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Optimal Sizing of Renewable Energy Communities: A multiple Swarms multi-objective Particle Swarm optimization Approach

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ENERGIES 2023年第21期16卷

作者： Faria, Joao Marques, Carlos Pombo, Jose Mariano, Silvio Calado, Maria do Rosario Fac Engn IT Inst Telecomunicacoes P-6201001 Covilha Portugal Univ Beira Interior Fac Engn Dept Electromech Engn P-6201001 Covilha Portugal

Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a multi-objective Particle Swarm optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.

关键词： renewable energy community (REC) energy management strategies multi-objective optimization algorithm multi-swarm MOPSO energy storage systems energy storage sharing

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A novel decomposition-ensemble forecasting system for dynamic dispatching of smart grid with sub-model selection and intelligent optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 201卷

作者： Wang, Jianzhou Zhang, Lifang Liu, Zhenkun Niu, Xinsong Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Short-Term Electricity Load Forecasting (STLF) has drawn increasing attention due to its considerable function on improving the dispatch and management of power system. The path of load forecasting has been widely explored in the past studies. However, most forecasting methods ignore the importance of sub-model selection and interval forecasting, causing poor forecasting performance. In this paper, a hybrid ensemble forecasting scheme, constructed by sub-model selection, data decomposition, multi-objective optimization, point prediction, and interval prediction, is proposed to enrich the current load forecasting system. In the load forecasting system design, the original load data is adaptively decomposed into some sub-series by using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). The most proper sub-model for decomposed series is selected from nine widely-used forecasting methods based on sub-model selection. The forecasting results of each sub-series are ensembled by the multi-objective Grasshopper optimization algorithm (MOGOA) to estimate the final point forecasting values. By integrating point forecasting errors and distribution fitting strategy, the interval forecasting results are further estimated, which investigates the effectiveness of the proposed system. Three numerical experiments are carried out based on the real load data obtained from three observation sites. The experimental results indicate that the hybrid ensemble system can provide better point and interval forecasting performance with the average Mean Absolute Percentage Error (MAPE) values of 0.3275%, 0.4419%, and 0.7024% and the mean Coverage Width-based Criteria (CWC) values (alpha = 0.05) of 0.0668, 0.1335, and 0.3178 from one-step to three-step forecasting, respectively.

关键词： Short-term load forecasting Sub-model selection Hybrid ensemble forecasting Data preprocessing multi-objective optimization algorithm

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Using Hybrid Artificial Intelligence and Evolutionary optimization algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices

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REMOTE SENSING 2021年第13期13卷

作者： Yoosefzadeh-Najafabadi, Mohsen Tulpan, Dan Eskandari, Milad Univ Guelph Dept Plant Agr Guelph ON N1G 2W1 Canada Univ Guelph Dept Anim Biosci Guelph ON N1G 2W1 Canada

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R-2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.

关键词： high-throughput phenotyping machine learning multi-objective optimization algorithm radial basis function random forest support vector regression SPEA2

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Axial force identification of space grid structural members using particle swarm optimization method

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JOURNAL OF BUILDING ENGINEERING 2020年 32卷

作者： Ding, Beidou Liu, Jinling Huang, Zhenhua Li, Xian Wu, Xiaosuo Cai, Liping China Univ Min & Technol Jiangsu Key Lab Environm Impact & Struct Safety E Xuzhou 221116 Jiangsu Peoples R China Univ North Texas Dept Mech Engn Denton TX 76207 USA

Space grid structures are popularly used in large-span civil structures. Identification of the axial internal forces and boundary conditions of the space grid structural members is very important. However, in the engineering practice, the boundary conditions of such members are not ideally rigid or pinned. They are sometimes semi-rigid conditions. Besides, direct measurements of the axial forces and the boundary rigidities of in-situ space grid structural members are difficult to make. Therefore, in this study, a Particle Swarm optimization (PSO) algorithm-based axial force and boundary rigidity identification method is proposed for space grid structural members using multi-order natural frequencies, which can be easily obtained from in-situ tests. The theoretical background of the proposed method is discussed in detail, including the coupling relationship between the axial force and natural frequency and the PSO algorithm. The applicability and accuracy of the proposed method are validated through experimental tests and comparative analyses.

