Abstract
Background: Modern upgrades of power grids and a rapidly expanding economy complexify the uncertainties of electricity demand.
Objective: The objective of the study is to have a more precise prediction on the demand side, which is beneficial in affirming the stable operation of the power system.
Methods: This paper presents a combined electricity forecasting method based on the users clustering and stacking ensemble learning to mine underlying properties of different individual consumers. The preprocessed electricity consumption profiles are inputted into the DBSCAN clustering algorithm to obtain the clusters. The alternative models are tailored for different clusters in the stacking fusion framework for training and testing.
Results: Experimental results on the operating data of Shandong Power Grid show that the proposed method has higher prediction accuracy and better generalization ability.
Conclusion: The framework is of great significance for improving the level of power supply service.
Keywords: Electricity forecasting, model combination, DBSCAN, stacking ensemble learning, random forest, linear regression.
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