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  • Open Access

    ARTICLE

    Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

    Juan Li1, Tingting Xu1,*, Yi Gu2, Chuang Liu1, Guiping Zhou2, Guoliang Bian1

    Energy Engineering, Vol.121, No.10, pp. 2777-2795, 2024, DOI:10.32604/ee.2024.052331 - 11 September 2024

    Abstract In fossil energy pollution is serious and the “double carbon” goal is being promoted, as a symbol of fresh energy in the electrical system, solar and wind power have an increasing installed capacity, only conventional units obviously can not solve the new energy as the main body of the scheduling problem. To enhance the system scheduling ability, based on the participation of thermal power units, incorporate the high energy-carrying load of electro-melting magnesium into the regulation object, and consider the effects on the wind unpredictability of the power. Firstly, the operating characteristics of high energy… More >

  • Open Access

    ARTICLE

    Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

    Ibrahim Aliyu1, Tai-Won Um2, Sang-Joon Lee3, Chang Gyoon Lim4,*, Jinsul Kim1,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5947-5964, 2023, DOI:10.32604/cmc.2023.037202 - 29 April 2023

    Abstract In the quest to minimize energy waste, the energy performance of buildings (EPB) has been a focus because building appliances, such as heating, ventilation, and air conditioning, consume the highest energy. Therefore, effective design and planning for estimating heating load (HL) and cooling load (CL) for energy saving have become paramount. In this vein, efforts have been made to predict the HL and CL using a univariate approach. However, this approach necessitates two models for learning HL and CL, requiring more computational time. Moreover, the one-dimensional (1D) convolutional neural network (CNN) has gained popularity due… More >

  • Open Access

    ARTICLE

    Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

    Peng Zhao1, Yongxin Zhang1, Qiaozhi Hua2,*, Haipeng Li3, Zheng Wen4

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 957-979, 2023, DOI:10.32604/cmes.2022.021783 - 31 August 2022

    Abstract Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of More >

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