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Access Right: info:eu-repo/semantics/openAccessSubject: Artificial neural networks

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    Article
    Citation - WoS: 8
    Citation - Scopus: 11
    A Study of Load Demand Forecasting Models in Electricity Using Artificial Neural Networks and Fuzzy Logic Model
    (Materials & Energy Research Center-merc, 2022) Al-ani, B. R. K.; Erkan, E. T.
    Since load time series are very changeable. demand forecasting of the short-term load is challenging based on hourly, daily, weekly, and monthly load forecast demand. As a result, the Turkish Electricity Transmission Company (TEA) load forecasting is proposed in this paper using artificial neural networks (ANN) and fuzzy logic (FL). Load forecasting enables utilities to purchase and generate electricity, load shift, and build infrastructure. A load forecast was classified into three sorts (hourly, weekly and monthly). Over time, forecasting power loads with artificial neural networks and fuzzy logic reveals a massive decrease in ANN and a progressive increase in FL from 24 to 168 hours. As illustrated, fuzzy logic and artificial neural netANorks outperform regression algorithms. This study has the highest growth and means absolute percentage error (MAPE) rates compared to FL and ANN. Although regression has the highest prediction growth rate, it is less precise than FL and ANN due to their lower MAPE percentage. Artificial Neural Networks and Fuzzy Logic are emerging technologies capable of forecasting and mitigating demand volatility. Future research can forecast various Turkish states using the same approach.
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    Article
    Citation - WoS: 9
    Citation - Scopus: 11
    Using Deep Learning Approaches for Coloring Silicone Maxillofacial Prostheses: a Comparison of Two Approaches
    (Wolters Kluwer Medknow Publications, 2023) Kurt, Meral; Kurt, Zuhal; Isik, Sahin
    Aim: This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses. Settings and Design: This was an in vitro study. Materials and Methods: A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the LFNx01, aFNx01, and bFNx01 values were recorded. The relationship between the LFNx01, aFNx01, and bFNx01 values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. LFNx01, aFNx01, and bFNx01 values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated. Statistical Analysis Used: Data were analyzed with the Student t-test (alpha=0.05). Results: The mean RMSE values and MAE values for the ANN algorithm (0.029 & PLUSMN; 0.0152 and 0.045 & PLUSMN; 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 & PLUSMN; 0.0005 and 0.002 & PLUSMN; 0.0008, respectively) (P < 0.001). Conclusions: Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.