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Gordon Reikard

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First Name:Gordon
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Last Name:Reikard
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RePEc Short-ID:pre720
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Articles

  1. Gordon Reikard, 2026. "Forecasting Exchange Rates with Machine Learning Models: Revised and Updated Estimates," International Economic Journal, Taylor & Francis Journals, vol. 40(2), pages 248-270, April.
  2. Gordon Reikard, 2023. "Comparing Artificial Intelligence and Nonlinear Regression Models: The Issue of Test Design," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 69, pages 64-65, Q2.
  3. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
  4. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
  5. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.
  6. Parkinson, Simon C. & Dragoon, Ken & Reikard, Gordon & García-Medina, Gabriel & Özkan-Haller, H. Tuba & Brekken, Ted K.A., 2015. "Integrating ocean wave energy at large-scales: A study of the US Pacific Northwest," Renewable Energy, Elsevier, vol. 76(C), pages 551-559.
  7. Pinson, P. & Reikard, G. & Bidlot, J.-R., 2012. "Probabilistic forecasting of the wave energy flux," Applied Energy, Elsevier, vol. 93(C), pages 364-370.
  8. Gordon Reikard, 2011. "Total Factor Productivity And R&D In The Production Function," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 601-613.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.

    Cited by:

    1. Lopes, Francis M. & Conceição, Ricardo & Silva, Hugo G. & Salgado, Rui & Collares-Pereira, Manuel, 2021. "Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants," Renewable Energy, Elsevier, vol. 163(C), pages 755-771.
    2. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
    3. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    4. Puah, Boon Keat & Chong, Lee Wai & Wong, Yee Wan & Begam, K.M. & Khan, Nafizah & Juman, Mohammed Ayoub & Rajkumar, Rajprasad Kumar, 2021. "A regression unsupervised incremental learning algorithm for solar irradiance prediction," Renewable Energy, Elsevier, vol. 164(C), pages 908-925.
    5. Bintao Wu & Jianlong Hao, 2025. "Enhanced xPatch for Short-Term Photovoltaic Power Forecasting: Supporting Sustainable and Resilient Energy Systems," Sustainability, MDPI, vol. 17(16), pages 1-22, August.
    6. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    7. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    8. Stéphanie Monjoly & Maina André & Rudy Calif & Ted Soubdhan, 2019. "Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model," Energies, MDPI, vol. 12(12), pages 1-20, June.
    9. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    10. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    11. Hartmann, Bálint, 2020. "Comparing various solar irradiance categorization methods – A critique on robustness," Renewable Energy, Elsevier, vol. 154(C), pages 661-671.
    12. Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    13. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah & Raed M. Abendeh & Rami Alawneh, 2023. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations," Energies, MDPI, vol. 16(13), pages 1-24, June.
    14. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    15. Musaed Alhussein & Syed Irtaza Haider & Khursheed Aurangzeb, 2019. "Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance," Energies, MDPI, vol. 12(8), pages 1-27, April.

  2. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.

    Cited by:

    1. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    2. Mari R. Tye & Sue Ellen Haupt & Eric Gilleland & Christina Kalb & Tara Jensen, 2019. "Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait," Energies, MDPI, vol. 12(23), pages 1-17, November.
    3. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    4. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    5. Chang, Chih-Hao & Chen, Zih-Bing & Huang, Shih-Feng, 2022. "Forecasting of high-resolution electricity consumption with stochastic climatic covariates via a functional time series approach," Applied Energy, Elsevier, vol. 309(C).
    6. Lopes, Francis M. & Conceição, Ricardo & Silva, Hugo G. & Salgado, Rui & Collares-Pereira, Manuel, 2021. "Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants," Renewable Energy, Elsevier, vol. 163(C), pages 755-771.
    7. Nantian Huang & Ruiqing Li & Lin Lin & Zhiyong Yu & Guowei Cai, 2018. "Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    8. Tingting Zhu & Yuanzhe Li & Zhenye Li & Yiren Guo & Chao Ni, 2022. "Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm," Energies, MDPI, vol. 15(3), pages 1-14, January.
    9. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    10. Abhnil Amtesh Prasad & Merlinde Kay, 2020. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar," Energies, MDPI, vol. 13(2), pages 1-22, January.
    11. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    12. Stéphanie Monjoly & Maina André & Rudy Calif & Ted Soubdhan, 2019. "Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model," Energies, MDPI, vol. 12(12), pages 1-20, June.
    13. Zhang, Xuedong & Zhao, Huanyu & Yao, Junhao & Wang, Zheng & Zheng, Yongshun & Peng, Tian & Zhang, Chu, 2025. "A multi-scale component feature learning framework based on CNN-BiGRU and online sequential regularized extreme learning machine for wind speed prediction," Renewable Energy, Elsevier, vol. 242(C).
    14. Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    15. Rong-Jong Wai & Pin-Xian Lai, 2022. "Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data," Energies, MDPI, vol. 15(10), pages 1-30, May.
    16. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.

