Some searches may not work properly. We apologize for the inconvenience.
My bibliography Save this articleData-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2024.123006
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Assereto, Martina & Byrne, Julie, 2021. "No real option for solar in Ireland: A real option valuation of utility scale solar investment in Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023.
"Machine learning advances for time series forecasting,"
Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
- Higgins, P. & Foley, A.M. & Douglas, R. & Li, K., 2014. "Impact of offshore wind power forecast error in a carbon constraint electricity market," Energy, Elsevier, vol. 76(C), pages 187-197.
- Bahloul, Mohamed & Daoud, Mohamed & Khadem, Shafi K., 2024. "Optimal dispatch of battery energy storage for multi-service provision in a collocated PV power plant considering battery ageing," Energy, Elsevier, vol. 293(C).
- Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
- Newbery, David, 2017. "Tales of two islands – Lessons for EU energy policy from electricity market reforms in Britain and Ireland," Energy Policy, Elsevier, vol. 105(C), pages 597-607.
- Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
- Newbery, David, 2021. "National Energy and Climate Plans for the island of Ireland: wind curtailment, interconnectors and storage," Energy Policy, Elsevier, vol. 158(C).
- Vorushylo, Inna & Keatley, Patrick & Shah, Nikhilkumar & Green, Richard & Hewitt, Neil, 2018. "How heat pumps and thermal energy storage can be used to manage wind power: A study of Ireland," Energy, Elsevier, vol. 157(C), pages 539-549.
- Drew, Daniel R. & Coker, Phil J. & Bloomfield, Hannah C. & Brayshaw, David J. & Barlow, Janet F. & Richards, Andrew, 2019. "Sunny windy sundays," Renewable Energy, Elsevier, vol. 138(C), pages 870-875.
- Bahloul, Mohamed & Daoud, Mohamed & Khadem, Shafiuzzaman K., 2022. "A bottom-up approach for techno-economic analysis of battery energy storage system for Irish grid DS3 service provision," Energy, Elsevier, vol. 245(C).
- Al kez, Dlzar & Foley, Aoife M. & McIlwaine, Neil & Morrow, D. John & Hayes, Barry P. & Zehir, M. Alparslan & Mehigan, Laura & Papari, Behnaz & Edrington, Chris S. & Baran, Mesut, 2020. "A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation," Energy, Elsevier, vol. 205(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
- Coker, Phil J. & Bloomfield, Hannah C. & Drew, Daniel R. & Brayshaw, David J., 2020. "Interannual weather variability and the challenges for Great Britain’s electricity market design," Renewable Energy, Elsevier, vol. 150(C), pages 509-522.
- Newbery, David M., 2023. "High renewable electricity penetration: Marginal curtailment and market failure under “subsidy-free” entry," Energy Economics, Elsevier, vol. 126(C).
- Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
- Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
- Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
- Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022.
"Forecasting US Inflation Using Bayesian Nonparametric Models,"
Papers
2202.13793, arXiv.org.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Working Papers 22-05, Federal Reserve Bank of Cleveland.
- Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary, 2023. "Forecasting US Inflation Using Bayesian Nonparametric Models," CEPR Discussion Papers 18244, C.E.P.R. Discussion Papers.
- James, Nick & Menzies, Max, 2022. "Global and regional changes in carbon dioxide emissions: 1970–2019," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
- Vassilis M. Charitopoulos & Mathilde Fajardy & Chi Kong Chyong & David M. Reiner, 2022.
"The case of 100% electrification of domestic heat in Great Britain,"
Working Papers
EPRG2206, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
- Charitopoulos, V. & Fajardy, M. & Chyong, C. K. & Reiner, D., 2022. "The case of 100% electrification of domestic heat in Great Britain," Cambridge Working Papers in Economics 2210, Faculty of Economics, University of Cambridge.
- Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
- Khasanzoda, Nasrullo & Safaraliev, Murodbek & Zicmane, Inga & Beryozkina, Svetlana & Rahimov, Jamshed & Ahyoev, Javod, 2022. "Use of smart grid based wind resources in isolated power systems," Energy, Elsevier, vol. 253(C).
- Lewis, Matt & McNaughton, James & Márquez-Dominguez, Concha & Todeschini, Grazia & Togneri, Michael & Masters, Ian & Allmark, Matthew & Stallard, Tim & Neill, Simon & Goward-Brown, Alice & Robins, Pet, 2019. "Power variability of tidal-stream energy and implications for electricity supply," Energy, Elsevier, vol. 183(C), pages 1061-1074.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023.
"Tail Forecasting With Multivariate Bayesian Additive Regression Trees,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
- Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
- Gohdes, Nicholas & Simshauser, Paul & Wilson, Clevo, 2022.
"Renewable entry costs, project finance and the role of revenue quality in Australia's National Electricity Market,"
Energy Economics, Elsevier, vol. 114(C).
- Nicholas Gohdes & Paul Simshauser, 2022. "Renewable entry costs, project finance and the role of revenue quality in Australia’s National Electricity Market," Working Papers EPRG2204, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
- Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
- Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
- Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2020.
"Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets,"
Energy Economics, Elsevier, vol. 92(C).
- Do, Hung & Nepal, Rabindra & Jamasb, Tooraj, 2020. "Electricity Market Integration, Decarbonisation and Security of Supply: Dynamic Volatility Connectedness in the Irish and Great Britain Markets," Working Papers 3-2020, Copenhagen Business School, Department of Economics.
- Hung Do & Rabindra Nepal & Tooraj Jamasb, 2020. "Electricity Market Integration, Decarbonisation and Security of Supply: Dynamic Volatility Connectedness in the Irish and Great Britain Markets," Working Papers EPRG2003, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
- Hung Do & Rabindra Nepal & Tooraj Jamasb, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," CAMA Working Papers 2020-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Do, H. & Nepal, R. & Jamasb, T., 2020. "Electricity Market Integration, Decarbonisation and Security of Supply: Dynamic Volatility Connectedness in the Irish and Great Britain Markets," Cambridge Working Papers in Economics 2007, Faculty of Economics, University of Cambridge.
- Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
- Junda Chen & Xuejing Lan & Ye Zhou & Jiaqiao Liang, 2022. "Formation Control with Connectivity Assurance for Missile Swarms by a Natural Co-Evolutionary Strategy," Mathematics, MDPI, vol. 10(22), pages 1-24, November.
More about this item
Keywords
SNSP; Machine learning; Data-driven analysis; Day-ahead forecasting; Neural networks; Deep learning;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:362:y:2024:i:c:s0306261924003891. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.