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Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review

BUILDING AND ENVIRONMENT 2023年第1期242卷

作者： Mousavi, SeyedehNiloufar Villarreal-Marroquin, Maria Guadalupe Hajiaghaei-Keshteli, Mostafa Smith, Neale R. Tecnol Monterrey Escuela Ingn & Ciencias Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico Tecnol Monterrey Escuela Ingn & Ciencias Puebla Mexico

Recent advances toward sustainable cities have promoted the concept of near-zero energy consumption. A Positive Energy Building (PEB) model has been developed by the European Union as part of Horizon 2020 to contribute to a cleaner neighborhood environment. To achieve PEB goals, a variety of factors must be optimized, including occupant comfort, building efficiency, economic benefits, and clean energy provision. Building modeling simulation combined with data-driven tools such as machine learning and artificial intelligence can be used to predict energy production and optimize passive and active systems. Based on these findings, this study evaluates studies from the past decade that include data-driven approaches, which accelerate different aspects of PEB, including supply and demand. These aspects include renewable energy supply prediction with the local context, optimizing comfort control with IoT, and reducing demand by optimizing building envelope design, materials selection, and active systems. While there are a few surveys regarding renewable energy management and energy efficiency in buildings, none simultaneously classified the algorithms in a PEB framework. Hence, this work inherently creates a technical framework for future researchers and building engineers to apply the appropriate data-driven approach for achieving net positive energy performance in residential, educational, and commercial buildings. Finally, comparing different applications suggests future research problems that can be addressed by integrating optimization algorithms and machine learning approaches, as well as data gaps that can be resolved to improve prediction accuracy.

关键词： Machine learning Positive energy building optimization algorithms Data-driven prediction Energy efficiency Renewable energy

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Super Twisting Sliding Mode Controller for Trajectory Tracking Control of Autonomous Ground Vehicle System 1

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10th EAI International Conference on Advancements of Science and Technology (EAI ICAST)

作者： Takele, Tamiru Terefe, Tefera Ma, Sam Sun Wolaita Sodo Univ Sodo Ethiopia Adama Sci & Technol Univ Elect Power & Control Engn Adama Ethiopia

ISBN: (数字)9783031287251

ISBN: (纸本)9783031287244;9783031287251

Day to day increase in demand of safe and accident free ground vehicle, rapid growth and development of artificial intelligence algorithms and also rapid growth of microelectronics technology are major motives that are driving the development and increased attention of Autonomous Ground Vehicle (AGV) systems. Unstable and non-linear features of AGV need robust control techniques to control the trajectory tracking tasks of the system. Review of related works summery shows that sliding mode controller can handle non-linearity and relatively assure robustness of the system. However;ripple is one of the most common challenge in sliding mode controllers. In this research, Super Twisting Sliding Mode controller (STSMC) is designed to resolve the ripple in sliding mode controller for trajectory tracking control of AGV. Optimal parameters of STSMC controller are tuned using Genetic Algorithm (GA) and Particle Swarm optimization (PSO) technique. To compare the performance of the proposed algorithm, GA tuned Fractional-Order-PID (FOPID) controller is also designed and implemented. Accordingly, STSMC has less (similar to 0.0006 s) tracking error than FOPID controller. The result reveals the outperformance of the proposed algorithm over FOPID controller.

关键词： Autonomous Ground Vehicle Trajectory tracking Super Twisting Sliding Mode Controller Fractional order proportional integral derivative controller optimization algorithms

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A Quantum-Like Human Learning optimization Algorithm 8th

A Quantum-Like Human Learning Optimization Algorithm

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Ling Huang, Yi Qian, Chaolin Kang, Xing Zhang, Anfa Pardalos, Panos M. Fei, Minrui Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Sino-European School of Technology Shanghai University Shanghai200444 China Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida GainesvilleFL32611 United States

ISBN: (纸本)9789819602933

Human Learning optimization (HLO) is a novel potential and promising meta-heuristic, which is developed based on a simplified human learning model. In recent years, the hypothesis that the human cognitive process obeys the laws of quantum rather than classical probability has been successfully used to explain a variety of seemingly "irrational" judgment and human decision-making behaviors, and the independent evidence for this hypothesis has been provided from different experiments. Inspired by this hypothesis, this paper introduces a quantum cognition model of humans into HLO and proposes a quantum-like human learning optimization algorithm (QHLO), in which the belief and its change in human brains during the learning process is simulated and implemented in the search of the algorithm. The developed QHLO is validated on the benchmark functions, and the performance is compared with one recent HLO variants, two quantum-inspired meta-heuristics and two recent binary meta-heuristics. The experimental results show that that the presented QHLO inherits the advantages of HLO and has better global search ability as its diversity is further enhanced with the integration of the quantum cognition model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Solving Decision-Dependent Robust Problems as Bilevel optimization Problems