  3. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.

    Cited by:

    1. Fairley, I. & Smith, H.C.M. & Robertson, B. & Abusara, M. & Masters, I., 2017. "Spatio-temporal variation in wave power and implications for electricity supply," Renewable Energy, Elsevier, vol. 114(PA), pages 154-165.
    2. Jamei, Mehdi & Ali, Mumtaz & Karbasi, Masoud & Xiang, Yong & Ahmadianfar, Iman & Yaseen, Zaher Mundher, 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach," Applied Energy, Elsevier, vol. 326(C).
    3. Majidi, Ajab Gul & Ramos, Victor & Rosa-Santos, Paulo & das Neves, Luciana & Taveira-Pinto, Francisco, 2025. "Power production assessment of wave energy converters in mainland Portugal," Renewable Energy, Elsevier, vol. 243(C).
    4. Maolin Cheng & Jiano Li & Yun Liu & Bin Liu, 2020. "Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    5. Taylor, James W. & Jeon, Jooyoung, 2018. "Probabilistic forecasting of wave height for offshore wind turbine maintenance," European Journal of Operational Research, Elsevier, vol. 267(3), pages 877-890.
    6. Renken, Volker & Sorg, Michael & Marschner, Volker & Gerdes, Lewin & Gerdes, Gerhard & Fischer, Andreas, 2018. "Geographical comparison between wind power, solar power and demand for the German regions and data filling concepts," Renewable Energy, Elsevier, vol. 126(C), pages 475-484.
    7. Ayob, Mohd Nasir & Castellucci, Valeria & Waters, Rafael, 2017. "Wave energy potential and 1–50 TWh scenarios for the Nordic synchronous grid," Renewable Energy, Elsevier, vol. 101(C), pages 462-466.
    8. Shi, Xueli & Wei, Maoxing & Lin, Yuan & Zheng, Siming & Chen, Jiawang & He, Fang, 2026. "Hotspot selection and complementarity for combined offshore wind and wave energy utilization in the China East Adjacent Seas," Energy, Elsevier, vol. 342(C).
    9. Chenglong Guo & Wanan Sheng & Dakshina G. De Silva & George Aggidis, 2023. "A Review of the Levelized Cost of Wave Energy Based on a Techno-Economic Model," Energies, MDPI, vol. 16(5), pages 1-30, February.
    10. Hameedullah Zaheb & Mikaeel Ahmadi & Nisar Ahmad Rahmany & Mir Sayed Shah Danish & Habibullah Fedayi & Atsushi Yona, 2023. "Optimal Grid Flexibility Assessment for Integration of Variable Renewable-Based Electricity Generation," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
    11. Kluger, Jocelyn M. & Haji, Maha N. & Slocum, Alexander H., 2023. "The power balancing benefits of wave energy converters in offshore wind-wave farms with energy storage," Applied Energy, Elsevier, vol. 331(C).
    12. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    13. Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.
    14. Akdemir, Kerem Ziya & Robertson, Bryson & Oikonomou, Konstantinos & Kern, Jordan & Voisin, Nathalie & Hanif, Sarmad & Bhattacharya, Saptarshi, 2023. "Opportunities for wave energy in bulk power system operations," Applied Energy, Elsevier, vol. 352(C).
    15. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    16. Dragomir, George & Șerban, Alexandru & Năstase, Gabriel & Brezeanu, Alin Ionuț, 2016. "Wind energy in Romania: A review from 2009 to 2016," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 129-143.
    17. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    18. Ştefan Dragoş Cîrstea & Claudia Steluţa Martiş & Andreea Cîrstea & Anca Constantinescu-Dobra & Melinda Timea Fülöp, 2018. "Current Situation and Future Perspectives of the Romanian Renewable Energy," Energies, MDPI, vol. 11(12), pages 1-22, November.
    19. Zhang, Zeguo & Yin, Jianchuan, 2025. "A novel depthwise separable U-Net for large-scale wave field prediction," Renewable Energy, Elsevier, vol. 254(C).
