Hybrid Holts-Winter’s model and artificial neural network for short term load data

Abstract

Since seasonal data incorporates a seasonal cycle, forecasting seasonal data differs from forecasting ordinary time series data. Because of its utility in forecasting a linear relationship with other factors, Holt-Winter's model has been frequently employed in load forecasting. However, Holt-has Winter's the drawback of having difficulty modeling a nonlinear connection between the variables and influencing factors. On the other hand, the neural network model is an excellent model for representing nonlinear data. As a result, a combination of Holt-Winter's and NN models is proposed in this work to anticipate future load demand. This hybrid model is then compared to the Holt-Winter and NN models to assess how well it performs. As a performance metric, the RMSE and MAE are utilized, and a fractional residual plot is presented to visualize the error graphically. This model, based on the findings, provides a better prognosis than the other two models.