The shift from conventional buildings to the so-called Nearly Zero Energy Buildings (NZEBs) is becoming one of the major contemporary challenges in the world. In this work, a multi-objectiveoptimization approach, bas...
详细信息
The shift from conventional buildings to the so-called Nearly Zero Energy Buildings (NZEBs) is becoming one of the major contemporary challenges in the world. In this work, a multi-objectiveoptimization approach, based on a smart surrogate model, has been developed to minimize the energy consumption, improve the thermal comfort of the occupants and increase the energy self-sufficiency of residential buildings. For this purpose, two main phases have been considered: the first one is related to the development of the surrogate model, based on machine learning utilities, in particular Artificial Neural Networks (ANNs), and the second is related to the optimization process, performed by means of the multi-objective Particle Swarm optimization algorithm (MOPSO). This approach has been applied to a typical Moroccan building, Ground Floor thorn First Floor (GFFF), in different regulatory climate zones. The results show that the approach was successfully implemented using TRNSYS, Matlab and other numerical simulation tools, leading to different solutions in terms of building envelope design. The best-fit solution achieved a huge improvement potential in most climate zones, averaging about 75%, 50% and 85% respectively for energy consumption, thermal comfort and energy self-sufficiency of the studied building. Finally, we strongly recommend this approach to the various stakeholders in this field, including de-signers, engineers, architects, consulting firms, etc., since the results have proven its effectiveness as a very promising step towards designing Comfortable and Nearly Zero Energy Buildings. Future work will focus on the implementation of a hardware device that is able to perform all the steps of the proposed framework for possible pre-project optimizations.(c) 2022 Elsevier Ltd. All rights reserved.
dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective changes with changes in the environment. To solve DMOPs, a transfer learning (TL) approach is used, which can continuously adapt ...
详细信息
dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective changes with changes in the environment. To solve DMOPs, a transfer learning (TL) approach is used, which can continuously adapt to environmental changes and reuse valuable knowledge from the past. However, if all individuals are transferred, they may experience negative transfers. Therefore, this paper proposes a novel knowledge transfer method for the dynamicmulti-objective evolutionary algorithm (T-DMOEA) to solve DMOPs, which consists of a multi-time prediction model (MTPM) and a manifold TL algorithm. First, according to the movement trend of historical knee points, the MTPM model uses a weighted method to effectively track knee points after environmental changes. Then, the knowledge of the suboptimal solution is reused in the non -knee point set using the manifold TL technique, which yields more high-quality individuals and speeds up the convergence. In the dynamic evolutionary process, the knee points and high-quality solutions are combined to guide the generation of the initial population in the next environment, ensuring the diversity of the population while reducing the computational cost. The experimental results show that the proposed T-DMOEA algorithm can converge rapidly in solving DMOPs while obtaining better-quality solutions.
This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new...
详细信息
This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.
This article presents a dynamic propulsion allocation strategy for automated ship berthing based on the dynamic nondominated sorting genetic II(DNSGAII) *** the DNSGAII algorithm conserves energy and minimizes the com...
详细信息
ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
This article presents a dynamic propulsion allocation strategy for automated ship berthing based on the dynamic nondominated sorting genetic II(DNSGAII) *** the DNSGAII algorithm conserves energy and minimizes the computational load required for ***,it ensures precise tracking of the berthing *** method involves a detailed assessment of ship propulsion characteristics and thruster properties,integrating various DNSGAII algorithm variants,and considering practical physical constraints and dynamicmulti-objective *** effectiveness and practicality of the proposed approach are validated through ship simulation experiments.
Sustained power frequency overvoltage control strategy is an important part of power system restoration plan. The existing sustained overvoltage control methods are based on static network, ignoring the dynamic restor...
详细信息
ISBN:
(纸本)9781467380416
Sustained power frequency overvoltage control strategy is an important part of power system restoration plan. The existing sustained overvoltage control methods are based on static network, ignoring the dynamic restoration process. By choosing restoration targets as milestones, the dynamic process can be divided into several restoration sequences, each sequence consists of several restoration operations. In this paper, a dynamicmulti-objective sustained overvoltage control model based on the restoration sequence is proposed, which takes the coupling of voltage control schemes at different stages into consideration. The objective functions adapted to the characteristics of restoration process are defined, including the operation risk of restoration sequence, adjusting time of voltage control scheme and voltage deviation of energized buses. The effectiveness of proposed model is verified by the simulation results of dynamic and static optimization under different restoration scenarios in Shandong power grid.
暂无评论