从工业4.0到能源5.0:智能能源系统的概念、内涵及体系框架

分析推动工业进程和能源进程交互发展的因素和趋势, 结合能源互联网的发展要求, 提出了建立能源5.0的迫切性和必要性. 着重讨论了在网络化之后, 能源系统呈现的社会性问题, 认为在传统方式之外, 必须引入人类社会学、管理学等软科学进行分析建模; 指出了虚拟人工系统根本不同于传统仿真系统等理念, 只有利用虚拟人工模型, 采用平行系统, 才能建立能源5.0. 阐述了能源5.0的理论、框架和技术, 明确了能源5.0、基于社会物理信息系统(Cyber-physical-social system, CPSS) 的平行能源是等价的概念. 指出能源5.0核心是构建与实际能源系统同构的虚拟人工能源系统, 通过虚拟人工能源系统的计算实验, 确定优化控制策略, 引导实际能源系统运行, 并使虚拟人工系统和实际系统平行执行、共同演化, 形成智能能源系统. 最后以华电集团已经完成的分布式能源5.0示范项目和正在实施的火力发电5.0项目及智能家居能源系统, 探讨了能源5.0的研究内容、技术途径及应用前景.     

Research Progress of Parallel Control and Management

Based on ACP (artificial systems, computational experiments, and parallel execution) methodology, parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems. This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework, core technologies, and the application demonstration. The future research, application directions, and suggestions are also discussed.      

Parallel Building: A Complex System Approach for Smart Building Energy Management

These days’ smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5G, the ACP theory (i.e., artificial systems, computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.      

NSGA-II-Based Design Space Exploration for Energy and Throughput Aware Multicore Architectures

Multicore architectures are mainstream due to ever increasing demand of throughput by modern applications. However, the suboptimal utilization of available resources in these architectures may imply an inevitable energy overhead. This energy overhead can only be avoided if the multicore systems support reconfiguration of available resources as per application demand. To achieve the target objectives (i.e., Energy efficiency with Throughput maximization) in multicore systems, many decision variables need to be optimized or analyzed to find the better trade-off. Heuristic-based approaches are aimed to provide a good-enough solution instead of a lengthy exhaustive search. This paper presents an Evolutionary Algorithm (EA)-based approach, i.e., Nondominated Sorting Genetic Algorithm-II (NSGA-II). Three decision variables, i.e., number of cores, cache size and frequency are used to find best solution. The proposed approach is validated over a set of parallel benchmarks using a cycle accurate simulator. The results show a significant amount of energy saving along with minimal impact on the throughput of the system.     

并行计算系统可扩展性的研究

可扩展性是设计并行计算系统和并行算法所要考虑的一个重要性能指标。分析了等效率、等速度、平均延迟和等并行计算开销比几种并行系统可扩展性模型的特征,提出了一种新的更有效的可扩展性度量标准。通过实验结果分析,该模型能很好地评测并行计算系统的可扩展性。     

Parallel Factories for Smart Industrial Operations: From Big AI Models to Field Foundational Models and Scenarios Engineering

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence, internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems, QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called EuArtisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.      

平行系统和数字孪生的一种数据驱动形式表示及计算框架

旨在为平行系统及ACP方法建立一种数据驱动的数学形式和计算框架, 该形式与框架也适用于数字孪生系统.首先, 基于动态系统状态方程方法论, 给出了平行系统的虚实双系统表示方法, 基于此表示方法为平行系统问题提供了一种数学表示.围绕该表示, 讨论了虚实系统互动、平行系统与数字孪生系统异同等问题.然后, 为ACP方法提供了一种计算框架, 详细解释了人工系统(Artificial systems, A)、计算实验(Computational experiments, C)、平行执行(Parallel execution, P)的数学计算求解过程, 并讨论了“学习与训练”、“实验与评估”、“管理与控制”、灵捷–聚焦–收敛(AFC)、小数据-大数据-小智能等概念的相关数学表示, 并讨论了智能科学与平行系统数学架构的关系以及平行智能的内涵.最后, 以大学校园园区能源管理系统为案例, 为平行系统数学架构和方法提供一个直观的算例.     

A survey of recommender systems for energy efficiency in buildings: Principles,challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems’ performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors’ knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.     

Study on Parallel Computing

In this paper, we present a general survey on parallel computing. The main contents include parallel computer system which is the hardware platform of parallel computing, parallel algorithm which is the theoretical base of parallel computing, parallel programming which is the software support of parallel computing. After that, we also introduce some parallel applications and enabling technologies. We argue that parallel computing research should form an integrated methodology of ＂architecture algorithm programming application＂. Only in this way, parallel computing research becomes continuous development and more realistic.     

能耗并行加速比:高性能计算系统综合性能的有效度量

随着并行系统规模的扩大,高性能计算系统运行时消耗的能耗也在急剧增长,过高的能耗也给系统的可靠性、稳定性等方面带来严峻挑战。在这种情形下,能耗问题受到了前所未有的关注。因此,设计和研究高性能计算系统,需要在考虑高计算性能的同时兼顾系统低能耗的要求,这为高性能计算系统的度量模型提出了新的挑战。于是,大规模并行系统逐渐从"高性能"走向"高效能"的衡量标准。基于此,本文采用加速比度量指标,从系统可扩展角度将计算性能和能量消耗要素进行综合,提出了一种度量高性能计算系统综合性能的能耗并行加速比模型。该模型能够直观地反映并行计算系统的效能,旨在指导系统设计和应用研究。最后,通过对该模型的分析和模拟,验证了模型的有效性。     

