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Beyond passive House: use of evolutionary algorithms in architectural design

JOURNAL OF BUILDING ENGINEERING 2023年 76卷

作者： Vettorazzi, Egon Figueiredo, Antonio Rebelo, Filipe Vicente, Romeu Feiertag, Gabriel Alves UNILA Fed Univ Latin Amer Integrat Foz Do Lguacu Brazil Univ Aveiro RISCO Dept Civil Engn Campus Univ Santiago Aveiro Portugal

As buildings are accountable for one-fifth of the total greenhouse gas emissions, improving their energy efficiency is foreseen as a pivotal opportunity towards climate neutrality, answering the UN call for climate action. Thus, this work intends to study to what extent the optimisation of the geometrical characteristics of building envelope can contribute to reducing their energy consumption under different climatic scenarios. The case studies have focused on three single-family houses located in different climatic zones in southern Brazil. Therefore, an evolutionary algorithm was employed in a two-step optimisation approach. First the window area and secondly the shading solutions dimensions, considering the Passive House (PH) concept requirements. The optimisation of the window areas and shading solutions dimensions together with the PH concept resulted in a reduction of energy demand between 76 and 91% and an increase in thermal comfort between 89 and 93%. Regarding window dimensions, all scenarios experienced a significant reduction of glazed areas, from 38.9 to 60.5%. As conclusion, an adequate design of shading elements and windows significantly contribute to improve indoor thermal comfort and to reduce the buildings' energy demand, an utmost relevant factor to be considered in the early stages of project design pursuing high energy performance buildings.

关键词： Building dynamic simulation Energy efficiency Thermal comfort Passive house optimisation algorithms

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Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies

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ENERGY CONVERSION AND MANAGEMENT 2022年 259卷 1页

作者： Neshat, Mehdi Nezhad, Meysam Majidi Mirjalili, Seyedali Piras, Giuseppe Garcia, Davide Astiaso Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE Rome Italy Yonsei Univ Yonsei Frontier Lab Seoul South Korea Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy

An accurate prediction of short-term and long-term wind speed is necessary in order to integrate wind energy into large-scale grid power. However, wind speed presents diverse and complex seasonal and stochastic characteristics that impose challenges on wind speed forecasting models. This study proposes a Quaternion Convolutional Neural Network combined with a Bi-directional Long Short-Term Memory recurrent network to forecast wind speed. Quaternion Convolutional Neural Network is used to elicit more effective features from the stochastic sub-signals of wind speed. A new decomposition method is also proposed, comprising variational mode decomposition to decompose the wind speed data into optimal signal components, and an improved arithmetic optimisation algorithm to optimise the parameters of the variational mode decomposition. Furthermore, a fast and effective hyper-parameters tuner is introduced in order to adjust the hyper-parameters and architecture of the proposed hybrid forecasting model. The proposed forecasting model is developed based on data collected from Lesvos and Samothraki Greek islands located in the North Aegean Sea with the forecasting range in one-day ahead (long-term) and achieved considerable accuracy improvements in these case studies compared with the bi-directional long short-term memory model at 13% and 20%, respectively. The experimental outcomes confirm that, first, the proposed hybrid forecasting model considerably outperforms the five existing machine learning and two hybrid models in terms of precision and stability.

关键词： Wind speed Short-term forecasting optimisation algorithms Quaternion Convolutional neural network Deep learning models Hyper-parameters tuning

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Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

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ENERGIES 2022年第4期15卷 1266-1266页

作者： Barbaresi, Alberto Ceccarelli, Mattia Menichetti, Giulia Torreggiani, Daniele Tassinari, Patrizia Bovo, Marco Univ Bologna Dept Agr & Food Sci I-40127 Bologna Italy Northeastern Univ Dept Phys Boston MA 02115 USA

Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance.

关键词： machine learning building energy simulation optimisation algorithms building energy saving solutions

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Integral maximisation algorithm. Comparison with standard methods and application to adaptive optical systems with statistical backlash

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第7期35卷 075207-075207页

作者： Jan, Luis Emiliano Zaldivar Escola, Facundo Univ Buenos Aires Fac Ingn Dept Fis Buenos Aires Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires Argentina

One of the fundamental challenges of engineering is establishing the optimal parameters in problems with multiple local solutions. A large number of optimisation algorithms have been developed for this task. This paper proposes a new optimisation algorithm that is effective in systems with hysteresis and non-linearities. Tests are performed with standard optimisation functions and with a complex optical model. For each test, the results of several known optimisation algorithms are compared with the proposed one. All optimisation algorithms used in the tests are then implemented in an actual thermal diffusivity measuring instrument which uses an optical technique. The optical alignment process of this real-world instrument is carried out by various optimisation algorithms and the dispersions in the measured values are compared. It is concluded that the proposed algorithm presents high robustness and the fastest convergence to the global optimum.

