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Open AccessArticle

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

1
College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China
2
Business School, Sichuan University, Chengdu 610065, China
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(1), 217; https://doi.org/10.3390/su10010217
Received: 14 December 2017 / Revised: 10 January 2018 / Accepted: 15 January 2018 / Published: 16 January 2018
(This article belongs to the Collection Power System and Sustainability)
With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models. View Full-Text
Keywords: peak load; classification forecasting; group method of data handling; semi-supervised learning peak load; classification forecasting; group method of data handling; semi-supervised learning
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Yang, L.; Yang, H.; Liu, H. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting. Sustainability 2018, 10, 217.

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