Increasing the accuracy of short-term electrical load forecasting with considering to changes in the structure of consumption during the year
The paper presents an analysis of the influence of annual periodicity on the accuracy and stability of the electrical load short-term forecasting results. Two approaches are considered which take into account the different behavior of the electrical load in the heating season and off-season. For forecasting, we used the multilayer perceptron with scaled exponential linear unit (SELU) function used as a nonlinear transformation in hidden neurons. This function stabilizes mean and variance of layers and accelerates the learning process. In the first approach, the neural network included an additional input neuron that takes values of 1 for days that are part of the heating season and 0 for the off-season days. In this case, the given model fitted on the annual data. In the second approach, two separate neural networks are developed for work in different seasons of the year. Input vector was generated separately for each network. Estimation of the accuracy and stability of the forecasting results was carried out on year data for real electricity supply company. References 8, figures 3, table.