    20. Saheli, Mahdieh Arabzadeh & Lari, Kamran & Salehi, Gholamreza & Azad, Masoud Torabi, 2022. "Techno-economic assessment of a hybrid on grid PV-wave system: A case study in Caspian Sea," Renewable Energy, Elsevier, vol. 186(C), pages 596-608.
    21. Ge, Chang & Yan, Jie & Zhang, Haoran & Li, Yuhao & Wang, Han & Liu, Yongqian, 2024. "Joint short-term power forecasting of hydro-wind-photovoltaic considering spatiotemporal delay of weather processes," Renewable Energy, Elsevier, vol. 237(PB).
    22. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    23. Wang, Mie & Ying, Feixiang & Zhu, Yunlou, 2026. "A novel multistep point and interval prediction framework for accurate short-term wave height estimation incorporating the TCN-GRU-Attention model and error distribution analysis," Renewable Energy, Elsevier, vol. 256(PE).
    24. Robertson, Bryson & Bekker, Jessica & Buckham, Bradley, 2020. "Renewable integration for remote communities: Comparative allowable cost analyses for hydro, solar and wave energy," Applied Energy, Elsevier, vol. 264(C).
    25. Bhattacharya, Saptarshi & Pennock, Shona & Robertson, Bryson & Hanif, Sarmad & Alam, Md Jan E. & Bhatnagar, Dhruv & Preziuso, Danielle & O’Neil, Rebecca, 2021. "Timing value of marine renewable energy resources for potential grid applications," Applied Energy, Elsevier, vol. 299(C).
    26. Flavius Cosmin Darie, "undated". "The Effect of Renewable Energy Consumption in Romania," BASIQ Conference Proceedings 2023:007, Bucharest University of Economic Studies.
    27. Jeon, Jooyoung & Taylor, James W., 2016. "Short-term density forecasting of wave energy using ARMA-GARCH models and kernel density estimation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 991-1004.
    28. Zhou, Jianguo & Zhou, Luming & Zhao, Yunlong & Wu, Kai, 2025. "Significant wave height prediction based on improved fuzzy C-means clustering and bivariate kernel density estimation," Renewable Energy, Elsevier, vol. 245(C).
    29. Satymov, Rasul & Bogdanov, Dmitrii & Dadashi, Mojtaba & Lavidas, George & Breyer, Christian, 2024. "Techno-economic assessment of global and regional wave energy resource potentials and profiles in hourly resolution," Applied Energy, Elsevier, vol. 364(C).
    30. Wang, Mie & Ying, Feixiang & Jia, Jian, 2026. "Ocean wave power flux forecasting using a stacking ensemble of LSTM and LightGBM," Renewable Energy, Elsevier, vol. 256(PI).
    31. Jahangir, Mohammad Hossein & Fakouriyan, Samaneh & Vaziri Rad, Mohammad Amin & Dehghan, Hassan, 2020. "Feasibility study of on/off grid large-scale PV/WT/WEC hybrid energy system in coastal cities: A case-based research," Renewable Energy, Elsevier, vol. 162(C), pages 2075-2095.
    32. Anthony Roy & François Auger & Florian Dupriez-Robin & Salvy Bourguet & Quoc Tuan Tran, 2018. "Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies," Energies, MDPI, vol. 11(7), pages 1-27, July.
    33. Wyckoff, Kevin & Moazeni, Faegheh & Khazaei, Javad & Banerjee, Arindam, 2025. "Economic dispatch of offshore renewable energy resources for islanded communities with optimal storage sizing," Renewable Energy, Elsevier, vol. 241(C).
    34. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.
    35. Huseyin Balta & Zehra Yumurtaci, 2024. "Investigation and Optimization of Integrated Electricity Generation from Wind, Wave, and Solar Energy Sources," Energies, MDPI, vol. 17(3), pages 1-34, January.
    36. Abbasi, S.A. & Tabassum-Abbasi, & Abbasi, Tasneem, 2016. "Impact of wind-energy generation on climate: A rising spectre," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1591-1598.

