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

• Main Authors: Kamisan, Nur Arina Bazilah, Norrulashikin, Siti Mariam, Hassan, Siti Fatimah
• Format: Conference or Workshop Item
• Published: 2023
• Subjects: QA Mathematics
• Online Access: http://eprints.utm.my/107982/; http://dx.doi.org/10.1063/5.0110907
• Institution: Universiti Teknologi Malaysia

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.