Short-term Power Load Forecasting of Residential Community Based on GRU Neural Network

Zheng, Jiyue; Chen, Xingying; Yu, Kun; Gan, Lei; Wang, Yifan; Wang, Ke

doi:10.1109/powercon.2018.8601718

Cited by 37 publications

(22 citation statements)

References 7 publications

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“…Zheng et al [13] compared the performance of single and two-layer 20-node LSTM and gated recurrent unit (GRU) RNNs for forecasting residential community load using a dataset of hourly loads. The study found that GRU and LSTM networks gave similar accuracy but the former incurred shorter training time.…”

Section: Building Energy Consumption Predictionmentioning

confidence: 99%

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Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

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With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

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Section: Building Energy Consumption Predictionmentioning

confidence: 99%

“…The LSTM and GRU networks explored are all single or two-layer networks with up to a maximum of 512 nodes per layer. Only Zheng et al [13] and Shi et al [19] explored the use of long sequence lengths.…”

Section: Deep Learning In Energy Consumption Predictionmentioning

confidence: 99%

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

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“…Zheng et al proposed a short-term load forecasting method for residential community based on gated recurrent unit neural network. The simulation results show that the GRU is faster within the similar forecasting accuracy, compared with the LSTM network [22]. However, the RNN-based models can not achieve the satisfactory result due to the poor performance in peak values and valley values.…”

Section: Literature Reviewmentioning

confidence: 94%

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

Zhang

2020

IEEE Access

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Short-term load forecasting is a critical task in the smart grid, which can be used to optimize power deployment and reduce power losses. Recurrent neural networks (RNNs) are the most popular deep learning models for short-term load forecasting. However, despite of achieving better forecasting accuracy than the traditional models, the performance of the existing RNN-based load forecasting approaches is still unsatisfactory for practical usage. Therefore, in this work, we have proposed input attention mechanism (IAM) and hidden connection mechanism (HCM) to greatly enhance the accuracy and efficiency of RNNbased load forecasting models. Specifically, we use IAM to assign the importance weights on input layers, which have better performances in both efficiency and accuracy than traditional attention mechanisms. To further enhance the models' efficiency, HCM is then applied to utilize residual connections to enhance the model's converging speed. We have applied both IAM and HCM on four state-of-the-art RNN implementations, and then conducted extensive experimental studies on two public datasets. Experimental results show that the proposed RNNs with IAM and HCM models achieve much better performances than the state-of-the-art baselines in both accuracy and efficiency. Ablation studies show that both IAM and HCM are essential to achieve such superior performances.

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“…In [16] the authors propose the power load forecasting of residential community using Gated Recurrent Unit (GRU). The GRU results are compared with LSTM results in different configurations.…”

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 Power Load Forecasting of Residential Community Based on GRU Neural Network

Cited by 37 publications

References 7 publications

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

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

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