Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

Jiajie, Tang; Zhao, Jie; Zou, Hongliang; Ma, Guang‐Lei; Wu, Jun; Jin, Xu; Zhang, Huaixun

doi:10.3390/su131910526

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Therefore, the same parameters as for LSTM in Table 1 are also used for Bi‐LSTM. Bi‐LSTM network is derived as the cyclic bidirectional architecture of the traditional LSTM network [55]. This architecture splits traditional LSTM in two directions, but there is a link to the same output layer for both LSTMs.…”

Section: Methodsmentioning

confidence: 99%

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Akil

Dokur

Bayındır

2022

IET Renewable Power Gen

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The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi-state and high-frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short-Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short-Term Memory is applied to forecast for each sub-component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data-driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model.

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Section: Methodsmentioning

confidence: 99%

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Akil

Dokur

Bayındır

2022

IET Renewable Power Gen

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“…Secondly, in the comparison of the latest prediction models, this paper uses the VMD-Bi-LSTM prediction model proposed by Tang [33], the VMD-CISSA-LSSVM prediction model proposed by Guijuan Wang [34] and the VMD-GWO-SVR prediction model proposed by Mengran Zhou [35] to compare with the VMD-SG-LSTM. Each parameter is set according to the value in the article to predict the power load in the next hour.…”

Section: Comparison Of Prediction Modelsmentioning

confidence: 99%

Short-Term Power Load Prediction Based on VMD-SG-LSTM

Sun

Cai

2022

IEEE Access

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Power load prediction plays an important role in the safety and stability of national power system. However, due to the nonlinear and multi-frequency characteristics of the power system itself, power load prediction is difficult. To address this problem, we propose a short-term power load prediction model based on variational mode decomposition (VMD). First, original data are decomposed into intrinsic mode function (IMF) of different frequencies using the VMD algorithm, and the decomposed sub-functions are reconstructed. After smoothing the reconstructed data by Savitzky-Golay (S-G) filtering algorithm, the change trend of raw data (CTRD) is obtained. Then, IMF, CTRD and raw data are used as inputs to predict short-term power load by long short-term memory network (LSTM). Finally, the proposed prediction model is compared with the other two groups of prediction models. The results show that the proposed VMD-SG-LSTM prediction model has high fitting ability and high prediction accuracy, and is an effective method for short-term power load prediction.

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“…Nevertheless, the VMD algorithm also has limitations, including the need for manual parameter configuration and potential instability. In [24], a method based on VMD‐BiLSTM was introduced, in which the Bayesian optimization algorithm is utilized to optimize the hyperparameters of the model. Like EMD, this approach decomposes the original data into multiple components for individual predictions and subsequently combines them.…”

Section: Introductionmentioning

confidence: 99%

Short‐term electric load forecasting based on empirical wavelet transform and temporal convolutional network

Zhao,

Lin

2024

IET Generation Trans & Dist

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Short‐term load forecasting is the basis of power system operation and analysis and is of great significance for the stable operation of power systems. To solve the problems of insufficient mining of the intrinsic information of load data, randomness, and non‐linearity, and to improve the accuracy of load prediction, the authors propose a short‐term power load prediction model based on empirical wavelet transform (EWT) decomposition and the temporal convolutional network (TCN). First, the Pearson coefficient is used to eliminate the less relevant influencing factors to reduce the complexity of the model. Then, the EWT algorithm is used to decompose the original load signal to fully exploit the load data time domain and frequency domain information, while solving the problems of non‐linearity and randomness. Finally, the decomposed subsequences are input to the TCN network for prediction superposition to obtain the final results. The experimental results show that, compared with the single models TCN, gated recurrent units (GRU), and long short‐term memory (LSTM), and the hybrid models EWT‐GRU, EWT‐LSTM, and VMD‐TCN, the R2 of the short‐term power load forecasting model based on EWT‐TCN is improved by 0.2099%, 0.2519%, 0.3453%, 0.0334%, 0.1766%, and 0.1228%, respectively.

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Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

Cited by 8 publications

References 17 publications

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Short-Term Power Load Prediction Based on VMD-SG-LSTM

Short‐term electric load forecasting based on empirical wavelet transform and temporal convolutional network

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