| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 04041 | |
| Number of page(s) | 4 | |
| Section | Circuit Simulation, Electric Modules and Displacement Sensor | |
| DOI | https://doi.org/10.1051/matecconf/201823204041 | |
| Published online | 19 November 2018 | |
Daily Electricity Consumption Forecasting Based on Lazy Learning
Department of Electrical Engineering, University of Shanghai for Science and Technology, Yangpu District, Shanghai 200093, China
a Corresponding author: y-somnus-love@foxmail.com
Daily electricity consumption is varying randomly. To improve forecasting accuracy, a Lazy Learning (LL) model is proposed. LL aims to build the regression forecasting models upon vectors which are chosen by K-vector nearest neighbors (K-VNN) method. K-VNN can solve overfitting problem and high accuracy can be ensured. Since there are many factors related to electricity consumption, Grey T's correlation degree is used to determine key indexes to further improve the running efficiency of the model. In addition, fuzzy C-means (FCM) clustering is applied to explore the similar scenarios, then the searching scope of LL is reduced. A case studied in one building in Shanghai shows the proposed method can enhance the accuracy and efficiency of electricity consumption forecasting.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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