Abstract
Accurate energy load forecasting provides good decision support for energy management. Current energy load forecasts focus more on forecast accuracy without exploring the similar patterns and correlations of energy load demand between regions. Our proposed interpretable hybrid multi-target regression approach provides more explanatory abilities for each region’s energy load prediction. After combining the correlation between forecast targets and hierarchical forecast information, our model achieves a high forecast accuracy in that the mean square error is reduced by three quarters compared to LightGBM’s independent prediction for each region on the GEFCom 2017 dataset.
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Notes
- 1.
A partial dependence plot shows the marginal effect of one or two features on the predicted outcome of a machine learning model.
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Wu, Z., Loo, C.K., Pasupa, K., Xu, L. (2023). An Interpretable Multi-target Regression Method for Hierarchical Load Forecasting. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_1
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