阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优...阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优化(proximal policy optimization,PPO)算法求解CIES低碳优化调度问题。该方法基于低碳优化调度模型搭建强化学习交互环境,利用设备状态参数及运行参数定义智能体的状态、动作空间及奖励函数,再通过离线训练获取可生成最优策略的智能体。算例分析结果表明,采用PPO算法得到的CIES低碳优化调度方法能够充分发挥阶梯式碳交易机制减少碳排放量和提高能源利用率方面的优势。展开更多
Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise...Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.展开更多
为了探索指挥信息系统(CIS,Command Information System)网络与作战体系的内在关系,构建了由组织关系层、信息交互层和通信链路层组成的作战体系分层模型。定义了反映网络结构对作战体系影响的体系耦合强度概念,以体系耦合强度与成本系...为了探索指挥信息系统(CIS,Command Information System)网络与作战体系的内在关系,构建了由组织关系层、信息交互层和通信链路层组成的作战体系分层模型。定义了反映网络结构对作战体系影响的体系耦合强度概念,以体系耦合强度与成本系数比值最大化为目标,建立了网络结构优化模型。设计了一种路径规划遗传算法,针对空中进攻作战(AOC,Air Offensive Campaign)系统网络进行了仿真。仿真结果表明,模型和算法可对网络结构进行优化。展开更多
文摘Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.
文摘为了探索指挥信息系统(CIS,Command Information System)网络与作战体系的内在关系,构建了由组织关系层、信息交互层和通信链路层组成的作战体系分层模型。定义了反映网络结构对作战体系影响的体系耦合强度概念,以体系耦合强度与成本系数比值最大化为目标,建立了网络结构优化模型。设计了一种路径规划遗传算法,针对空中进攻作战(AOC,Air Offensive Campaign)系统网络进行了仿真。仿真结果表明,模型和算法可对网络结构进行优化。
