Research on the technical framework and critical path of new energy portfolio prediction based on multi-algorithm fusion

[1] Keyhani, A., Marwali, M. N., & Dai, M. (2009). Integration of green and renewable energy in electric power systems. John Wiley & Sons. Keyhani A. Marwali M. N. Dai M. ( 2009 ). Integration of green and renewable energy in electric power systems . John Wiley & Sons . Search in Google Scholar

[2] Infield, D., & Freris, L. (2020). Renewable energy in power systems. John Wiley & Sons. Infield D. Freris L. ( 2020 ). Renewable energy in power systems . John Wiley & Sons . Search in Google Scholar

[3] Bajpai, P., & Dash, V. (2012). Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renewable and Sustainable Energy Reviews, 16(5), 2926-2939. Bajpai P. Dash V. ( 2012 ). Hybrid renewable energy systems for power generation in stand-alone applications: A review . Renewable and Sustainable Energy Reviews , 16 ( 5 ), 2926 - 2939 . Search in Google Scholar

[4] Gandoman, F. H., Ahmadi, A., Sharaf, A. M., Siano, P., Pou, J., Hredzak, B., & Agelidis, V. G. (2018). Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems. Renewable and sustainable energy reviews, 82, 502-514. Gandoman F. H. Ahmadi A. Sharaf A. M. Siano P. Pou J. Hredzak B. Agelidis V. G. ( 2018 ). Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems . Renewable and sustainable energy reviews , 82 , 502 - 514 . Search in Google Scholar

[5] Ahmed, A., & Khalid, M. (2019). A review on the selected applications of forecasting models in renewable power systems. Renewable and Sustainable Energy Reviews, 100, 9-21. Ahmed A. Khalid M. ( 2019 ). A review on the selected applications of forecasting models in renewable power systems . Renewable and Sustainable Energy Reviews , 100 , 9 - 21 . Search in Google Scholar

[6] Calleja-Sanz, G., Olivella-Nadal, J., & Solé-Parellada, F. (2020). Technology forecasting: Recent trends and new methods. Research methodology in management and industrial engineering, 45-69. Calleja-Sanz G. Olivella-Nadal J. Solé-Parellada F. ( 2020 ). Technology forecasting: Recent trends and new methods . Research methodology in management and industrial engineering , 45 - 69 . Search in Google Scholar

[7] Obrecht, M., & Denac, M. (2016). Technology forecast of sustainable energy development prospects. Futures, 84, 12-22. Obrecht M. Denac M. ( 2016 ). Technology forecast of sustainable energy development prospects . Futures , 84 , 12 - 22 . Search in Google Scholar

[8] Tsai, S. B., Xue, Y., Zhang, J., Chen, Q., Liu, Y., Zhou, J., & Dong, W. (2017). Models for forecasting growth trends in renewable energy. Renewable and Sustainable Energy Reviews, 77, 1169-1178. Tsai S. B. Xue Y. Zhang J. Chen Q. Liu Y. Zhou J. Dong W. ( 2017 ). Models for forecasting growth trends in renewable energy . Renewable and Sustainable Energy Reviews , 77 , 1169 - 1178 . Search in Google Scholar

[9] Kariniotakis, G. (Ed.). (2017). Renewable energy forecasting: from models to applications. Woodhead Publishing. Kariniotakis G. (Ed.). ( 2017 ). Renewable energy forecasting: from models to applications . Woodhead Publishing . Search in Google Scholar

[10] Sharifzadeh, M., Sikinioti-Lock, A., & Shah, N. (2019). Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression. Renewable and Sustainable Energy Reviews, 108, 513-538. Sharifzadeh M. Sikinioti-Lock A. Shah N. ( 2019 ). Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression . Renewable and Sustainable Energy Reviews , 108 , 513 - 538 . Search in Google Scholar

[11] Zendehboudi, A., Baseer, M. A., & Saidur, R. (2018). Application of support vector machine models for forecasting solar and wind energy resources: A review. Journal of cleaner production, 199, 272-285. Zendehboudi A. Baseer M. A. Saidur R. ( 2018 ). Application of support vector machine models for forecasting solar and wind energy resources: A review . Journal of cleaner production , 199 , 272 - 285 . Search in Google Scholar

[12] Oğcu, G., Demirel, O. F., & Zaim, S. (2012). Forecasting electricity consumption with neural networks and support vector regression. Procedia-Social and Behavioral Sciences, 58, 1576-1585. Oğcu G. Demirel O. F. Zaim S. ( 2012 ). Forecasting electricity consumption with neural networks and support vector regression . Procedia-Social and Behavioral Sciences , 58 , 1576 - 1585 . Search in Google Scholar

[13] De Leone, R., Pietrini, M., & Giovannelli, A. (2015). Photovoltaic energy production forecast using support vector regression. Neural Computing and Applications, 26(8), 1955-1962. De Leone R. Pietrini M. Giovannelli A. ( 2015 ). Photovoltaic energy production forecast using support vector regression . Neural Computing and Applications , 26 ( 8 ), 1955 - 1962 . Search in Google Scholar

