GMDH-Type Neural Network and Genetic Algorithm

Fallahi, Saeed; Shaverdi, Meysam; Bashiri, Vahab

doi:10.1142/9781848166110_0010

Cited by 1 publication

(1 citation statement)

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“…These relationships help the experts to make accurate and quick decisions and prevent damage to the equipment due to wrong or late decisions. Fallahi et al [19] have studied the prediction of the market settlement price for each competitive cluster of the Iranian market by using the genetic algorithm to perform the neural network training process. Finally, Chen et al [20] have modeled and forecasted the hourly demand of electricity consumption with the help of integrated self-correlated moving average models, taking into account the main and mutual effects.…”

Section: Introductionmentioning

confidence: 99%

Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers

Shaghaghi,

Omidifar,

Zahedi

et al. 2023

Therm. Sci. Eng.

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To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables to the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

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Section: Introductionmentioning

confidence: 99%