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A comprehensive optimization framework for the design of high-performance building systems

JOURNAL OF BUILDING ENGINEERING 2023年 65卷

作者： Zeng, Zhaoyun Lu, Di Hu, Yuqing Augenbroe, Godfried Chen, Jianli Argonne Natl Lab Lemont IL 60439 USA China Acad Bldg Res Beijing Peoples R China Penn State Univ State Coll PA 16801 USA Georgia Inst Technol Atlanta GA 30332 USA Univ Utah Salt Lake City UT 84112 USA

Improving the performance of buildings is crucial to achieving the Paris Agreement's goal of limiting global warming to 1.5 degrees C. optimization is critical to ensuring that buildings achieve the desired performance. In the literature, a systematic discussion on the interdependences between the objective functions, constraints, and design variables is missing. In this work, we try to fill this research gap by presenting a comprehensive optimization framework for the design of high-performance building systems. This framework provides a systematic and detailed introduction to each step of formulating and solving optimization problems. Two application studies are also presented to demonstrate how this optimization framework can be used in practical applications. The first study demonstrates how to construct cost models and handle optimization problems with economy objective functions, and the second study demonstrates how to solve multi-objective optimization problems with a-posteriori-articulation methods to generate the Pareto optimal set. This paper can serve as a guide for future researchers and designers by providing them with a basic problem-solving procedure and helping them avoid common mistakes.

关键词： Formulation optimization algorithm Multi-objective optimization Design variable Constraint Adaptive building envelope

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Optimizing Distributed Generation Resources for Power Grid Quality Improvement Using Hybrid optimization Technique

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年

作者： Thanasingh, Sengolrajan Sasikumar, A. N. Mahendran, K. Saranya, R. Kongunadu Coll Engn & Technol Autonomous Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India Rajalakshmi Inst Technol Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India Kongunadu Coll Engn & Technol Autonomous Dept Elect & Elect Engn Trichy 621215 Tamil Nadu India

Distributed generation (DG) integration is a critical requirement in modern distribution networks to enhance power grid quality. An essential aspect of this endeavor involves strategically placing and sizing various DG resources optimally. The primary objective of this study is to determine an efficient approach for allocating Photovoltaic (PV) and Wind Turbine resources to minimize overall power losses within the grid. To achieve this goal, we introduce a novel hybrid optimization technique that combines Particle Swarm optimization (PSO) with Genetic algorithms (GAs). Furthermore, we explore the influence of factors such as variations in PV daily consumption, load profile fluctuations, penetration levels, and changing climatic conditions on the optimization process across two comprehensive case studies presented in this research. Our modeling results reveal that the hybrid PSO-GA approach outperforms traditional PSO in terms of integration speed, power loss reduction, and enhancements to grid quality, including voltage and frequency profiles. Additionally, this study highlights the dynamic impact of load curve fluctuations and climate changes on the optimal location and capacity of DG resources, leading to a substantial reduction in grid power losses.

关键词： wind turbines solar PV optimization algorithm genetic algorithm and optimum placement of RES

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Revolutionizing Connectivity: Exploring Blockchain Integration in Advanced Communication Networks 1st

Revolutionizing Connectivity: Exploring Blockchain Integrati...

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1st International Conference on Smart Environment and Green Technologies, ICSEGT 2024

作者： Sadıkoğlu, Fahreddin Sabuncu, Özlem Bilgehan, Bülent Department of Electrical and Electronic Engineering Near East University Nicosia Mersin Turkey Odlar Yurdu University Baku Azerbaijan Nicosia Mersin Turkey

ISBN: (纸本)9783031815638

The paper explores the critical intersection of road safety for electric vehicles and the demand for rapid and reliable data flow. Addressing this challenge, it leverages the capabilities of sixth-generation (6G) networks, which are ultra-reliable and offer hyper-speed and minimal latency communication. Integrating blockchain technology introduces a widely accepted decentralized data management system that is explored to ensure data reliability. The study adopts an optimal path with stringent signal-to-interference noise constraints, achieving error-free transmission for large datasets at a high transmission rate. The proposed optimization strategy begins with edge computers at partnering locations serving as storage, resolving the initial level of data management. The work also introduces a Cellular Vehicle-to-Everything (C-V2X) network solution, addressing the second tier of economic activity. The blockchain network introduced in this article not only revolutionizes communications in electric vehicle networks but also aligns with green technology principles, creating a smart environment that prioritizes energy efficiency, reduced emissions, and a sustainable future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 6G Blockchain Network C-V2X Green Technology optimization algorithm Smart Environment

