Hybrid Holts-Winter’s model and artificial neural network for short term load data
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-ha...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.utm.my/107982/ http://dx.doi.org/10.1063/5.0110907 |
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| Summary: | 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. |
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