关键词： Particle swarm optimization (PSO) Natural vibration frequency multi-objective optimization algorithm Axial force identification Grid structure

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Adaptive differential privacy preserving based on multi-objective optimization in deep neural networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第20期33卷

作者： Fan, Tian Cui, Zhihua Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 003024 Peoples R China

Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks. ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi-objective optimization model (DPMOM). DPMOM adopts multi-objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA-II algorithm as the basic framework, a multi-objective optimization algorithm based on differential privacy protection (DPPMOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA-II, IBEA, PESA-II, and AGE-II, DPPMOA is proved that the solution set of this algorithm is better.

关键词： deep neural networks differential privacy protection multi-objective optimization algorithm multi-objective optimization model

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An energy-efficient topology-aware virtual machine placement in Cloud Datacenters: A multi-objective discrete JAYA optimization

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2023年 38卷

作者： Shirvani, Mirsaeid Hosseini Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices;it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objective optimization problem with power consumption and resource wastage minimi-zation perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of JAYA (MOD-JAYA) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-JAYA algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics.

关键词： Cloud computing Data center networks Virtual machine placement JAYA optimization algorithm multi-objective optimization algorithm Topology-aware Scheme

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Thermo-economic analysis and multi-objective optimization of a solar dish Stirling engine

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第22期43卷 2861-2877页

作者： Rostami, Mohsen Assareh, Ehsanolah Moltames, Rahim Jafarinejad, Tohid Islamic Azad Univ Dezful Branch Dept Mech Engn Dezful Iran Sharif Univ Technol Dept Energy Engn Tehran Iran

Stirling engines operate in a variety of temperatures and the electric power production via dish Stirling systems could be considered as an appropriate alternative for high-temperature solar concentrator energy harvesting systems. To this end, by performing various studies and analyses on the engine, Stirling cycle, and heat exchangers while utilizing the solar energy as the input thermal energy of the Stirling engine, parameters with the highest effect on the output power and engine stability are detected and considered as optimization variables. In this case, output power, thermal efficiency, and economic evaluation are taken to be the three suitable objective functions for multi-objective optimization. Moreover, two optimization algorithms of MOPSO and SPEA/2 are introduced and applied for the first time for analyzing a dish Stirling engine. Finally, the optimization variable values before and after optimization, as well as the yielded improvement in the output power and thermal efficiency are presented, using LINAMP and TOPSIS techniques for optimum point selection. The optimization results show that the optimum conditions for three objective functions in case of three-objective optimization acquired by LINAMP and TOPSIS methods are 38.96, 0.2391, 0.3127 and 38.71, 0.2433, 0.3152 of P-eta-F, respectively, acquired from optimization by SPEA/2 algorithm. However, single-objective optimization of each of the three objective functions optimized separately yields the values of (42.27, 27.51, and 0.3379) and (42.12, 27.62, and 0.3361) for MOPSO and SPEA/2 algorithms, respectively.

关键词： Dish Stirling multi-objective optimization algorithm MOPSO SPEA 2 thermo-economic analysis solar energy

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BIM-based retrofit method (RBIM) for building envelope thermal performance optimization

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ENERGY AND BUILDINGS 2022年 256卷

作者： Seghier, Taki Eddine Lim, Yaik-Wah Harun, Muhamad Farhin Ahmad, Mohd Hamdan Samah, Azurah A. Majid, Hairudin Abdul UCSI Univ Fac Engn Technol & Built Environm Sch Architecture & Built Environm Cheras Wilayah Perseku Malaysia Univ Teknol Malaysia UTM Fac Built Environm & Surveying Skudai Johor Malaysia UTM Inst Smart Infrastruct & Innovat Construct Isi Ctr Study Built Environm Malay World KALAM Skudai Johor Malaysia UTM Sch Comp Fac Engn Skudai Johor Malaysia

