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  • Open Access

    ARTICLE

    Peak Shaving Strategy of Concentrating Solar Power Generation Based on Multi-Time-Scale and Considering Demand Response

    Lei Fang*, Haiying Dong, Xiaofei Zhen, Shuaibing Li

    Energy Engineering, Vol.121, No.3, pp. 661-679, 2024, DOI:10.32604/ee.2023.029823

    Abstract According to the multi-time-scale characteristics of power generation and demand-side response (DR) resources, as well as the improvement of prediction accuracy along with the approaching operating point, a rolling peak shaving optimization model consisting of three different time scales has been proposed. The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination, generation power, and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response… More >

  • Open Access

    ARTICLE

    Conveyor Belt Detection Based on Deep Convolution GANs

    Xiaoli Hao1,*, Xiaojuan Meng1, Yueqin Zhang1, Jindong Xue2, Jinyue Xia3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 601-613, 2021, DOI:10.32604/iasc.2021.017963

    Abstract The belt conveyor is essential in coal mine underground transportation. The belt properties directly affect the safety of the conveyor. It is essential to monitor that the belt works well. Traditional non-contact detection methods are usually time-consuming, and they only identify a single instance of damage. In this paper, a new belt-tear detection method is developed, characterized by two time-scale update rules for a multi-class deep convolution generative adversarial network. To use this method, only a small amount of image data needs to be labeled, and batch normalization in the generator must be removed to avoid artifacts in the generated… More >

  • Open Access

    ARTICLE

    Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model

    Mohamed F. El-Amin1,5, Abdulhamit Subasi2, Mahmoud M. Selim3,*, Awad Mousa4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2349-2362, 2021, DOI:10.32604/cmc.2021.017102

    Abstract In the process of oil recovery, experiments are usually carried out on core samples to evaluate the recovery of oil, so the numerical data are fitted into a non-dimensional equation called scaling-law. This will be essential for determining the behavior of actual reservoirs. The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data. This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity, which dynamics of the process. One of the practical machine learning (ML)… More >

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