关键词： optimisation algorithms optoelectronics thermal diffusivity measurement

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Automatic identification of macroscopic constitutive parameters for polycrystalline materials based on computational homogenisation

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ENGINEERING COMPUTATIONS 2024年

作者： Goncalves, Guilherme Fonseca Coelho, Rui Pedro Cardoso Lopes, Igor Andre Rodrigues Univ Porto Fac Engn Dept Engn Mecan Porto Portugal Univ Porto Inst Ciencia & Inovacao Engn Mecan & Engn Ind Oporto Portugal

PurposeThe purpose of this research is to establish a robust numerical framework for the calibration of macroscopic constitutive parameters, based on the analysis of polycrystalline RVEs with computational ***/methodology/approachThis framework is composed of four building-blocks: (1) the multi-scale model, consisting of polycrystalline RVEs, where the grains are modelled with anisotropic crystal plasticity, and computational homogenisation to link the scales, (2) a set of loading cases to generate the reference responses, (3) the von Mises elasto-plastic model to be calibrated, and (4) the optimisation algorithms to solve the inverse identification problem. Several optimisation algorithms are assessed through a reference identification problem. Thereafter, different calibration strategies are tested. The accuracy of the calibrated models is evaluated by comparing their results against an FE2 model and experimental *** the initial tests, the LIPO optimiser performs the best. Good results accuracy is obtained with the calibrated constitutive models. The computing time needed by the FE2 simulations is 5 orders of magnitude larger, compared to the standard macroscopic simulations, demonstrating how this framework is suitable to obtain efficient micro-mechanics-informed constitutive ***/valueThis contribution proposes a numerical framework, based on FE2 and macro-scale single element simulations, where the calibration of constitutive laws is informed by multi-scale analysis. The most efficient combination of optimisation algorithm and definition of the objective function is studied, and the robustness of the proposed approach is demonstrated by validation with both numerical and experimental data.

关键词： Inverse identification Constitutive modelling Crystal plasticity Finite element method optimisation algorithms

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Experimental study of the temperature variations in a standing wave loudspeaker driven thermoacoustic refrigerator

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2020年 17卷

作者： Alamir, Mahmoud A. Flinders Univ S Australia Coll Sci & Engn Clovelly Pk Adelaide SA 5042 Australia

The temperature difference across the stack is essential to the performance of thermoacoustic refrigerators. This paper experimentally investigates the effect of the stack geometric parameters on the temperature difference across the stack in a standing wave loudspeaker driven thermoacoustic refrigerator. In addition, it investigates the temperature variations through the resonator and its cross-sections. The effect of this variability on the performance of the refrigerator is also presented. Celcor Ceramic stacks are used at five normalised stack positions of 0.286, 0.764, 1.05, 1.43 and 1.72, four normalised stack lengths of 0.076, 0.114, 0.153 and 0.191, and two stack porosities of 0.8 and 0.85. Results show that the maximum and minimum temperatures through the resonator are across the stack, particularly at the centre point of each side of the stack. Moreover, at certain stack positions, hot and cold sides across the stack are altered. The coefficient of performance also increases at high-temperature difference positions. As a result, this study provides guidelines for increasing the performance of thermoacoustic refrigerators, which still lack competitiveness because of their relatively low performance. It also helps design some parts such as heat exchangers to consider the maximum and minimum temperature positions of thermoacoustic refrigerators.

关键词： Thermoacoustic refrigeration Temperature difference optimisation algorithms Oscillatory heat transfer Heat exchangers

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Adapting reservoir operations for optimal water management under varying climate and demand scenarios using metaheuristic algorithms

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AIN SHAMS ENGINEERING JOURNAL 2024年第8期15卷

作者： Chong, J. Y. Hooi, G. L. Goh, Q. Y. Lai, V. Huang, Y. F. Koo, C. H. El-Shafie, Ahmed Ahmed, Ali Najah Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang Selangor Malaysia Univ Malaya UM Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates Sunway Univ Sch Engn & Technol Dept Engn Bandar Sunway 47500 Malaysia

The operational rule curve is significant for proper reservoir operations and water resources management of the dam. The optimisation of the release policy is rather a complicated and stochastic problem. Various types of algorithms have been used to develop the release policy for the Klang Gate Dam (KGD), but alas, climate parameters associated with climate change, be it regional or globally, e.g., temperature factors, had not been considered then. In this study, the consideration of maximum and minimum temperature factors is given emphasis, while water demand is evaluated in the optimisation problem using simulation-optimisation approaches. The support vector regression simulation model (SVRS), and the multilayer perceptron simulation model (MLPS) were used to simulate the demand for the scenario cases. SVRS utilises the hyperplane theory to study the relationship between the input and output data, whereas MLPS employs the human neural system to weight and process the inputs via multiple layers of neurons into a targeted output. Four distinct types of simulation-optimisation combination model, including the SVRS-firefly algorithm (SVRS-FA), the SVRS-particle swarm optimisation (SVRS-PSO), the MLPS-firefly algorithm (MLPS-FA), and the MLPS-particle swarm optimisation (MLPS-PSO), were applied and investigated. The ultimate goal of this study was to minimise the water deficit. The PSO optimisation models overall have outperformed the FA optimisation models in all aspects. The periodic reliability of the MLPS-PSO was found to be the highest when the minimum temperature scenario was considered, and is 0.6491, with the least shortage period of 80 months. The MLPS-PSO is also the most resilient model in the same scenario, with a resilience value of 0.6750. Whereas the SVRS-PSO was the least vulnerable model in the minimum temperature scenario with the lowest shortage index of 0.0047 and a volumetric deficiency of about 2.44 MCM in total. The SVRS-PSO model showed excellen