Retrofitting building envelope thermal performance is crucial to ensure adequate interior thermal com-fort of the building, minimize cooling load rate and, therefore, reduce overall building energy consump-tion. The retrofit process of an existing building envelope involves the design team selecting the best performing building materials and components based on various design variables and predefined design objectives. Worldwide, an overall thermal transfer value (OTTV) metric has been developed and used for assessing heat transfer through the building envelope. However, the application of OTTV implicates deal-ing with many design variables and much information, which makes the design decision-making process time-consuming and complicated. Furthermore, selecting the most appropriate design alternative in terms of OTTV performance while considering the most cost-effective design option could be a very chal-lenging task. Therefore, this research proposes a method for retrofitting building information modelling (RBIM) to achieve a tradeoff design set between two conflicting objectives, namely minimizing OTTV and minimizing the retrofit cost. The prototype system derived from this method integrates BIM authoring tools (e.g., Autodesk Revit (R)), visual scripting (e.g., Dynamo), and a non-dominated sorting genetic algo-rithm (NSGA-II) customized in MATLAB (R). The applicability of the proposed system is validated with a case study of an office building. The results show that the developed system provides a valuable design decision support system for retrofitting building envelope thermal performance while considering the retrofit cost. Additionally, the system shows an improved level of automation in terms of data manage-ment compared to the conventional methods. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Building information modelling multi-objective optimization algorithm Visual programming Overall thermal transfer value Green retrofitting Existing building

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A new combined model based on multi-objective salp swarm optimization for wind speed forecasting

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APPLIED SOFT COMPUTING 2020年 92卷

作者： Cheng, Zishu Wang, Jiyang Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China

Wind energy as the representative renewable energy sources attracted the global attention and wind power plays a significant role in power system. Thus, wind speed forecasting is highly critical in wind power grid management. The short-term wind speed prediction can effectively support power grid-management to reduce wind curtailments. In the past, lots of researches had often considered how to enhance the accuracy or stability in short wind speed forecasting. Nevertheless, just focus on one criterion is the inability to build an effective predictive system. In this paper, a novel combined forecasting system was proposed and effectively applied to address the issue of wind speed prediction while obtaining high precision and strong stability simultaneously at the same time. Four ANNs (artificial neural networks) were combined by the optimal weighting coefficients determined by MSSO (multi-objective salp swarm optimizer) in this system and data decomposition and denoising are included in the data preprocessing stage. The multi-objective optimization algorithm overcomes the weakness of the single-objective optimization algorithm that can only achieve one criterion. It can simultaneously optimize accuracy and stability. The 10-minute wind speed data of three data sets of Penglai, China were selected for multi-step forecasting to evaluate the effectiveness of the proposed combined model. And experimental results show that the proposed model not only achieves excellent precision and stability but also outperforms other proposed combined models. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Combined model Wind speed forecasting multi-objective optimization algorithm Data preprocessing technique

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Wind speed interval prediction model based on variational mode decomposition and multi-objective optimization

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APPLIED SOFT COMPUTING 2021年 113卷

作者： Wang, Jianzhou Cheng, Zishu Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

As a potential new energy power generation technology, wind power is gradually developing into the world's mainstream energy. In the research on wind power generation, wind speed prediction is an important part, which has been widely studied. The accurate wind speed prediction is a key part of wind power management to help wind power grid-tied. Currently, most research has focused on point prediction, which in fact does not facilitate the quantitative characterization of the endogenous uncertainty involved. However, interval prediction can avoid this deficiency and make better operation and scheduling of the wind power models. In this study, a novel interval prediction model based on wind speed distribution and multi-objective optimization is designed, which includes data noise reduction module, prediction module, and combination module based on a multi-objective salp swarm algorithm, to provide accurate forecast for power model operation and grid dispatching. The 10-minute wind speed data from three data sets in China were selected for prediction to evaluate the effectiveness of the proposed combined model. The results show that the model is not only better than the considered benchmark model, but also has good potential practical application value in wind power models. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Interval prediction multi-objective optimization algorithm Wind speed Combined models

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