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A review on Forecasting of Photovoltaic Power Generation based on Machine Learning and Metaheuristic Techniques

MUHAMMAD AKHTER, Mekhilef Saad, HAZLIE MOKHLIS, Noraisyah Mohamed Shah
2019 IET Renewable Power Generation  
This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods.  ...  Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.  ...  Model accuracies for estimating solar radiation were improved by including the periodic component in the input variables.  ... 
doi:10.1049/iet-rpg.2018.5649 fatcat:3cjmomuvbrhzzd3zr3vdzowc3q

Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science [article]

Michael J. Way, Catherine Naud (NASA Goddard Institute for Space Studies)
2011 arXiv   pre-print
This year was also special as it included authors from the upcoming book titled "Advances in Machine Learning and Data Mining for Astronomy".  ...  These proceedings attempt to provide a small window into what the current state of research is in this vast interdisciplinary field and we'd like to thank the speakers who spent the time to contribute  ...  This research is partially supported by NSF grant DMS-0456713, from ONR DRI grants N25-74200-F6607 and N00014-10-1-0554, and from DARPA grants N00014-07-10750 and N00014-08-1-1080.  ... 
arXiv:1104.1580v3 fatcat:rxgwvofl75g4roa74ihcu3lt2y

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Min Yan Chia, Yuk Feng Huang, Chai Hoon Koo, Kit Fai Fung
2020 Agronomy  
This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data.  ...  To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced  ...  Unlike the Bayesian model averaging, the boosting algorithm works in a step-wise method, where a learner is added at a time to minimize the loss function.  ... 
doi:10.3390/agronomy10010101 fatcat:mhdonhbpnbcyfl7uj3riunwyga

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Amir Mosavi, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, Annamaria Varkonyi-Koczy
2019 Energies  
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems.  ...  This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications.  ...  An ELM algorithm was used for the feature selection process, and solar radiation was estimated using the optimally screened variables by the CRO-ELM model ( Figure 28 ).  ... 
doi:10.3390/en12071301 fatcat:vzwylqto3zdjhofwfot7jdpvce

100 years of Progress in Applied Meteorology Part III: Additional Applications

Sue Ellen Haupt, Branko Kosović, Scott W. McIntosh, Fei Chen, Kathleen Miller, Marshall Shepherd, Marcus Williams, Sheldon Drobot
2018 Meteorological Monographs  
The study of space weather is advancing our understanding of how the barrage of particles from other planetary bodies in the solar system impacts Earth's atmosphere.  ...  The applications reviewed in this series of chapters are not comprehensive, but they will whet the reader's appetite for learning more about how meteorology can make a concrete impact on the world's population  ...  Chen, and K. Miller were supported, in part, by NCAR funds. NCAR is sponsored by the National Science Foundation.  ... 
doi:10.1175/amsmonographs-d-18-0012.1 fatcat:ra5nseow3zhbbbufgmupb44bua

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

Seyed Hamed Alemohammad, Bin Fang, Alexandra G. Konings, Filipe Aires, Julia K. Green, Jana Kolassa, Diego Miralles, Catherine Prigent, Pierre Gentine
2017 Biogeosciences  
by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables.  ...  </strong> A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (<i>H</i>), and gross primary production (GPP) is developed using a machine learning approach informed  ...  The MTE method used is an ensemble learning algorithm that enables the learning of a diverse sequence of different model trees by perturbing the base learning algorithm .  ... 
doi:10.5194/bg-14-4101-2017 pmid:29290755 fatcat:cut2mecjwndchoinguffqnkgs4

A benchmarking of machine learning techniques for solar radiation forecasting in an insular context

Philippe Lauret, Cyril Voyant, Ted Soubdhan, Mathieu David, Philippe Poggi
2015 Solar Energy  
In this paper, we propose a benchmarking of supervised machine learning techniques (neural networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance  ...  The main findings of this work are, that for hour ahead solar forecasting, the machine learning techniques slightly improve the performances exhibited by the linear AR and the scaled persistence model.  ...  For unstable sky conditions, the discrepancy between the machine learning methods and the simple models is more pronounced with a 2% rRMSE difference in average.  ... 
doi:10.1016/j.solener.2014.12.014 fatcat:lbj6vvcn4faxhbj3biky5ua6ra