关键词： Operational rule curve Machine language model optimisation algorithms Climate change parameters Klang Gate Dam

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On the assessment and control optimisation of demand response programs in residential buildings

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2020年 127卷 109861-109861页

作者： Pallonetto, Fabiano De Rosa, Mattia D'Ettorre, Francesco Finn, Donal P. Univ Coll Dublin Energy Inst Dublin Ireland Univ Coll Dublin Sch Mech & Mat Engn Dublin Ireland Ist Nazl Fis Nucl Genoa Italy

The ability to control and optimise energy consumption at end-user level is of increasing interest as a means to achieve a balance between supply and demand, particularly when large penetration of distributed renewable energy sources is being considered. Demand Response programs consist of a series of externally-driven control strategies aimed at adapting consumer end-use load to specific grid requirements. In a demand response scenario, a network of connected systems can be exploited to activate balancing strategies, to provide demand flexibility during periods of high stress for the grid. However, the widespread deployment of demand response programs in the building sector still faces significant challenges. Smart technology deployment, the lack of common standardised assessment procedures and metrics, the absence of established regulatory frameworks are among the main obstacles limiting the development of portfolios of competitive flexibility assets. The residential sector is even more affected by these challenges due to a marginal economic case, the issue of long term harmonisation of hardware and software infrastructure and the influence of the end-user behaviour and preferences on energy consumption. The present paper provides a review on the current developments of the Demand Response programs, with specific reference to the residential building sector. Methodologies and procedures for assessing building energy flexibility and Demand Response programs are described with a special focus on numerical models and available control algorithms. Moreover, markets schemes and social aspects - such as technology acceptance and awareness - and their influence on smart control technologies and algorithms are discussed. Current research gaps and challenges are identified and analysed to provide guidance for future research activities.

关键词： Demand response Energy flexibility Residential building Smart grids Smart buildings optimisation algorithms

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Optimising ship principal dimensions with a Dung Beetle Optimizer and random forest proxy model

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SHIPS AND OFFSHORE STRUCTURES 2024年

作者： Wei, Yuchen Zhang, Huixia Jiang, Dong Zhang, Yongxing Xiao, Shenghao Jiangsu Ocean Univ Makarov Coll Marine Engn Lianyungang Peoples R China Jiangsu Ocean Univ Sch Ocean Engn Lianyungang Peoples R China Nanjing Tianfu Software Co Ltd Ship & Marine Dept Nanjing Peoples R China

Shipping serves as an important component of global trade, with speed being a key attribute closely tied to navigational resistance. Amid rising marine pollution, the International Maritime Organisation (IMO) requires shipowners to modify ships to comply with existing requirements. Herein, focusing on the static water sailing resistance, whole-ship hull line optimisation was conducted for Japan Bulk Carrier (JBC). The optimisation included the bulbous bow, inflow and outflow sections, and the parallel mid-body. Initially, a random forest resistance proxy model was introduced to replace traditional Computational Fluid Dynamics (CFD) methods. The model's hyperparameters were optimised using the Dung Beetle Optimizer algorithm. Subsequently, this algorithm was employed to optimise ship design parameters. Finally, comparisons confirmed the proxy model's high precision, and the optimisation yielded substantial outcomes, achieving a 4.7% decrease in resistance.

关键词： Hull optimization numerical simulation proxy model optimisation algorithms drag performance

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Comparative PSO optimisation of Microgrid Management Models in Island Operation to Minimise Cost

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ENERGIES 2024年第16期17卷 3901页

作者： Zigman, Dubravko Tvoric, Stjepan Lonic, Manuel Zagreb Univ Appl Sci Dept Elect Engn Konavoska 2 Zagreb 10000 Croatia

The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal control of the operation of microgrids in islanded mode. The main objective is to minimise production costs while ensuring a reliable energy supply. Algorithm P prioritises the use of photovoltaic (PV) and battery storage and operates the diesel generator at minimum capacity to reduce fuel consumption and maximise the use of renewable energy sources. Algorithm M70, on the other hand, uses a heuristic approach to adaptively manage energy resources in real time. In this study, the performance of both algorithms is evaluated through simulation in different operating scenarios. The results show that both algorithms significantly improve the efficiency of the microgrid, with the M70 algorithm showing better adaptability and cost efficiency in dynamic environments. This research contributes to ongoing efforts to develop robust and scalable energy management systems for future smart grids.

关键词： microgrid energy management optimisation algorithms predictive control heuristic approach renewable energy islanding cost minimisation smart grids

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