Error identification strategies for Python Jupyter notebooks

Robinson, Derek J. S.; Ernst, Neil A.; Vargas, Enrique Larios; Storey, Margaret-Anne

doi:10.1145/3524610.3529156

Cited by 3 publications

(1 citation statement)

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“…Colab has integrated Python 3 modules used in building our models. Some examples of used modules are Pandas, Keras, Numpy, and Sklearn [7]. Python is a powerful tool used for its capabilities to work with machine learning models, incorporating advanced NN [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

et al. 2023

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Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.

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Section: Introductionmentioning

confidence: 99%