Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

General Research Article

Combining Daily Electricity Forecasts Based on DBSCAN and Stacking Fusion

Author(s): Zhixing Lv*, Sijin Cheng, Yi Wang, Shenzheng Wang, Xinyi Li and Ran Ran

Volume 14, Issue 7, 2021

Published on: 18 October, 2021

Page: [767 - 777] Pages: 11

DOI: 10.2174/2352096514666211018124044

Price: $65

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.

« Previous
Graphical Abstract

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy