Prediction of the Stability of Various Tunnel Shapes Based on Hoek–Brown Failure Criterion Using Artificial Neural Network (ANN)

Jearsiripongkul, Thira; Keawsawasvong, Suraparb; Thongchom, Chanachai; Ngamkhanong, Chayut

doi:10.3390/su14084533

Cited by 21 publications

(1 citation statement)

References 67 publications

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“…Accurate electricity demand forecasting can not only guide the power company's generation capacity, but also alleviate the decision errors caused by information asymmetry during the gaming process, thus improving the accuracy of the pricing results and enhancing the quality of service to customers. Among the current electricity forecasting methods, the most common are traditional time series regression algorithms (e.g., autoregressive integrated moving average, Arima) and machine learning algorithms (e.g., neural networks, NN [26]). As early as 1951, the Arima model was proposed and applied to time series forecasting [27].…”

Section: Literaturementioning

confidence: 99%

Collaborative Energy Price Computing Based on Sarima-Ann and Asymmetric Stackelberg Games

Zhang

Chen

et al. 2023

Symmetry

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The energy trading problem in smart grids has been of great interest. In this paper, we focus on two problems: 1. Energy sellers’ inaccurate grasp of users’ real needs causes information asymmetry in transactions, making it difficult for energy sellers to develop more satisfactory pricing strategies for users based on those real needs. 2. The uneven variation of user demand causes the grid costs to increase. In this paper, we design a collaborative pricing strategy based on the seasonal autoregressive integrated moving average-artificial neural network (Sarima-Ann) and an asymmetric Stackelberg game. Specifically, we propose a dissatisfaction function for users and an incentive function for grid companies to construct a utility function for both parties, which introduces an incentive amount to achieve better results in equilibrating user demand while optimizing the transaction utility. In addition, we constructed a demand fluctuation function based on user demand data and introduced it into the game model to predict the demand by Sarima-Ann, which achieves better prediction accuracy. Finally, through simulation experiments, we demonstrate the effectiveness of our scheme in balancing demand and improving utility, and the superiority of our Sarima-Ann model in terms of forecasting accuracy. Specifically, the peak reduction can reach 94.1% and the total transaction utility increase can reach 4.6 × 107, and better results can be achieved by adjusting the incentive rate. Our Sarima-Ann model improves accuracy by 64.95% over Arima and 64.47% over Sarima under MAE metric evaluation, and also shows superior accuracy under other metrics evaluation.

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Section: Literaturementioning

confidence: 99%