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  • 1.
    Amini, E.
    et al.
    Department of Civil, Environmental, Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
    Nasiri, M.
    Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.
    Pargoo, N. S.
    Department of Civil, Environmental, Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
    Mozhgani, Z.
    Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.
    Golbaz, D.
    Center for Applied Coastal Research, Civil and Environmental Engineering, University of Delaware, Newark, DE, United States.
    Baniesmaeil, M.
    Department of Marine Industries, Islamic Azad University, Science and Research Branch, Tehran, Iran.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Neshat, M.
    Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia.
    Astiaso Garcia, D.
    Department of Planning, Design, Technology of Architecture, Sapienza University of Rome, Rome, Italy.
    Sylaios, G.
    Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, Xanthi, Greece.
    Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach2023In: Energy Conversion and Management: X, E-ISSN 2590-1745, Vol. 19, article id 100371Article in journal (Refereed)
    Abstract [en]

    In recent years, there has been an increasing interest in renewable energies in view of the fact that fossil fuels are the leading cause of catastrophic environmental consequences. Ocean wave energy is a renewable energy source that is particularly prevalent in coastal areas. Since many countries have tremendous potential to extract this type of energy, a number of researchers have sought to determine certain effective factors on wave converters’ performance, with a primary emphasis on ambient factors. In this study, we used metaheuristic optimization methods to investigate the effects of geometric factors on the performance of an Oscillating Surge Wave Energy Converter (OSWEC), in addition to the effects of hydrodynamic parameters. To do so, we used CATIA software to model different geometries which were then inserted into a numerical model developed in Flow3D software. A Ribed-surface design of the converter's flap is also introduced in this study to maximize wave-converter interaction. Besides, a Bi-level Hill Climbing Multi-Verse Optimization (HCMVO) method was also developed for this application. The results showed that the converter performs better with greater wave heights, flap freeboard heights, and shorter wave periods. Additionally, the added ribs led to more wave-converter interaction and better performance, while the distance between the flap and flume bed negatively impacted the performance. Finally, tracking the changes in the five-dimensional objective function revealed the optimum value for each parameter in all scenarios. This is achieved by the newly developed optimization algorithm, which is much faster than other existing cutting-edge metaheuristic approaches. 

  • 2.
    Arslan, N.
    et al.
    Department of Mining Engineering, Cukurova University, Adana, Turkey.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Heydari, A.
    Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, Rome, Italy.
    Astiaso Garcia, Davide
    Department of Planning, Design, Technology of Architecture, Sapienza University of Rome, Design, Italy.
    Sylaios, G.
    Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, Xanthi, Greece.
    A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors2023In: Remote Sensing, E-ISSN 2072-4292, Vol. 15, no 5, article id 1460Article in journal (Refereed)
    Abstract [en]

    Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.

  • 3.
    Guezgouz, Mohammad
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Does Peak Load Occur at the Same Time as High Electricity Prices?: A Case Study of Sports Facilities2024In: Energy Proceedings, Scanditale AB , 2024, Vol. 39Conference paper (Refereed)
    Abstract [en]

    In this study, a simple framework was developed that can help identify and quantify peak load at sports facilities called Rocklunda Fastigheter AB. By analysing the electricity demand profiles and electricity prices from the Nord pool market, we characterize the equipment contributing most to a particular peak load. In addition, we quantified peak loads that occur during high electricity prices. This framework is beneficial in choosing an appropriate demand-side management strategy for reducing peak loads and electricity costs for both academic and public end-users. Finally, a load-shifting strategy based on Mixed Integer Linear Programming (MILP) was developed to minimize the total annual electricity cost. This approach suggests shifting the electricity demand to the early morning hours while reducing it in the evening when the electricity prices are higher. Finally, a cost-benefit analysis revealed the potential for savings of up to 9.5% when implementing a flexibility factor of 30%.

  • 4.
    Majidi Nezhad, Meysam
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Neshat, M.
    Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, Australia.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Piras, G.
    Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, Roma, 00184, Italy.
    Astiaso Garcia, D.
    Department of Planning, Design, Technology of Architecture, Sapienza University of Rome, Via Flaminia 72 – 00196 Rome, Italy.
    Offshore wind farm layouts designer software's2023In: e-Prime - Advances in Electrical Engineering, Electronics and Energy, ISSN 2772-6711, Vol. 4, article id 100169Article in journal (Refereed)
    Abstract [en]

    Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

  • 5.
    Majidi Nezhad, Meysam
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Neshat, M.
    Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, Australia .
    Sylaios, G.
    Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, Xanthi, 67100, Greece.
    Astiaso Garcia, D.
    Department of Planning, Design, Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, Rome, 00196, Italy .
    Marine energy digitalization digital twin's approaches2024In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 191, article id 114065Article in journal (Refereed)
    Abstract [en]

    Digital twins (DTs) promise innovation for the marine renewable energy sector using modern technological advances and the existing maritime knowledge frameworks. The DT is a digital equivalent of a real object that reflects and predicts its behaviours and states in a virtual space over its lifetime. DTs collect data from multiple sources in pilots and leverage newly introduced low-cost sensor systems. They synchronize, homogenize, and transmit the data to a central hub and integrate it with predictive and learning models to optimize plant performance and operations. This research presents critical aspects of DT implementation challenges in marine energy digitalization DT approaches that use and combine data systems. Firstly, the DT and the existing framework for marine knowledge provided by systems are presented, and the DT's main development steps are discussed. Secondly, the DT implementing main stages, measurement systems, data harmonization and preprocessing, modelling, comprehensive data analysis, and learning and optimization tools, are identified. Finally, the ILIAD (Integrated Digital Framework for Comprehensive Maritime Data and Information Services) project has been reviewed as a best EU funding practice to understand better how marine energy digitalization DT's approaches are being used, designed, developed, and launched. 

  • 6.
    Neshat, M.
    et al.
    Center for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Mirjalili, S.
    University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
    Garcia, D. A.
    Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Gandomi, A. H.
    Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia.
    Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy2023In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 278, article id 127701Article in journal (Refereed)
    Abstract [en]

    Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation.

  • 7.
    Neshat, M.
    et al.
    Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
    Sergiienko, N. Y.
    School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, 5005, SA, Australia.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    da Silva, L. S. P.
    Delmar Systems, Perth, 6000, WA, Australia.
    Amini, E.
    Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
    Marsooli, R.
    Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
    Astiaso Garcia, D.
    Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy.
    Mirjalili, S.
    Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
    Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method2024In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 362, article id 122955Article in journal (Refereed)
    Abstract [en]

    Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms.

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