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Optimum Operating Parameters and Blade Setting of a High-Speed Propeller

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JOURNAL OF AIRCRAFT 2022年第2期59卷 484-501页

作者： Boulkeraa, Tayeb Ghenaiet, Adel Benini, Ernesto Polytech Mil Sch Fac Mech Engn & Proc Engn Lab Energy Convers Syst Bordj El Bahri 16046 Algeria Univ Sci & Technol Houari Boumediene Lab Turbomachinery Fluid Mech & Energet Dept Bab Ezzouar 16111 Algeria Univ Padua Dept Ind Engn Via Venezia 1 I-35131 Padua Italy

Traditionally, the design of propellers is accomplished on the bases of engineering experience and the availability of well-established experimental data. However, the emergence of computational fluid dynamics (CFD) tools coupled with advanced optimizers has made this task easier. This paper presents the essentials of an optimization-based CFD chain aimed at finding the optimal operating parameters and blade settings of a high-speed propeller. This fully automated procedure combines an improved hypercube sampling (IHS) for the initial design of experiment and a radial basis function (RBF) as the meta-model, hence leading to lesser CFD simulations. The coupling between IHS and RBF is judiciously implemented, and the sampling strategy and updates are thoroughly examined. The optimization procedure uses the multi-objective evolutionary algorithm nondominated sorting genetic algorithm to determine the operating parameters and blade setting that maximize the propeller efficiency and thrust. At the optimum point the blade operates with mild pressure gradients, whereas the transonic region is restricted to the upper corner of the blade tip.

关键词： High Speed Propellers Large Eddy Simulation Multi Objective Evolutionary algorithms Genetic algorithm Design of Experiments Freestream Conditions Reynolds Averaged Navier Stokes Blade Element Momentum Theory Aerodynamic Efficiency optimization algorithm

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Research on Operational Risk Monitoring Method of Intelligent Financial System Based on Deep Learning and Improved RPA 6th

Research on Operational Risk Monitoring Method of Intelligen...

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6th European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Yuan, Liang Zhu, Hui State Grid Huitong Jincai Beijing Informat Techno Beijing 100031 Peoples R China

ISBN: (纸本)9783031287862;9783031287879

The accuracy of traditional financial system operational risk monitoring is low. Therefore, this paper proposes a method of intelligent financial system operational risk monitoring based on deep learning and improved RPA. Set the financial monitoring index and obtain the warning threshold parameters;Using deep neural network method to mine key risk indicators and obtain reconstruction coefficients of data mining errors of financial system;By improving the RPA method to calculate the fit degree of financial risk, it matches the internal business process of the enterprise;The operational risk monitoring algorithm of financial system is designed to realize the operational risk monitoring of financial system. The experimental results show that the risk monitoring accuracy of the design method is 80.3%, and the overall test threshold of the model is 0.5 after the introduction of non-financial indicators, which shows that it can be applied in practice.

关键词： Deep learning Improve RPA Intelligent financial system Operational risk monitoring optimization algorithm

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A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Bi, Ziqiang Chu, Guanying Pan, Xinyu Guo, Jichong Gu, Minming Ban, Jianmin Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou Peoples R China