arXiv

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arXiv 2025年

作者： Lefebvre, Henri Schmidt, Martin Stevens, Simon Thürauf, Johannes Trier University Department of Mathematics Universitätsring 15 Trier54296 Germany Department Liberal Arts and Social Sciences Discrete Optimization Lab Dr.-Luise-Herzberg-Str. 4 Nuremberg90461 Germany

Both bilevel and robust optimization are established fields of mathematical optimization and operations research. However, only until recently, the similarities in their mathematical structure has neither been studied theoretically nor exploited computationally. Based on the recent results by Goerigk et al. (2025), this paper is the first one that reformulates a given strictly robust optimization problem with a decision-dependent uncertainty set as an equivalent bilevel optimization problem and then uses solution techniques from the latter field to solve the robust problem at hand. If the uncertainty set can be dualized, the respective bilevel techniques to obtain a single-level reformulation are very similar compared to the classic dualization techniques used in robust optimization but lead to larger single-level problems to be solved. Our numerical study shows that this leads to larger computation times but may also slightly improve the dual bound. For the more challenging case of a decision-dependent uncertainty set that cannot be dualized because it is represented as a mixed-integer linear problem, we are not aware of any applicable robust optimization techniques. Fortunately, by exploiting the corresponding bilevel reformulation and recent bilevel solvers, we are able to present the first numerical results for this class of robust *** Codes 90C17 © 2025, CC BY.

关键词： optimization algorithms

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FABLE: A Bundle Method For Federated Learning In Wireless Systems

FABLE: A Bundle Method For Federated Learning In Wireless Sy...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cederberg, Daniel Larsson, Erik G. Johansson, Mikael Department of Electrical Engineering Stanford University Stanford United States Department of Electrical Engineering Linköping University Linköping Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

This paper presents a comprehensive approach to federated learning in wireless networks. We discuss communication strategies that address packet loss and bitrate limitations in both uplink and downlink transmissions, and introduce FABLE, a novel optimization algorithm designed to operate effectively under these network constraints. The algorithm also supports non-smooth regularizers and accommodates heterogeneous data distributions across clients. We provide theoretical convergence guarantees under gradient compression and asynchronous operation, and demonstrate the efficiency of our approach through numerical experiments. © 2025 IEEE.

关键词： Federated learning optimization algorithms wireless communications

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Switching Cost Aware Rounding for Relaxations of Mixed-Integer Optimal Control Problems: The 2-D Case

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 548-553页

作者： Bestehorn, Felix Hansknecht, Christoph Kirches, Christian Manns, Paul Tech Univ Braunschweig Inst Math Optimizat D-38106 Braunschweig Germany Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA

This article is concerned with a recently proposed switching cost aware rounding (SCARP) strategy in the combinatorial integral approximation for mixed-integer optimal control problems (MIOCPs). We consider the case of a control variable that is discrete-valued and distributed on a two-dimensional domain. While the theoretical results from the one-dimensional case directly apply to the multidimensional setting, the structure of the cost function in the graph-based rounding computation is significantly more involved in the two-dimensional case. We describe a set up of the computational graph and the traversal algorithm underlying the SCARP strategy that enable a transfer to the two-dimensional setting. We demonstrate the SCARP strategy in this two-dimensional setting using the example of a MIOCP from topology optimization. We compare the graph-based approach to a ground truth computation using an integer linear programming (ILP) solver. The graph-based approach becomes computationally intractable for medium grid sizes. We show that the one-dimensional SCARP algorithm can be employed on a serialization of the grid cells in these cases and still provides an efficient heuristic that yields superior performance compared with that of other rounding heuristics such as sum-up rounding (SUR).

