpose of this paper is to compare the performance of different structures of Artificial Neural
Networks (ANNs) regarding the output variables used for short term forecasting of the next day load of the
interconnected Greek power system. In all cases the output variables are the hourly actual loads of the next day.
The classical ANN design adopts an ANN model with 24 output variables. Alternatively, 24 different ANN
models can be implemented for each hour of the day. This solution can affect the selection of input variables
indirectly. In this paper, various scenarios of the solution of 24 different ANN models are going to be studied
with different sets of input variables using the scaled conjugate gradient training algorithm, for which a
calibration process is conducted regarding the crucial parameters values, such as the number of neurons, the
type of activation functions, etc. The performance of each structure is evaluated by the Mean Absolute
Percentage Error (MAPE) between the experimental measurements and estimated values of the hourly load
demand of the next day for the evaluation set in order to specify the optimal ANN. Next, the load demand for
the next day of the test set (with the historical data of the current year) is estimated using the best ANN
structure, to verify the behaviour of ANN load prediction techniques. Finally the classical design and different
proposed structures are compared.
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15th WSEAS International Conference on Systems
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