  4. Parkinson, Simon C. & Dragoon, Ken & Reikard, Gordon & García-Medina, Gabriel & Özkan-Haller, H. Tuba & Brekken, Ted K.A., 2015. "Integrating ocean wave energy at large-scales: A study of the US Pacific Northwest," Renewable Energy, Elsevier, vol. 76(C), pages 551-559.

    Cited by:

    1. Ponce de León, S. & Orfila, A. & Simarro, G., 2016. "Wave energy in the Balearic Sea. Evolution from a 29 year spectral wave hindcast," Renewable Energy, Elsevier, vol. 85(C), pages 1192-1200.
    2. Sinfield, J.V. & Ajmani, A. & McShane, W., 2024. "Strategic roadmapping to accelerate and risk-mitigate enabling innovations: A generalizable method and a case illustration for marine renewable energy," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    3. Xu, Xinxin & Robertson, Bryson & Buckham, Bradley, 2020. "A techno-economic approach to wave energy resource assessment and development site identification," Applied Energy, Elsevier, vol. 260(C).
    4. Bailey, Helen & Robertson, Bryson & Buckham, Bradley, 2018. "Variability and stochastic simulation of power from wave energy converter arrays," Renewable Energy, Elsevier, vol. 115(C), pages 721-733.
    5. Robertson, Bryson & Bailey, Helen & Leary, Matthew & Buckham, Bradley, 2021. "A methodology for architecture agnostic and time flexible representations of wave energy converter performance," Applied Energy, Elsevier, vol. 287(C).
    6. Kilcher, Levi & García Medina, Gabriel & Yang, Zhaoqing, 2023. "A scalable wave resource assessment methodology: Application to U.S. waters," Renewable Energy, Elsevier, vol. 217(C).
    7. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.
    8. Sánchez, Antonio Santos & Rodrigues, Diego Arruda & Fontes, Raony Maia & Martins, Márcio Fernandes & Kalid, Ricardo de Araújo & Torres, Ednildo Andrade, 2018. "Wave resource characterization through in-situ measurement followed by artificial neural networks' modeling," Renewable Energy, Elsevier, vol. 115(C), pages 1055-1066.
    9. Pasquale Contestabile & Vincenzo Ferrante & Diego Vicinanza, 2015. "Wave Energy Resource along the Coast of Santa Catarina (Brazil)," Energies, MDPI, vol. 8(12), pages 1-25, December.
    10. Humberto Verdejo & Almendra Awerkin & Wolfgang Kliemann & Cristhian Becker & Héctor Chávez & Karina A. Barbosa & José Delpiano, 2019. "A Dynamic Stochastic Hybrid Model to Represent Significant Wave Height and Wave Period for Marine Energy Representation," Energies, MDPI, vol. 12(5), pages 1-15, March.
    11. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    12. Robertson, Bryson & Bekker, Jessica & Buckham, Bradley, 2020. "Renewable integration for remote communities: Comparative allowable cost analyses for hydro, solar and wave energy," Applied Energy, Elsevier, vol. 264(C).

  5. Pinson, P. & Reikard, G. & Bidlot, J.-R., 2012. "Probabilistic forecasting of the wave energy flux," Applied Energy, Elsevier, vol. 93(C), pages 364-370.

    Cited by:

