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 >

Xueqing Lu, Yuetao Shi^*, Jinsong Li

Energy Engineering, Vol.120, No.6, pp. 1325-1352, 2023, DOI:10.32604/ee.2023.026142

Abstract In this study, a model of combined cooling, heating and power system with municipal solid waste (MSW) and liquefied natural gas (LNG) as energy sources was proposed and developed based on the energy demand of a large community, and MSW was classified and utilized. The system operated by determining power by heating load, and measures were taken to reduce operating costs by purchasing and selling LNG, natural gas (NG), cooling, heating, and power. Based on this system model, three operation strategies were proposed based on whether MSW was classified and the length of kitchen waste fermentation time, and each strategy… More > Graphic Abstract