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence

Seyed Hamed Alemohammad, Bin Fang, Alexandra G. Konings, Julia K. Green, Jana Kolassa, Catherine Prigent, Filipe Aires, Diego Miralles, Pierre Gentine
2016 Biogeosciences Discussions  
The approach uses an artificial neural network (ANN) with a Bayesian perspective to learn from the training datasets: a target input dataset is generated using three independent data sources and a triple  ...  A new global estimate of surface turbulent fluxes, including latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP) is developed using remotely sensed Solar-Induced Fluorescence  ...  Like all supervised machine learning models, the MPI-BGC method relies on a training dataset to determine the non-linear statistical relationships.  ... 
doi:10.5194/bg-2016-495 fatcat:f3wstefshzcgxebvdsbvrdbvka

A review of machine learning applications in wildfire science and management [article]

Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, Mike D Flannigan
2020 arXiv   pre-print
Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.  ...  There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science.  ...  The authors would also like to thank Intact Insurance and the Western Partnership for Wildland Fire Science for their support.  ... 
arXiv:2003.00646v1 fatcat:5ufhtbwlsvd2rdk3ogbmqpnxuu

Solar Photovoltaic Power Forecasting

Abdelhakim El hendouzi, Abdennaser Bourouhou, Anna Diva Plasencia Lotufo
2020 Journal of Electrical and Computer Engineering  
At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons.  ...  In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy.  ...  Brito who carried out a study in the PV power forecasting for a time horizon of one day ahead. e approach, meanwhile, based on the extreme learning machine (ELM), that is a novel algorithm is used to train  ... 
doi:10.1155/2020/8819925 fatcat:633tnzecebddpe6g34bqek3ozm

Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques

Samuel N. Araya, Anna Fryjoff-Hung, Andreas Anderson, Joshua H. Viers, Teamrat A. Ghezzehei
2021 Hydrology and Earth System Sciences  
In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict  ...  Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture  ...  This research has been supported by the US Fish and Wildlife Service Agreement (grant no. P1740401). Review statement.  ... 
doi:10.5194/hess-25-2739-2021 fatcat:aweqrgv765glfiltmb524q2n6u

A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings

Gohar Gholamibozanjani, Mohammed Farid
2021 Energies  
However, the initial capital cost of PCM is still high, and thus the establishment of a control strategy has become essential to optimize its use in buildings in an effort to lower investment costs.  ...  In this paper, an extensive review has been made with regard to various control strategies applied to PCM-enhanced buildings, such as ON/OFF control, conventional control methods (classical control, optimal  ...  In problems having a large number of variables or for systems that are difficult to model, machine learning is one of the techniques used [169] .  ... 
doi:10.3390/en14071929 fatcat:tkxxunxmczbufbm5o45qqyvv5q

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

Matilde Maria Passamonti, Elisa Somenzi, Mario Barbato, Giovanni Chillemi, Licia Colli, Stéphane Joost, Marco Milanesi, Riccardo Negrini, Monia Santini, Elia Vajana, John Lewis Williams, Paolo Ajmone-Marsan
2021 Animals  
Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios  ...  The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation.  ...  Artificial Intelligence (AI) and Machine Learning (ML) methods are increasingly used to extract information from this type of data to overcome the limits of traditional linear models (250, 251) (see  ... 
doi:10.3390/ani11102833 pmid:34679854 fatcat:xpneh3uh35fefdichsffddoram

A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings

Benedetto Grillone, Gerard Mor, Stoyan Danov, Jordi Cipriano, Andreas Sumper
2021 Applied Energy  
These are then used, in combination with an innovative technique to evaluate the building's weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings  ...  A B S T R A C T Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase  ...  The authors thank the Catalan Institute of Energy (ICAEN) for providing the monitoring and EEM data that was analysed in the case study.  ... 
doi:10.1016/j.apenergy.2021.117502 fatcat:ftxon5flgnbyxk35hpxo7uz7xy

Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning

Yinghao Chu, Hugo T.C. Pedro, Carlos F.M. Coimbra
2013 Solar Energy  
The validation process is carried over by the Cross Validation Method (CVM) and by a randomized training and validation set method (RTM).  ...  The forecast performance for each solar variability season is evaluated, and the models with the best forecasting skill for each season are selected to build a hybrid model that exhibits optimal performance  ...  Acknowledgements The authors gratefully acknowledge funding from the California Solar Initiative (CSI) Research, Development, Demonstration, and Deployment (RD&D) Program Grant III; and from the National  ... 
doi:10.1016/j.solener.2013.10.020 fatcat:izjijkidl5g4zl4gwwjebwhu44
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