    1. Carrelhas, A.A.D. & Gato, L.M.C. & Henriques, J.C.C. & Falcão, A.F.O. & Varandas, J., 2019. "Test results of a 30 kW self-rectifying biradial air turbine-generator prototype," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 187-198.
    2. He, Fang & Huang, Zhenhua & Law, Adrian Wing-Keung, 2013. "An experimental study of a floating breakwater with asymmetric pneumatic chambers for wave energy extraction," Applied Energy, Elsevier, vol. 106(C), pages 222-231.
    3. López-Ruiz, Alejandro & Bergillos, Rafael J. & Lira-Loarca, Andrea & Ortega-Sánchez, Miguel, 2018. "A methodology for the long-term simulation and uncertainty analysis of the operational lifetime performance of wave energy converter arrays," Energy, Elsevier, vol. 153(C), pages 126-135.
    4. López-Ruiz, Alejandro & Bergillos, Rafael J. & Ortega-Sánchez, Miguel, 2016. "The importance of wave climate forecasting on the decision-making process for nearshore wave energy exploitation," Applied Energy, Elsevier, vol. 182(C), pages 191-203.
    5. Zheng, C.W. & Li, C.Y., 2017. "Propagation characteristic and intraseasonal oscillation of the swell energy of the Indian Ocean," Applied Energy, Elsevier, vol. 197(C), pages 342-353.
    6. Veigas, M. & López, M. & Iglesias, G., 2014. "Assessing the optimal location for a shoreline wave energy converter," Applied Energy, Elsevier, vol. 132(C), pages 404-411.
    7. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Alireza Shadmani & Mohammad Reza Nikoo & Riyadh I. Al-Raoush & Nasrin Alamdari & Amir H. Gandomi, 2022. "The Optimal Configuration of Wave Energy Conversions Respective to the Nearshore Wave Energy Potential," Energies, MDPI, vol. 15(20), pages 1-29, October.
    10. Ayob, Mohd Nasir & Castellucci, Valeria & Waters, Rafael, 2017. "Wave energy potential and 1–50 TWh scenarios for the Nordic synchronous grid," Renewable Energy, Elsevier, vol. 101(C), pages 462-466.
    11. Xu, Xinxin & Robertson, Bryson & Buckham, Bradley, 2020. "A techno-economic approach to wave energy resource assessment and development site identification," Applied Energy, Elsevier, vol. 260(C).
    12. Bubbar, K. & Buckham, B., 2018. "On establishing an analytical power capture limit for self-reacting point absorber wave energy converters based on dynamic response," Applied Energy, Elsevier, vol. 228(C), pages 324-338.
    13. Sun, Peidong & Xu, Bin & Wang, Jichao, 2022. "Long-term trend analysis and wave energy assessment based on ERA5 wave reanalysis along the Chinese coastline," Applied Energy, Elsevier, vol. 324(C).
    14. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.
    15. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    16. Zhang, H.C. & Xu, D.L. & Liu, C.R. & Wu, Y.S., 2016. "Wave energy absorption of a wave farm with an array of buoys and flexible runway," Energy, Elsevier, vol. 109(C), pages 211-223.
    17. Neill, Simon P. & Hashemi, M. Reza, 2013. "Wave power variability over the northwest European shelf seas," Applied Energy, Elsevier, vol. 106(C), pages 31-46.
    18. Zhang, Haicheng & Xu, Daolin & Zhao, Huai & Xia, Shuyan & Wu, Yousheng, 2018. "Energy extraction of wave energy converters embedded in a very large modularized floating platform," Energy, Elsevier, vol. 158(C), pages 317-329.
    19. Liang, Bingchen & Shao, Zhuxiao & Wu, Guoxiang & Shao, Meng & Sun, Jinwei, 2017. "New equations of wave energy assessment accounting for the water depth," Applied Energy, Elsevier, vol. 188(C), pages 130-139.
    20. Liang, Bingchen & Fan, Fei & Liu, Fushun & Gao, Shanhong & Zuo, Hongyan, 2014. "22-Year wave energy hindcast for the China East Adjacent Seas," Renewable Energy, Elsevier, vol. 71(C), pages 200-207.
    21. Keskin Citiroglu, H. & Okur, A., 2014. "An approach to wave energy converter applications in Eregli on the western Black Sea coast of Turkey," Applied Energy, Elsevier, vol. 135(C), pages 738-747.
    22. Zhao, Ming & Ning, Dezhi, 2024. "A review of numerical methods for studying hydrodynamic performance of oscillating water column (OWC) devices," Renewable Energy, Elsevier, vol. 233(C).