关键词： Switches Approximation algorithms Optimal control Topology Runtime Labeling Dynamical systems Optimal control optimization algorithms control applications

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An Interior-Point Algorithm for Continuous Nonlinearly Constrained optimization with Noisy Function and Derivative Evaluations

arXiv

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arXiv 2025年

作者： Curtis, Frank E. Dezfulian, Shima Waechter, Andreas Department of Industrial and Systems Engineering Lehigh University United States Department of Industrial Engineering and Management Sciences Northwestern University United States

An algorithm based on the interior-point methodology for solving continuous nonlinearly constrained optimization problems is proposed, analyzed, and tested. The distinguishing feature of the algorithm is that it presumes that only noisy values of the objective and constraint functions and their first-order derivatives are available. The algorithm is based on a combination of a previously proposed interior-point algorithm that allows inexact subproblem solutions and recently proposed algorithms for solving bound- and equality-constrained optimization problems with only noisy function and derivative values. It is shown that the new interior-point algorithm drives a stationarity measure below a threshold that depends on bounds on the noise in the function and derivative values. The results of numerical experiments show that the algorithm is effective across a wide range of *** Codes 65K05, 49M37, 90C53, 90C30, 90C51 © 2025, CC BY.

关键词： optimization algorithms

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Solution of Robust Linear optimization Problems

arXiv

引用

arXiv 2025年

作者： Mondal, Parthasarathi Ojha, Akshay Kumar Department of Mathematics IIT Bhubaneswar India

Robust optimization(RO) is an important tool for handling optimization problem with uncertainty. The main objective of RO is to solve optimization problems due to uncertainty associated with constraints satisfying all realizations of uncertain values within a given uncertainty set. The challenge of RO is to reformulate the constraints so that the uncertain optimization problem is transformed into a tractable deterministic form. In this paper, we have given more emphasis to study the robust counterpart(RC) of the RO problems and have developed a mathematical model on the solution strategy for robust linear optimization problems, where the constraints only are associated with uncertainties. The box and ellipsoidal uncertainty sets are considered and some illustrative numerical examples have been solved in each corresponding case for validating our proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

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A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy

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JOURNAL OF MOLECULAR MODELING 2014年第7期20卷 2251-1-2251-10页

作者： Guo, Liyong Yan, Zhiqiang Zheng, Xiliang Hu, Liang Yang, Yongliang Wang, Jin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Chinese Acad Sci Changchun Inst Appl Chem State Key Lab Electroanalyt Chem Changchun 130022 Jilin Peoples R China Dalian Univ Technol Sch Life Sci & Biotechnol Ctr Mol Med Dalian 116024 Peoples R China SUNY Stony Brook Dept Chem & Phys Stony Brook NY 11794 USA

In protein-ligand docking, an optimization algorithm is used to find the best binding pose of a ligand against a protein target. This algorithm plays a vital role in determining the docking accuracy. To evaluate the relative performance of different optimization algorithms and provide guidance for real applications, we performed a comparative study on six efficient optimization algorithms, containing two evolutionary algorithm (EA)-based optimizers (LGA, DockDE) and four particle swarm optimization (PSO)-based optimizers (SODock, varCPSO, varCPSO-ls, FIPSDock), which were implemented into the protein-ligand docking program AutoDock. We unified the objective functions by applying the same scoring function, and built a new fitness accuracy as the evaluation criterion that incorporates optimization accuracy, robustness, and efficiency. The varCPSO and varCPSO-ls algorithms show high efficiency with fast convergence speed. However, their accuracy is not optimal, as they cannot reach very low energies. SODock has the highest accuracy and robustness. In addition, SODock shows good performance in efficiency when optimizing drug-like ligands with less than ten rotatable bonds. FIPSDock shows excellent robustness and is close to SODock in accuracy and efficiency. In general, the four PSO-based algorithms show superior performance than the two EA-based algorithms, especially for highly flexible ligands. Our method can be regarded as a reference for the validation of new optimization algorithms in protein-ligand docking.

关键词： Protein-ligand docking AutoDock software optimization algorithms Evolutionary algorithms (EAs) Particle swarm optimizations (PSOs)

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Marginal Price optimization

arXiv

引用

arXiv 2025年

作者： Loesch, Stefan Richardson, Mark

We introduce a new framework for optimal routing and arbitrage in AMM driven markets. This framework improves on the original best-practice convex optimization by restricting the search to the boundary of the optimal space. We can parameterize this boundary using a set of prices, and a potentially very high dimensional optimization problem (2 optimization variables per curve) gets reduced to to a much lower dimensional root finding problem (1 optimization variable per token, irregardless of number of the curves). Our reformulation is similar to the dual problem of a reformulation of the original convex problem. We show our reformulation of the problem is equivalent to the original formulation except in the case of infinitely concentrated liquidity, where we provide a suitable approximation. Our formulation performs far better than the original one in terms of speed – we obtain an improvement of up to 200x against Clarabel, the new CVXPY default solver – and robustness, especially on levered curves. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

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