Trabalho de Conclusão de Curso de Graduação
Análise do efeito da temperatura na previsão de curto prazo da demanda de energia elétrica através de redes neurais
Fecha
2021-02-23Autor
Miranda, Priscila Bernardeli
Institución
Resumen
Demand forecasting is a crucial and decisive activity for the operation of electrical
power systems, thus estimating the adequate load to be supplied. In the short-term
horizon, energy consumption forecasts occur from minutes, hours and even a week ahead
and climatic factors that can influence demand are analyzed, thereby obtaining the best
instant load profile. Several techniques are applied for load prediction, among, there are
artificial neural networks, which are non-linear computational intelligence systems that
operate like neural networks human brain and reproduce human characteristics, such
generalization, association, learning and abstraction information. The feedforward architecture
is characterized as the most appropriate model for modeling a system, among the
main types of networks with this architecture is the multilayer perceptron with Backpropagation
training algorithm, which has applicability in the prediction of time series
and standards recognition. In this context, this work proposes a methodology to carry
out demand forecast in the short-term using real energy, demand and temperature data,
which demonstrate the characteristics of the demand curve profiles. Therefore, a model
with artificial multilayer perceptron neural networks is being using the scaled conjugate
gradient backpropagation (SCGB) training algorithm. The network is developed using
MATLABR
software, with Neural Network Toolbox tool (nntool) applied to create, train
and present data for analysis 24 hours ahead. For the forecast system tested, the results
showed the efficiency of the created ANN, and adequate forecast curves with satisfactory
MSE and MAPE error values.