Fault Injection for TensorFlow Applications

Narayanan, Niranjhana; Chen, Zitao; Fang, B.; Li, Guanpeng; Pattabiraman, Karthik; DeBardeleben, Nathan

doi:10.1109/tdsc.2022.3175930

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…It can provide how well the model fits the data overall, how much it deviates from the data on average, and how sensitive it is to outliers and variations. By assessing the overall model using the metrics of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) about the genuine values, the accuracy of the model can be determined [11]. The calculation is based on the formula given:…”

Section: Model Evaluationmentioning

confidence: 99%

Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

Muhammad Aiqal Iskandar,

Muhammad Azfar Shamil Abd Aziz,

S. S. Sivaraju

et al. 2024

ARFMTS

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Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

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Section: Model Evaluationmentioning

confidence: 99%