Solar irradiance is a crucial environmental parameter for optimal control of photovoltaic (PV) systems. However, precise measurements of the solar irradiance are difficult since the irradiation sensors (i.e., pyranometer or pyrheliometer) are expensive and hard to calibrate. This paper proposes a cost-effective and accurate method for estimating the solar irradiance with a PV module via curve fitting. A dual-mode Jaya (DM-Jaya) optimization algorithm is introduced to extract the real-time value of solar irradiance from the measured PV characteristics data by using two search strategies. The step sizes of a random walk are taken from even and Levy distribution distributions in different searching phases. Compared with the traditional irradiance sensors, the proposed estimator does not require additional circuit and obtains relatively lower error rates. A comparative study of seven population-based optimization algorithms for the optimal design of the estimator is presented. These algorithms include particle swarm optimization (PSO), cuckoo search (CS), Jaya, simulated annealing (SA), genetic algorithm (GA), supply-demand-based optimization (SDO), and the proposed DM-Jaya algorithm. Simulations and experimental results reveal that DM-Jaya outperforms the other optimization algorithms in terms of the estimation speed and accuracy.

关键词： solar irradiance optimization algorithm Jaya algorithm DM-Jaya photovoltaics

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Multimodal critical-scenarios search method for test of autonomous vehicles

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Journal of Intelligent and Connected Vehicles 2022年第3期5卷 167-176页

作者： Tianyue Feng Lihao Liu Xingyu Xing Junyi Chen Tongji University ShanghaiChina

Purpose–The purpose of this paper is to search for the critical-scenarios of autonomous vehicles(AVs)quickly and comprehensively,which is essential for verification and validation(V&V).Design/methodology/approach–The author adopted the index F1 to quantitative critical-scenarios’coverage of the search space and proposed the improved particle swarm optimization(IPSO)to enhance exploration ability for higher *** with the particle swarm optimization(PSO),there were three *** the initial phase,the Latin hypercube sampling method was introduced for a uniform distribution of *** the iteration phase,the neighborhood operator was adapted to explore more modals with the particles divided into *** the convergence phase,the convergence judgment and restart strategy were used to explore the search space by avoiding local *** with the Monte Carlo method(MC)and PSO,experiments on the artificial function and critical-scenarios search were carried out to verify the efficiency and the application effect of the ***–Results show that IPSO can search for multimodal critical-scenarios comprehensively,with a stricter threshold and fewer samples in the experiment on critical-scenario search,the coverage of IPSO is 14%higher than PSO and 40%higher than ***/value–The critical-scenarios’coverage of the search space is firstly quantified by the index F1,and the proposed method has higher search efficiency and coverage for the critical-scenarios search of AVs,which shows application potential for V&V.

关键词： Autonomous driving system Virtual test Scenario optimization algorithm

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Fuzzy rule-based acceptance criterion in metaheuristic algorithms

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第9期34卷 7775-7789页

作者： Arik, Oguzhan Ahmet Nuh Naci Yazgan Univ Ind Engn Dept TR-38170 Kayseri Turkey

Metaheuristic algorithms are solution approaches to solve optimization problems by repeating some algorithmic steps while searching the solution space. The strategy of the metaheuristic includes two basic tactics;exploration for escaping the local optimum and exploitation for the global optimum. The number of solutions while exploring the solution space can be used to classify metaheuristics. If the metaheuristic uses only one solution to generate a new solution, we call it the single-solution-based metaheuristic. Simulated annealing, iterated local search, adaptive large neighborhood search, iterated greedy, local search, and tabu search are examples of single-solution-based metaheuristics. Most of these metaheuris-tics use an acceptance criterion to whether accept the newly generated solution instead of the incumbent solution to escape from the local optimal. The most used acceptance criterion in the literature is the Metropolis criterion or simulated annealing-like acceptance criterion that decides whether accept the new solution by calculating its acceptance probability. In this study, we propose a fuzzy rule-based acceptance criterion that fuzzies the inputs of the metaheuristic within a fuzzy inference system to create the decision output about the acceptance. The proposed new acceptance criterion is compared with the well-known probabilistic approach in the experimental study with traveling salesman problem, multidi-mensional knapsack problem, single machine weighted earliness/tardiness problem, linear regression problem, and two continuous optimization problems. The statistical analyses reveal that the number of acceptance solutions while using the proposed acceptance criterion is less than the probabilistic one has but the metaheuristic performance increases with the proposed criterion. Additional analyses are made to explain why the fuzzy criterion convergences better to the global optimum than the probabilistic criterion.(c) 2021 The Author. Published by

