Work

Research on urban power load forecasting based on improved LSTM

Year: 2024

Type: article

Abstract: In this paper, the maximal information coefficient method-variational mode decomposition-bidirectional long short term memory network-adaptive boosting (MIC-VMD-Bi-LSTM-Adaboost) algorithm is used to forecast the power load. Firstly, MIC is used to determine the correlation degree of meteorological parameters influencing power load. Features having a high correlation degree are then chosen to be input vectors. Secondly, the input characteristics are decomposed using VMD, and five distinct IMF components are retrieved in order to remove unnecessary information. Lastly, different assessment indices are computed and the power load is predicted using the Bi-LSTM-Adaboost method. In order to determine the benefit of the approach used in this work, the outcomes of LSTM, Bi-LSTM, and LSTM-Adaboost are compared concurrently.

Source: Frontiers in Energy Research

DOI: 10.3389/fenrg.2024.1443814

Authors: Zhou Zhenglei, Chen Jun, Yang Zhou, Wu Wenguang, Ding Hong

Institution: NARI Group (China)

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,795