A Reliable Short‐Term Power Load Forecasting Method Based on <scp>VMD‐IWOA‐LSTM</scp> Algorithm

Zhuang, Zhiyuan; Zheng, Xidong; Chen, Zixing; Jin, Tao

doi:10.1002/tee.23603

Cited by 18 publications

(7 citation statements)

References 25 publications

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“…The water source of Ha Cai Tou Dang has a moderate continental monsoon climate, characterized by average annual temperatures ranging from 6.0 to 8.5 • C, precipitation levels of 220-400 mm, evaporation rates of 2100-2600 mm, and an average annual wind speed of 4.5 m/s. The climate of the Ha Cai Tou Dang water source area is characterized by significant evaporation, a fluctuating distribution of precipitation, intense solar radiation, windy and sandy conditions, and aridity [29].…”

Section: Study Areamentioning

confidence: 99%

“…While the secondary decomposition method can further refine the data and optimize the prediction model, most current studies are still limited to the primary decomposition, which results in residual components that still contain a significant amount of non-stationarity despite the decomposition technique's effectiveness in reducing non-stationarity and complexity of the data [28]. However, the decomposed characteristics of the current quadratic decomposition models are not thoroughly analyzed, which results in inadequate information extraction and lowers prediction accuracy [29]. Our goal is to improve these decomposition techniques so that vegetation change may be predicted with greater accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

Zheng,

Hou,

et al. 2024

Agronomy

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To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study’s findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

Zheng,

Hou,

et al. 2024

Agronomy

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“…Digital networking consists of two parts: network digitization in physical space and network intelligence in digital space. Both the State Plan and China Southern Energy have begun developing digital energy plans and have made some progress [1] . Digital Twin (DT) is the core of digital networking technology, but its definition continues to evolve.…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Wang,

TAN,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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In order to understand the multidimensional situation prediction of digital twin active power grid, a research on multidimensional situation prediction of digital twin active power grid based on LSTM algorithm is proposed. In this paper, firstly, a multi-dimensional situation prediction algorithm of power grid key indicators based on LSTM is established to realize the change prediction of key indicators attributes of digital twin active power grid. Secondly, the data of several key indicators such as load characteristics are collected, and a multi-dimensional system prediction model is established, which can control the state of active power grid; The LSTM prediction algorithm is proposed to fit the characteristics of multi-dimensional data, and the next stage of multi-dimensional data prediction is mapped to the power digital twin, so as to realize the synchronous implementation and intelligent regulation of smart energy system operation planning. Finally, a simulation test model is established, and an example shows that the multi-dimensional situation prediction method of digital twin power grid based on deep learning can better predict and distinguish the power grid situation, and provide decision support for accurate planning of energy system in the future.

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“…References [13,14] have used LSTM for power load forecasting in their research. Reference [15] used the power load data after PCC for short-term power load prediction using the LSTM network and achieved high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Power Load Prediction Based on IGWO‐BILSTM Network

Wang

Liu

2023

Mathematical Problems in Engineering

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Recently, neural networks based on intelligent algorithms have been widely used in short-term power load prediction. However, these algorithms have poor reproducibility in the case of repetition. Aiming at the shortcomings of the gray wolf optimizer (GWO) algorithm, such as slow convergence speed and easy to fall into local optimum, an improved gray wolf optimizer (IGWO) was proposed. In order to better extract features from time series data, the improved grey wolf optimizer-bidirectional long short-term memory (IGWO-BILSTM) power load prediction model is established by bidirectional long short-term memory (BILSTM). Pearson correlation analysis uses the actual power load data set of a certain region and selects the highly correlated factors as the input of the network. The BILSTM model was used to train the preprocessed data, the IGWO algorithm was used to find the optimal solution parameters during the training, and finally, the optimal prediction results were output to the test set. The experimental results show that compared with the prediction model established by the traditional optimization algorithm, the mean absolute percentage error (MAPE) value of the IGWO-BILSTM model is 1.63, the root mean square error (RMSE) value is 1.47, and the mean absolute error (MAE) value is 1.17. The convergence speed is faster and the prediction result is more accurate. The accuracy of multidimension load forecasting is greatly improved.

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A Reliable Short‐Term Power Load Forecasting Method Based on VMD‐IWOA‐LSTM Algorithm

Cited by 18 publications

References 25 publications

Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Power Load Prediction Based on IGWO‐BILSTM Network

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