Short-term electricity load forecasting method based on multilayered self-normalizing GRU network

Lu, Kuan; Zhao, Yan; Wang, Xin; Yan, Cheng; Pang, Xiaolu; Sun, Wei; Jiang, Zhe; Zhang, Yong; Xu, Nan; Zhao, Xinhui

doi:10.1109/ei2.2017.8245330

Cited by 44 publications

(18 citation statements)

References 15 publications

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“…However, the vanishing gradient point is a problem for RNNs to improve the performance. To solve this problem, the long short-term memory (LSTM) and gated recurrent units (GRU), which variants of RNNs, have been proposed and perform well in long-term horizon forecasting based on the past data [25][26][27]. In [28],the proposed LSTM-based method is capable of forecasting accurately the complex electric load time series with a long forecasting horizon by exploiting the long-term dependencies.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

et al. 2018

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Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

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Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

et al. 2018

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“…Kuan et al [ 18 ] constructed a multilayered self-normalizing GRU model for STLF. They demonstrated that the multilayered self-normalizing technology could improve the prediction performance of the GRU and LSTM models through several experiments.…”

Section: Related Studiesmentioning

confidence: 99%

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Jung

Moon

Park

et al. 2021

Sensors

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Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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“…In [17] the authors propose the use of a multilayer recurrent neural network called MS-GRU to forecast load electricity time series. The results are compared with the traditional recurrent neural networks such as LSTM and GRU, showing greater precision in the proposal MS-GRU.…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Improving Long Short-Term Memory Predictions with Local Average of Nearest Neighbors

Flores¹,

Tito²,

Centty³

2019

IJACSA

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The study presented in this paper aims to improve the accuracy of meteorological time series predictions made with the recurrent neural network known as Long Short-Term Memory (LSTM). To reach this, instead of just making adjustments to the architecture of LSTM as seen in different related works, it is proposed to adjust the LSTM results using the univariate time series imputation algorithm known as Local Average of Nearest Neighbors (LANN) and LANNc which is a variation of LANN, that allows to avoid the bias towards the left of the synthetic data generated by LANN. The results obtained show that both LANN and LANNc allow to improve the accuracy of the predictions generated by LSTM, with LANN being superior to LANNc. Likewise, on average the best LANN and LANNc configurations make it possible to outperform the predictions reached by another recurrent neural network known as Gated Recurrent Unit (GRU).

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Short-term electricity load forecasting method based on multilayered self-normalizing GRU network

Cited by 44 publications

References 15 publications

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Improving Long Short-Term Memory Predictions with Local Average of Nearest Neighbors

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