    23. Younesian, Davood & Alam, Mohammad-Reza, 2017. "Multi-stable mechanisms for high-efficiency and broadband ocean wave energy harvesting," Applied Energy, Elsevier, vol. 197(C), pages 292-302.
    24. Huang, Sy-Ruen & Chen, Hong-Tai & Chung, Chih-Hung & Chu, Chen-Yeon & Li, Gung-Ching & Wu, Chueh-Cheng, 2012. "Multivariable direct-drive linear generators for wave energy," Applied Energy, Elsevier, vol. 100(C), pages 112-117.
    25. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    26. Zilong, Ti & Yubing, Song & Xiaowei, Deng, 2022. "Spatial-temporal wave height forecast using deep learning and public reanalysis dataset," Applied Energy, Elsevier, vol. 326(C).
    27. Humberto Verdejo & Almendra Awerkin & Wolfgang Kliemann & Cristhian Becker & Héctor Chávez & Karina A. Barbosa & José Delpiano, 2019. "A Dynamic Stochastic Hybrid Model to Represent Significant Wave Height and Wave Period for Marine Energy Representation," Energies, MDPI, vol. 12(5), pages 1-15, March.
    28. Xiaoying Ren & Yongqian Liu & Fei Zhang & Lingfeng Li, 2024. "A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection," Energies, MDPI, vol. 17(16), pages 1-25, August.
    29. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    30. Robertson, Bryson & Bekker, Jessica & Buckham, Bradley, 2020. "Renewable integration for remote communities: Comparative allowable cost analyses for hydro, solar and wave energy," Applied Energy, Elsevier, vol. 264(C).
    31. Ning, De-Zhi & Wang, Rong-Quan & Zou, Qing-Ping & Teng, Bin, 2016. "An experimental investigation of hydrodynamics of a fixed OWC Wave Energy Converter," Applied Energy, Elsevier, vol. 168(C), pages 636-648.
    32. Roy, Sanjoy, 2021. "Analytical estimates of short duration mean power output and variability for deepwater wave power generation," Energy, Elsevier, vol. 230(C).
    33. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    34. Jeon, Jooyoung & Taylor, James W., 2016. "Short-term density forecasting of wave energy using ARMA-GARCH models and kernel density estimation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 991-1004.
    35. Wang, Mie & Ying, Feixiang & Jia, Jian, 2026. "Ocean wave power flux forecasting using a stacking ensemble of LSTM and LightGBM," Renewable Energy, Elsevier, vol. 256(PI).
    36. Gao, Yuping & Shao, Shuangquan & Zou, Huiming & Tang, Mingsheng & Xu, Hongbo & Tian, Changqing, 2016. "A fully floating system for a wave energy converter with direct-driven linear generator," Energy, Elsevier, vol. 95(C), pages 99-109.
    37. Son, Daewoong & Yeung, Ronald W., 2017. "Optimizing ocean-wave energy extraction of a dual coaxial-cylinder WEC using nonlinear model predictive control," Applied Energy, Elsevier, vol. 187(C), pages 746-757.
    38. Zheng, Chong Wei & Wang, Qing & Li, Chong Yin, 2017. "An overview of medium- to long-term predictions of global wave energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1492-1502.
    39. Cheng, Yong & Ji, Chunyan & Zhai, Gangjun, 2019. "Fully nonlinear analysis incorporating viscous effects for hydrodynamics of an oscillating wave surge converter with nonlinear power take-off system," Energy, Elsevier, vol. 179(C), pages 1067-1081.

  6. Gordon Reikard, 2011. "Total Factor Productivity And R&D In The Production Function," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 601-613.

    Cited by:

    1. Grzelak Maria M. & Laskowska Iwona & Roszko-Wójtowicz Elżbieta, 2018. "The Importance of Research and Development Expenditure for the Competitiveness of Manufacturing Enterprises in Poland. The Application of Panel Models," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(3), pages 9-22, September.
    2. Arpita Ghose & Chandrima Chakraborti, 2013. "The Relative Role of Imports and Exports in Explaining Productivity of Indian Bio-Pharmaceutical Firms: Evidence from Non Parametric Data Envelopment Analysis," Foreign Trade Review, , vol. 48(2), pages 165-201, May.
    3. Crespi, Gustavo & Giuliodori, David & Giuliodori, Roberto & Rodriguez, Alejandro, 2016. "The effectiveness of tax incentives for R&D+i in developing countries: The case of Argentina," Research Policy, Elsevier, vol. 45(10), pages 2023-2035.

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