Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
- Timo Kern & Patrick Dossow & Serafin von Roon, 2020. "Integrating Bidirectionally Chargeable Electric Vehicles into the Electricity Markets," Energies, MDPI, vol. 13(21), pages 1-30, November.
- Mohamed Ibrahim & Saad Al-Sobhi & Rajib Mukherjee & Ahmed AlNouss, 2019. "Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit," Energies, MDPI, vol. 12(10), pages 1-12, May.
- Jayaraman J. Thiagarajan & Bindya Venkatesh & Rushil Anirudh & Peer-Timo Bremer & Jim Gaffney & Gemma Anderson & Brian Spears, 2020. "Designing accurate emulators for scientific processes using calibration-driven deep models," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
- Zhou, Yue & Wu, Jianzhong & Long, Chao, 2018. "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework," Applied Energy, Elsevier, vol. 222(C), pages 993-1022.
- Mathias Müller & Florian Biedenbach & Janis Reinhard, 2020. "Development of an Integrated Simulation Model for Load and Mobility Profiles of Private Households," Energies, MDPI, vol. 13(15), pages 1-33, July.
- Lotta Kannari & Jussi Kiljander & Kalevi Piira & Jouko Piippo & Pekka Koponen, 2021. "Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator," Forecasting, MDPI, vol. 3(2), pages 1-13, April.
- Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
- Etemadi, Nasrollah, 1983. "On the laws of large numbers for nonnegative random variables," Journal of Multivariate Analysis, Elsevier, vol. 13(1), pages 187-193, March.
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.- Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
- de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020.
"Variable time-step: A method for improving computational tractability for energy system models with long-term storage,"
Energy, Elsevier, vol. 213(C).
- Paul de Guibert & Behrang Shirizadeh & Philippe Quirion, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Post-Print hal-03100309, HAL.
- Pia Szichta & Ingela Tietze, 2020. "Sharing Economy in der Elektrizitätswirtschaft: Treiber und Hemmnisse [Title sharing economy in the electricity sector: drivers and barriers]," Sustainability Nexus Forum, Springer, vol. 28(3), pages 109-125, December.
- Siemroth, Christoph, 2014.
"Why prediction markets work : The role of information acquisition and endogenous weighting,"
Working Papers
14-02, University of Mannheim, Department of Economics.
- Siemroth, Christoph, 2014. "Why prediction markets work : the role of information acquisition and endogenous weighting," Working Papers 14-29, University of Mannheim, Department of Economics.
- Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
- Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
- Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.
- Theresa Liegl & Simon Schramm & Philipp Kuhn & Thomas Hamacher, 2023. "Considering Socio-Technical Parameters in Energy System Models—The Current Status and Next Steps," Energies, MDPI, vol. 16(20), pages 1-19, October.
- Min-Hwi Kim & Dong-Won Lee & Deuk-Won Kim & Young-Sub An & Jae-Ho Yun, 2021. "Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community," Energies, MDPI, vol. 14(17), pages 1-17, September.
- Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
- Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
- Ma, Li & Wang, Lingfeng & Liu, Zhaoxi, 2021. "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, Elsevier, vol. 285(C).
- Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
- Byeongtae Ahn, 2022. "Implementation and Early Adoption of an Ethereum-Based Electronic Voting System for the Prevention of Fraudulent Voting," Sustainability, MDPI, vol. 14(5), pages 1-16, March.
- João Mello & Cristina de Lorenzo & Fco. Alberto Campos & José Villar, 2023. "Pricing and Simulating Energy Transactions in Energy Communities," Energies, MDPI, vol. 16(4), pages 1-22, February.
- Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
- Wang, Zibo & Yu, Xiaodan & Mu, Yunfei & Jia, Hongjie, 2020. "A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System," Applied Energy, Elsevier, vol. 260(C).
- Sara Khan & Uzma Amin & Ahmed Abu-Siada, 2024. "P2P Energy Trading of EVs Using Blockchain Technology in Centralized and Decentralized Networks: A Review," Energies, MDPI, vol. 17(9), pages 1-17, April.
- Tania Cerquitelli & Giovanni Malnati & Daniele Apiletti, 2019. "Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings," Energies, MDPI, vol. 12(15), pages 1-18, July.
- Wallsten, Thomas S. & Diederich, Adele, 2001. "Understanding pooled subjective probability estimates," Mathematical Social Sciences, Elsevier, vol. 41(1), pages 1-18, January.
More about this item
Keywords
artificial intelligence; machine learning; distributed energy resources; electricity markets; energy communities; emulation-model; surrogate-model; meta-model; sampling; TSA;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:gam:jeners:v:15:y:2022:i:3:p:1239-:d:744656. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.