IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v155y2018icp705-720.html
   My bibliography  Save this item

Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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).
  2. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
  3. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
  4. Gvozdenac Urošević, Branka D. & Đozić, Damir J., 2021. "Testing long-term energy policy targets by means of artificial neural network," Energy, Elsevier, vol. 227(C).
  5. Xiaolei Sun & Jun Hao & Jianping Li, 2022. "Multi-objective optimization of crude oil-supply portfolio based on interval prediction data," Annals of Operations Research, Springer, vol. 309(2), pages 611-639, February.
  6. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
  7. Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2019. "Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models," Energy, Elsevier, vol. 180(C), pages 387-397.
  8. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
  9. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
  10. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
  11. 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).
  12. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  13. Mohammad Talaei & Majid Astaneh & Elmira Ghiasabadi Farahani & Farzin Golzar, 2023. "Application of Artificial Intelligence for Predicting CO 2 Emission Using Weighted Multi-Task Learning," Energies, MDPI, vol. 16(16), pages 1-18, August.
  14. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
  15. Xiaohui Gao, 2022. "Monthly Wind Power Forecasting: Integrated Model Based on Grey Model and Machine Learning," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
  16. Esra Ilbahar & Cengiz Kahraman & Selcuk Cebi, 2023. "Evaluation of sustainable energy planning scenarios with a new approach based on FCM, WASPAS and impact effort matrix," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11931-11955, October.
  17. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
  18. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
  19. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
  20. Guofeng Wang & Kangli Zhao & Yu Yang & Junjie Lu & Youbing Zhang, 2019. "A Decentralized Energy Flow Control Framework for Regional Energy Internet," Complexity, Hindawi, vol. 2019, pages 1-10, October.
  21. Xu, Xiaofeng & Wei, Zhifei & Ji, Qiang & Wang, Chenglong & Gao, Guowei, 2019. "Global renewable energy development: Influencing factors, trend predictions and countermeasures," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  22. Serdal Atiç & Ercan Izgi, 2024. "Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources," Sustainability, MDPI, vol. 16(12), pages 1-21, June.
  23. Cardo-Miota, Javier & Trivedi, Rohit & Patra, Sandipan & Khadem, Shafi & Bahloul, Mohamed, 2024. "Data-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis," Applied Energy, Elsevier, vol. 362(C).
  24. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.
  25. 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).
  26. Merve Kayacı Çodur, 2023. "Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand," Energies, MDPI, vol. 17(1), pages 1-25, December.
  27. 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.
  28. Gema Hernández-Moral & Sofía Mulero-Palencia & Víctor Iván Serna-González & Carla Rodríguez-Alonso & Roberto Sanz-Jimeno & Vangelis Marinakis & Nikos Dimitropoulos & Zoi Mylona & Daniele Antonucci & H, 2021. "Big Data Value Chain: Multiple Perspectives for the Built Environment," Energies, MDPI, vol. 14(15), pages 1-21, July.
  29. Yi-Chung Hu & Peng Jiang & Jung-Fa Tsai & Ching-Ying Yu, 2021. "An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
  30. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
  31. Song, Chao & Wang, Tao & Chen, Xiaohong & Shao, Quanxi & Zhang, Xianqi, 2023. "Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model," Applied Energy, Elsevier, vol. 345(C).
  32. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.