Short-Term Bus Load Forecasting Method Based on CNN-GRU Neural Network

Shen, Maoya; Xu, Qifeng; Wang, Kaijie; Tu, Mengfu; Wu, Bingxiang

doi:10.1007/978-981-13-9783-7_58

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…Further, the designed ANFIS model is based on the variation in various observational data and forecasted data from the proposed hybrid renewable energy system. The proposed model Science Progress 105 (4) includes two DERs inputs and MFs includes based on three input parameters. The FIS controller is assigned for maintains the constant load level with utilization of maximum demand by minimizing the ΔP demand deviation.…”

Section: Simulation Resultsmentioning

confidence: 99%

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

2022

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The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network [Formula: see text] is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

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Section: Simulation Resultsmentioning

confidence: 99%

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

2022

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“…For instance, in [47], authors employ 1D convnets and bidirectional GRUs for air pollution forecasting in Beijing, China. The other applications of GRU models in time series forecasting include personalized healthcare and climate forecasting [48], mine gas concentration forecasting [49], smart grid bus load forecasting [50]. In [51], authors present a CNN-based bagging model for forecasting hourly loads in a smart grid.…”

Section: A Deep Learning For Time Series Forecastingmentioning

confidence: 99%

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

Mode¹,

Hoque²

2020

Preprint

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Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safetycritical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance.

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“…The literature used a twolayer GRU neural network and made improvements in the construction of GRU models to obtain more accurate prediction results [7] . The literature improved the input data of GRU by going through convolutional neural networks to fuse multiscale feature vectors [8] . In the literature [9] ,Using Particle Swarm Optimization to optimize the parameters of GRU network showed a significant improvement over the previous GRU boosting algorithm in terms of artificially set network parameters and prediction accuracy of the network model.…”

Section: Introductionmentioning

confidence: 99%

Short-term power load forecasting based on IWOA-GRU

Zhao

Wang

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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This paper proposes a short-term load prediction model based on the improved whale optimization algorithm (IW0A) optimized gated recurrent neural network(GRU) to address the issue of strong unpredictability of electric load and low forecast accuracy. First, the whale population is initialized by S chaotic mapping to enhance the population diversity and improve the quality of the initial solution; second, a nonlinear convergence factor is proposed to balance the global and local search ability of the algorithm and improve the convergence speed in order to avoid the defects that the standard whale optimization algorithm is easy to fall into local optimum and slow convergence speed when solving the GRU parameter optimization problem. Finally, WOA is used to automatically determine the best parameters and create the IWOA-GRU load prediction model by optimizing the number of layer neurons, learning rate, and other factors. The results show that when compared to the prediction methods used by LSTM, GRU, PSO-GRU, RSO-GRU, and WOA-GRU, the proposed model may successfully increase convergence speed and prediction accuracy.

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Short-Term Bus Load Forecasting Method Based on CNN-GRU Neural Network

Cited by 12 publications

References 4 publications

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

Short-term power load forecasting based on IWOA-GRU

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