关键词： Acceptance criterion Fuzzy Metaheuristic Simulated annealing optimization algorithm

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Multi-population evolutionary computing based multi-agent smart distribution system service restoration

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ELECTRICAL ENGINEERING 2022年第5期104卷 3295-3311页

作者： Lin, Yu-Hsiu Natl Taipei Univ Technol Grad Inst Automat Technol Taipei 106344 Taiwan

Distribution system service restoration is a process of restoring power outages through changing the on/off status of sectionalizing and tie switches within the whole power outage in a distribution system altered accordingly with its topological structure. Multi-agent Systems (MASs) can be applied for distribution system service restoration, an engineering optimization problem that can be addressed through metaheuristics, of a distribution system because service restoration planning can be made in parallel among intelligent software agents embedded inside sectionalizing and tie switches and feeders to form a distributed multi-agent environment such that the time of power restoration can be reduced. Service restoration planning can be built upon an MAS. This paper presents a three-tiered MAS-based Multi-Population Parallel Genetic algorithm (MPPGA) and demonstrates its preliminary implementation to achieve service restoration planning for smart distribution system service restoration, which serves as a meta service restoration planner to perform efficient and fast switching operation for smart distribution system service restoration. The presented three-tiered MAS-based MPPGA is implemented, in Java programming language, on a Java Agent DEvelopment (JADE for short) platform, where in evolutionary computation (1) multiple populations are coevolved and (2) selection, crossover, and mutation operations for genetic search are parallelized. The effectiveness/feasibility of the presented three-tiered MAS-based MPPGA is demonstrated by a simulated distribution system and reported with simulation results.

关键词： Artificial intelligence Distribution feeder reconfiguration JADE Multi-agent system optimization algorithm Smart grid

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An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

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SOFT COMPUTING 2022年第24期26卷 13969-13984页

作者： Asadollah, Seyed Babak Haji Seyed Sharafati, Ahmad Haghbin, Masoud Motta, Davide Noghani, Mohamadreza Hosseinian Moghadam SUNY Coll Environm Sci & Forestry Dept Environm Resources Engn Syracuse NY 13210 USA Univ Granada UGR Andalusian Res Inst Data Sci & Computat Intellige Dept Struct Mech & Hydraul Engn Granada 18001 Spain Northumbria Univ Dept Mech & Construct Engn Wynne Jones Bldg Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Soft computing (SC) methods have increasingly been used to solve complex hydraulic engineering problems, especially those characterized by high uncertainty. SC approaches have previously proved to be an accurate tool for predicting the aeration efficiency coefficient (E20) in hydraulic structures such as weirs and flumes. In this study, the performance of the standalone support vector regression (SVR) algorithm and three of its hybrid versions, support vector regression-firefly algorithm (SVR-FA), support vector regression-grasshopper optimization algorithm (SVR-GOA), and support vector regression-artificial bee colony (SVR-ABC), is assessed for the prediction of E20 in stepped cascades. Mutual information theory is used to construct input variable combinations for prediction, including the parameters unit discharge (q), the total number of steps (N), step height (h), chute overall length (L), and chute inclination (alpha) Entropy indicators, such as maximum likelihood, Jeffrey, Laplace, Schurmann-Grassberger, and minimax, are computed to quantify the epistemic uncertainty associated with the models. Four indices-correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE)-are employed for evaluating the models' prediction performance. The models' outputs reveal that the SVR-FA model (with R = 0:947;NSE = 0:888;RMSE = 0:048 and MAE = 0:027 in testing phase) has the best performance among all the models considered. The input variable combination, including q, N, h, and L, provides the best predictions with the SVR, SVR-FA, and SVR-GOA models. From the uncertainty analysis, the SVR-FA model shows the closest entropy values to the observed ones (3.630 vs. 3.628 for the "classic" entropy method and 3.647 vs. 3.643 on average for the Bayesian entropy method). This study proves that SC algorithms can be highly accurate in simulating aeration efficiency in stepped cascades and provide a valid alternative to the

关键词： Aeration efficiency Stepped cascades Support vector regression optimization algorithm

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