Next Article in Journal
Visitors’ Attitudes towards Bicycle Use in the Teide National Park
Previous Article in Journal
Future Projected Changes in Local Evapotranspiration Coupled with Temperature and Precipitation Variation
Article Menu

Export Article

Open AccessArticle
Sustainability 2018, 10(9), 3282; https://doi.org/10.3390/su10093282

Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

1,2,3
,
1,3,* , 1,3
and
1
1
Economics and Management School, North China Electric Power University, Changping District, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping District, Beijing 102206, China
3
Institute of Smart Energy, North China Electric Power University, Changping District, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Received: 3 September 2018 / Revised: 8 September 2018 / Accepted: 10 September 2018 / Published: 14 September 2018
(This article belongs to the Section Energy Sustainability)
Full-Text   |   PDF [1777 KB, uploaded 14 September 2018]   |  

Abstract

Accurate load forecasting can help alleviate the impact of renewable-energy access to the network, facilitate the power plants to arrange unit maintenance and encourage the power broker companies to develop a reasonable quotation plan. However, the traditional prediction methods are insufficient for the analysis of load sequence fluctuations. The economic variables are not introduced into the input variable selection and the redundant information interferes with the final prediction results. In this paper, a set of the ensemble empirical mode is used to decompose the electricity consumption sequence. Appropriate economic variables are as selected as model input for each decomposition sequence to model separately according to its characteristics. Then the models are constructed by selecting the optimal parameters in the random forest. Finally, the result of the component prediction is reconstituted. Compared with random forest, support vector machine and seasonal naïve method, the example results show that the prediction accuracy of the model is better than that of the contrast models. The validity and feasibility of the method in the monthly load forecasting is verified. View Full-Text
Keywords: ensemble empirical mode decomposition; random forest; support vector machine; monthly load forecasting; economic influence ensemble empirical mode decomposition; random forest; support vector machine; monthly load forecasting; economic influence
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, D.; Sun, K.; Huang, H.; Tang, P. Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory. Sustainability 2018, 10, 3282.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top