Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Djedidi, Oussama; Djeziri, Mohand

doi:10.1016/j.sysarc.2020.101805

Cited by 8 publications

(5 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te NARX neural network handles feedback efciently, and the output is a function of the historical data and the current input. Te NARX neural network is capable of feedback, delay, memory storage, and integration with historical data, so the predictive model can adapt to changes over time signal load [18]. Figure 2 shows the short bootstrap process based on CEEMDAN algorithm and NARX neural network.…”

Section: Short Term Load Forecasting Model Based On Ceemdanmentioning

confidence: 99%

Short-Term Load Monitoring of a Power System Based on Neural Network

Yang

2023

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and 0.343% lower than the SVM, RNN, and NARX models, respectively, and 3.741% and 2.682% lower than CEEMDAN-SVM and CEEMDAN-RNN, respectively. The MAPE and RMSE of the CEEMDAN-NARX model are 0.765% and 101.7 MW, respectively, which are 0.468% and 45.2 MW lower than NARX models, respectively. Compared to CEEMDAN-SVM, the MAPE of CEEMDAN-NARX and CEEMDAN-RNN decreased by 0.986% and 0.692%, respectively, and the RMSE of CEEMDAN-NARX decreased by 111.5 and 65.7 MW, respectively, compared to CEEMDAN-SVM. Conclusion is that the load forecasting model based on the combination of CEEMDAN algorithm and NARX neural network can effectively connect, reduce the negative impact of noise on forecasting results, and improve forecasting accuracy.

show abstract

Section: Short Term Load Forecasting Model Based On Ceemdanmentioning

confidence: 99%

Short-Term Load Monitoring of a Power System Based on Neural Network

Yang

2023

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…This was compared to the conventionally used Newton-Raphson method and showed a lower computational load with highly accurate results. Similarly, Djedidi and Djeziri in (2020) developed a new type of power estimator for ARM-based (Advanced RISC Machine) embedded systems with granularity at the level of components. This estimator was based on nonlinear autoregressive with eXogenous input (NARX) neural network and authors were able to achieve mean absolute percentage error (MAPE) of 2.2%.…”

Section: Use Of Neural Network In the Contextmentioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

View full text Add to dashboard Cite

Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

show abstract

“…More recently, several new models were also published, either for the system as a whole [28][29][30][31][32], or just the CPU [7,33]. Some of them even offered better accuracy [7,28,33] and proved that software profiling is as and reliable as the hardware counterpart [29,31].…”

Section: Power Consumption Modeling In Embedded Socsmentioning

confidence: 99%

“…Since this work is focused around the SoC, in order to minimize the influence of other system components, all communication and secondary peripherals Wi-Fi, modem, cameras, screen, etc.) were disabled and assumed to consume a static constant amount of power in that state [28,80]. The measured consumed power becomes:…”

Section: Power Modelmentioning

confidence: 99%

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Djedidi

Djeziri

2020

Processes

Self Cite

View full text Add to dashboard Cite

This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.

show abstract

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Cited by 8 publications

References 67 publications

Short-Term Load Monitoring of a Power System Based on Neural Network

Short-Term Load Monitoring of a Power System Based on Neural Network

Computational Intelligence in the Context of Industry 4.0

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Contact Info

Product

Resources

About