Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Qian Zhang, Yuan Ma, Guoli Li, Jinhui Ma and Jinjin Ding

Mathematical Problems in Engineering, 2020, vol. 2020, 1-9

Abstract:

In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful in the reasonable planning and stable operation of the system in advance. For this purpose, a short-term load forecasting model based on frequency domain decomposition and deep learning is proposed. The original load data are decomposed into four parts as the daily and weekly periodic components and the low- and high-frequency components. Long short-term memory (LSTM) neural network is applied in the forecasting for the daily periodic, weekly periodic, and low-frequency components. The combination of isolation forest (iForest) and Mallat with the LSTM method is constructed in forecasting the high-frequency part. Finally, the four parts of the forecasting results are added together. The actual load data of a Chinese city are researched. Compared with the forecasting results of empirical mode decomposition- (EMD-) LSTM, LSTM, and recurrent neural network (RNN) methods, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of forecasting and actual values.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/7240320.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/7240320.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7240320

DOI: 10.1155/2020/7240320

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().