Ibrahim Aliyu¹, Tai-Won Um², Sang-Joon Lee³, Chang Gyoon Lim^4,*, Jinsul Kim^1,*

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

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 to its nominal computational complexity,… More >

Peng Zhao¹, Yongxin Zhang¹, Qiaozhi Hua^2,*, Haipeng Li³, Zheng Wen⁴

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

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 high-energy load and other demand-side… More >