智能控制：从学习控制到平行控制

20世纪60年代,学习控制开启了人类探究复杂系统控制的新途径,基于人工智能技术的智能控制随之兴起.本文以智能控制为主线,阐述其由学习控制向平行控制发展的历程.本文首先介绍学习控制的基本思想,描述了智能机器的架构设计与运行机理.随着信息科技的进步,基于数据的计算智能方法随之出现.对此,本文进一步简述了基于计算智能的学习控制方法,并以自适应动态规划方法为切入点分析非线性动态系统自学习优化问题的求解过程.最后,针对工程复杂性与社会复杂性互相耦合的复杂系统控制问题,阐述了基于平行控制的学习与优化方法求解思路,分析其在求解复杂系统优化控制问题方面的优势.智能控制思想经历了学习控制、计算智能控制到平行控制的演化过程,可以看出平行控制是实现复杂系统知识自动化的有效方法.     

Steps toward Parallel Intelligence

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the "new normal" of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS.      

一种更有效的并行系统可扩展性模型

文中首先分析了等效率、等速度和等并行开销计算比三种并行系统可扩展性模型的特点,论证了等效率、等速度和等并行开销计算比三种条件的等价性,并指出这三种模型在描描可扩展性时的不直观及其局限性。然后提出了一种新的可扩展性模型。此模型直观地反映出并行系统在机器规模和问题规模扩展时,其性能的扩展特性。实例研究表明,该模型能更有效地解决下列问题：（1）定量研究并行系统的可扩展性;（2）全面地反映程序、机器、环境方面的因素对可扩展性的影响;（3）指导如何保持并行系统的可扩展性。     

改善系统能量效率的体系结构方法:并行处理

因为对高性能微芯片和系统设计的广泛影响,能量消耗问题受到计算机界越来越广泛的关注.多个层次的技术被用于改善系统的能量效率,并行处理是体系结构层提高能量效率的主要手段.并行处理使用性能适中的计算节点减少能量消耗,使用多个节点并行执行维持高吞吐量.文中分析了并行处理提高能量效率的基本原理,给出了并行处理的时间开销和能量开销模型.基于模型分析,对低电压并行系统、动态电压调节(Dynamic Voltage Scaling,DVS)的并行系统和多核微处理器3个并行处理方向进行了展望,给出了这些并行处理方向改善能量效率的空间.     

核能5.0:智能时代的核电工业新形态与体系架构

本文旨在讨论核能5.0（Nuclear Energy 5.0）的基本概念、体系架构和关键平台技术等问题.首先讨论了核能5.0出现的新智能时代基础,阐述了虚拟数字工业崛起的技术背景.详细叙述了核电工业新形态与体系结构,即平行核能的定义、意义、研究内容、体系架构以及应用领域.接下来讨论了核能5.0中新一代核心技术,包括核能物联网、知识自动化、发展性人工智能、大规模协同演进技术、核能区块链等.最后讨论了核能5.0中在核电系统的具体应用场景与案例,重点是核电工控系统安全评估与核电站数字化仪控系统.     

Socially useful artificial intelligence

Artificial intelligence is presented as a set of tools with which we can try to come to terms with human problems, and with the assistance of which, some human problems can be solved. Artificial intelligence is located in its social context, in terms of the environment within which it is developed, and the applications to which it is put. Drawing on social theory, there is consideration of the collaborative and social problem-solving processes which are involved in artificial intelligence and society. In a look ahead to the coming generations of highly parallel computing systems, it is suggested that lessons can be learnt from the highly parallel processes of human social problem-solving.     

Agent-based modeling of ancient societies and their organization structure

Some of the most interesting questions one can ask about early societies, are about people and their relations, and the nature and scale of their organization. In this work, we attempt to answer such questions with approaches introduced by multiagent systems. Specifically, we developed a generic agent-based model (ABM) for simulating ancient societies. Unlike most existing ABMs used in archaeology, our model includes agents that are autonomous and utility-based. Our model can (and does) also incorporate different social organization paradigms and technologies used in ancient societies. Equipped with such paradigms, our model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics—i.e., the flexibility and evolution of power relationships depending on social context and time. As a case study, we employ our model to evaluate the impact of the implemented social and technological paradigms on an artificial Early Bronze Age “Minoan” society located at a particular region of the island of Crete. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate an impressive sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, and which was inspired by a recent framework for modern self-organizing agent organizations. This is the first time a self-organization approach is incorporated in an archaeology ABM system.     

iPACS: Power-aware covering sets for energy proportionality and performance in data parallel computing clusters

Energy consumption in datacenters has recently become a major concern due to the rising operational costs and scalability issues. Recent solutions to this problem propose the principle of energy proportionality, i.e., the amount of energy consumed by the server nodes must be proportional to the amount of work performed. For data parallelism and fault tolerance purposes, most common file systems used in MapReduce-type clusters maintain a set of replicas for each data block. A covering subset is a group of nodes that together contain at least one replica of the data blocks needed for performing computing tasks. In this work, we develop and analyze algorithms to maintain energy proportionality by discovering a covering subset that minimizes energy consumption while placing the remaining nodes in low-power standby mode in a data parallel computing cluster. Our algorithms can also discover covering subset in heterogeneous   computing environments. In order to allow more data parallelism, we generalize our algorithms so that it can discover k$k$-covering subset, i.e., a set of nodes that contain at least k$k$ replicas of the data blocks. Our experimental results show that we can achieve substantial energy saving without significant performance loss in diverse cluster configurations and working environments.