[14] Zafirakis, D., Tzanes, G., & Kaldellis, J. K. (2019). Forecasting of wind power generation with the use of artificial neural networks and support vector regression models. Energy Procedia, 159, 509-514. Zafirakis D. Tzanes G. Kaldellis J. K. ( 2019 ). Forecasting of wind power generation with the use of artificial neural networks and support vector regression models . Energy Procedia , 159 , 509 - 514 . Search in Google Scholar

[15] Shang, C., & Wei, P. (2018). Enhanced support vector regression based forecast engine to predict solar power output. Renewable energy, 127, 269-283. Shang C. Wei P. ( 2018 ). Enhanced support vector regression based forecast engine to predict solar power output . Renewable energy , 127 , 269 - 283 . Search in Google Scholar

[16] Kramer, O., & Gieseke, F. (2011, April). Short-term wind energy forecasting using support vector regression. In Soft computing models in industrial and environmental applications, 6th International Conference SOCO 2011 (pp. 271-280). Berlin, Heidelberg: Springer Berlin Heidelberg. Kramer O. Gieseke F. ( 2011 , April ). Short-term wind energy forecasting using support vector regression . In Soft computing models in industrial and environmental applications, 6th International Conference SOCO 2011 (pp. 271 - 280 ). Berlin, Heidelberg : Springer Berlin Heidelberg . Search in Google Scholar

[17] Zhu, J., Dong, H., Zheng, W., Li, S., Huang, Y., & Xi, L. (2022). Review and prospect of data-driven techniques for load forecasting in integrated energy systems. Applied Energy, 321, 119269. Zhu J. Dong H. Zheng W. Li S. Huang Y. Xi L. ( 2022 ). Review and prospect of data-driven techniques for load forecasting in integrated energy systems . Applied Energy , 321 , 119269 . Search in Google Scholar

[18] Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D., & Tjortjis, C. (2024). Energy Forecasting: A Comprehensive Review of Techniques and Technologies. Energies, 17(7), 1662. Mystakidis A. Koukaras P. Tsalikidis N. Ioannidis D. Tjortjis C. ( 2024 ). Energy Forecasting: A Comprehensive Review of Techniques and Technologies . Energies , 17 ( 7 ), 1662 . Search in Google Scholar

[19] Ssekulima, E. B., Anwar, M. B., Al Hinai, A., & El Moursi, M. S. (2016). Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review. IET Renewable Power Generation, 10(7), 885-989. Ssekulima E. B. Anwar M. B. Al Hinai A. El Moursi M. S. ( 2016 ). Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review . IET Renewable Power Generation , 10 ( 7 ), 885 - 989 . Search in Google Scholar

[20] Saxena, A., Shankar, R., El-Saadany, E., Kumar, M., Al Zaabi, O., Al Hosani, K., & Muduli, U. R. (2024). Intelligent load forecasting and renewable energy integration for enhanced grid reliability. IEEE Transactions on Industry Applications. Saxena A. Shankar R. El-Saadany E. Kumar M. Al Zaabi O. Al Hosani K. Muduli U. R. ( 2024 ). Intelligent load forecasting and renewable energy integration for enhanced grid reliability . IEEE Transactions on Industry Applications . Search in Google Scholar

[21] Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and sustainable energy reviews, 16(2), 1223-1240. Suganthi L. Samuel A. A. ( 2012 ). Energy models for demand forecasting—A review . Renewable and sustainable energy reviews , 16 ( 2 ), 1223 - 1240 . Search in Google Scholar

[22] Ghalehkhondabi, I., Ardjmand, E., Weckman, G. R., & Young, W. A. (2017). An overview of energy demand forecasting methods published in 2005–2015. Energy Systems, 8, 411-447. Ghalehkhondabi I. Ardjmand E. Weckman G. R. Young W. A. ( 2017 ). An overview of energy demand forecasting methods published in 2005–2015 . Energy Systems , 8 , 411 - 447 . Search in Google Scholar

[23] Qin Qian,Mingjing Lu,Anhai Zhong,Feng Yang,Wenjun He & Min Li. (2024). Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model. Energy Engineering(8),2167-2190. Qin Qian Mingjing Lu Anhai Zhong Feng Yang Wenjun He Min Li ( 2024 ). Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model . Energy Engineering(8) , 2167 - 2190 . Search in Google Scholar

[24] Mingyuan Li,Yuanli Gu,Qingqiao Geng & Hongru Yu. (2024). A Combination Prediction Model for Short Term Travel Demand of Urban Taxi. Computers, Materials & Continua(3),3877-3896. Mingyuan Li Yuanli Gu Qingqiao Geng Hongru Yu ( 2024 ). A Combination Prediction Model for Short Term Travel Demand of Urban Taxi . Computers, Materials & Continua(3) , 3877 - 3896 . Search in Google Scholar

[25] Lili Ye,Naiming Xie,John E. Boylan & Zhongju Shang. (2024). Forecasting seasonal demand for retail: A Fourier time-varying grey model. International Journal of Forecasting(4),1467-1485. Lili Ye Naiming Xie John E. Boylan Zhongju Shang ( 2024 ). Forecasting seasonal demand for retail: A Fourier time-varying grey model . International Journal of Forecasting(4) , 1467 - 1485 . Search in Google Scholar