Artificial intelligence‐based tri‐objective optimization of different demand load patterns on the optimal sizing of a smart educational buildings
Artificial intelligence‐based tri‐objective optimization of different demand load patterns on the optimal sizing of a smart educational buildings
复制标题
基于人工智能的不同需求负载模式的三目标优化对智能教育建筑最佳规模的影响
DOI：
10.1002/er.8095
复制
发表时间：
2022
影响因子：
4.6
通讯作者：
Najmeh Ansari
中科院分区：
文献类型：
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作者：
Paniz Hosseini;Sadegh Nikbakht Naserabad;Amir H. Keshavarzzadeh;Najmeh Ansari
在这项研究中，一个综合能源系统提供三重负荷的教育建筑已建模，分析和优化使用人工智能的使用不同的负荷供应模式的集成系统进行了优化和评估的角度火用，经济和环境。利用SketchUp、OpenStudio和EnergyPlus获取建筑物荷载，并利用MATLAB软件对集成系统进行动态建模。集成系统已根据不同的模式进行了优化，包括月度、季节性和双态恒定负荷，并使用人工智能遗传算法与小时动态负荷进行了比较。结果表明，动态负荷模式的火用效率最高，总费用率最低。该方案的火用效率和总费用率分别为58.78%和5.022 $/h。此外，二氧化碳排放指数已达到363.1克/千瓦时。由于电网电力的补贴价格，存在使用低容量燃气轮机并从电网购买电力用于动态模型的趋势。燃气涡轮机容量为35.56 kW，系统已从电网购买了242.4 MWh。由于高的热负荷，燃气涡轮机的容量显著增加，并且在固定负荷模式下不从电网购买电力。燃气涡轮机的最高容量为161.6 kW，这是通过季节性固定负载模式获得的。此外，994.9兆瓦时的电力以这种模式出售给电网。
In this research, an integrated energy system for providing triple loads of an educational building has been modeled, analyzed, and optimized using artificial intelligence The use of different load supply patterns for the integrated system was optimized and evaluated from the point of view of exergy, economics, and environment. SketchUp, OpenStudio, and EnergyPlus are used to obtain the building loads, and the integrated system is dynamically modeled by the MATLAB software. The integrated system has been optimized based on different patterns, including monthly, seasonal, and two‐state constant loads compared with hourly dynamic loads using an artificial intelligence genetic algorithm. The results show that the dynamic load pattern gives the highest exergy efficiency and the lowest total cost rate. The exergy efficiency and total cost rate in this scenario are 58.78% and 5.022 $/h, respectively. Also, the CO2 emission index has reached 363.1 g/kWh. Due to the subsidized price of grid electricity, there is a tendency to use low‐capacity gas turbines and buy electricity from the grid for the dynamic model. The gas turbine capacity is 35.56 kW, and the system has purchased 242.4 MWh from the grid. Due to high heating loads, the capacity of the gas turbine significantly increases, and no electricity is purchased from the grid in the fixed‐load pattern. The highest capacity of the gas turbine is 161.6 kW, which is obtained by a seasonal fixed‐load pattern. Also, 994.9 MWh of electricity is sold to the grid in this mode.