create a website

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey. (2024). Oyucu, Saadin ; Guler, Merve ; Guerrero, Josep M ; Unsal, Derya Betul ; Aksoz, Ahmet.
In: Sustainability.
RePEc:gam:jsusta:v:16:y:2024:i:7:p:2894-:d:1367360.

Full description at Econpapers || Download paper

Cited: 1

Citations received by this document

Cites: 52

References cited by this document

Cocites: 50

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. Hybrid AI-Based Framework for Renewable Energy Forecasting: One-Stage Decomposition and Sample Entropy Reconstruction with Least-Squares Regression. (2025). Zemouri, Nahed ; Mezaache, Hatem ; Zemali, Zakaria ; Angiulli, Giovanni ; Versaci, Mario ; la Foresta, Fabio.
    In: Energies.
    RePEc:gam:jeners:v:18:y:2025:i:11:p:2942-:d:1671170.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. Çıkılı, E.B. Güneş Panellerinin Temel Tasarımında Mevcut yöntemlerin Değerlendirilmesi; İstanbul Üniversitesi Fen Bilimleri Enstitüsü: İstanbul, Türkiye, 2017.
    Paper not yet in RePEc: Add citation now
  2. Abisoye, O.; Sun, Y.; Zenghui, W. A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights. Renew. Energy Focus 2024, 48, 100529. [CrossRef]
    Paper not yet in RePEc: Add citation now
  3. Ahmed, A.; Khalid, M. A review on the selected applications of forecasting models in renewable power systems. Renew. Sustain. Energy Rev. 2019, 100, 9–21. [CrossRef]

  4. Aksoz, A. An Optimized Overcurrent Protection Study Using Enough Number of SFCL at Optimal Points of a Distributed Real City Grid. Tehnički Vjesnik 2021, 28, 104–112.
    Paper not yet in RePEc: Add citation now
  5. Al-Selwi, S.; Hassan, M.; Muneer, A. LSTM Inefficiency in Long-Term Dependencies Regression Problems. J. Adv. Res. Appl. Sci. Eng. Technol. 2023, 30, 16–31. [CrossRef]
    Paper not yet in RePEc: Add citation now
  6. Amarasinghe, K.; Marino, D.L.; Manic, M. Deep neural networks for energy load forecasting. In Proceedings of the International Symposium on Industrial Electronics, Edinburgh, UK, 19–21 June 2017; pp. 1483–1488. [CrossRef] Sustainability 2024, 16, 2894 25 of 26
    Paper not yet in RePEc: Add citation now
  7. Aparna, S. Long Short Term Memory and Rolling Window Technique for Modeling Power Demand Prediction. In Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 14–15 June 2018; pp. 1675–1678.
    Paper not yet in RePEc: Add citation now
  8. Arıcı, N.; Iskender, A. Problems and Solutions of Grid-Connected in Photovoltaic Solar Plants. Politek. Derg. 2020, 900, 215–222. [CrossRef]
    Paper not yet in RePEc: Add citation now
  9. Benitez, I.B.; Ibañez, J.A.; Lumabad, C.I.D.; Cañete, J.M.; Principe, J.A. Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines. Energies 2023, 16, 7823. [CrossRef]
    Paper not yet in RePEc: Add citation now
  10. Chen, Y.; Wang, K. Prediction of Satellite Time Series Data Based on Long Short Term Memory- Autoregressive Integrated Moving Average Model (LSTM-ARIMA). In Proceedings of the 2019 IEEE 4th International Conference on Signal and Image Processing, Wuxi, China, 19–21 July 2019; pp. 308–312.
    Paper not yet in RePEc: Add citation now
  11. Easley, M.; Haney, L.; Paul, J.; Fowler, K.; Wu, H. Deep neural networks for short-term load forecasting in ERCOT system. In Proceedings of the 2018 IEEE Texas Power Energy Conference (TPEC 2018), College Station, TX, USA, 8–9 February 2018; pp. 1–6. [CrossRef]
    Paper not yet in RePEc: Add citation now
  12. Erdem, R.; Atalay, O.; Yorgun, B. Fotovoltaïk (pv) güneş enerjïsï sïstemlerï ve çati uygulamaları. In Proceedings of the 8th Güneş Enerjisi Sistemleri Sempozyumu ve Sergisi, Mersin, Türkiye, 8–9 November 2019.
    Paper not yet in RePEc: Add citation now
  13. Erkul, A. Monokristal, Polikristal ve Amorf-Silisyum Güneş Panellerinin Verimliğinin Incelenmesi ve Aydınlatma Sistemi Uygulaması; Gazi Üniversitesi Fen Bilimleri Enstitüsü: Ankara, Türkiye, 2010.
    Paper not yet in RePEc: Add citation now
  14. Forootan, M.M.; Larki, I.; Zahedi, R.; Ahmadi, A. Machine Learning and Deep Learning in Energy Systems: A Review. Sustainability 2022, 14, 4832. [CrossRef]
    Paper not yet in RePEc: Add citation now
  15. Hao, J.; Liu, Y.; Gu, H.; Yang, D.; Wang, R.; Lei, J. Short-Term Power Load Forecasting for Larger Consumer Based on TensorFlow Deep Learning Framework and Clustering-Regression Model. In Proceedings of the 2nd IEEE Conference on Energy Internet and Energy System Integration, (EI2), Beijing, China, 20–22 October 2018. [CrossRef]
    Paper not yet in RePEc: Add citation now
  16. Hosein, S.; Hosein, P. Load forecasting using deep neural networks. In Proceedings of the 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 23–26 April 2017. [CrossRef]
    Paper not yet in RePEc: Add citation now
  17. Huang, B.; Wu, D.; Lai, C.; Cun, X.; Yuan, H.; Xu, F.; Lai, L. Load Forecasting based on Deep Long Short-term Memory with Consideration of Costing Correlated Factor. In Proceedings of the IEEE 16th International Conference on Industrial Informatics, (INDIN), Porto, Portugal, 18–20 July 2018; pp. 496–501. [CrossRef]
    Paper not yet in RePEc: Add citation now
  18. Huang, C.J.; Kuo, P.H. Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access 2019, 7, 74822–74834. [CrossRef]
    Paper not yet in RePEc: Add citation now
  19. Hui, X.; Qun, W.; Yao, L.; Yingbin, Z.; Lei, S.; Zhisheng, Z. Short-term load forecasting model based on deep neural network. In Proceedings of the 2017 2nd International Conference on Power and Renewable Energy (ICPRE), Chengdu, China, 20–23 September 2017; pp. 589–591. [CrossRef]
    Paper not yet in RePEc: Add citation now
  20. Jiang, Q.; Zhu, J.-X.; Li, M.; Qing, H.-Y. Electricity Power Load Forecast via Long Short-Term Memory Recurrent Neural Networks. In Proceedings of the 2018 4th Annual International Conference on Network and Information Systems for Computers, Wuhan, China, 19–21 April 2019; pp. 265–268. [CrossRef]
    Paper not yet in RePEc: Add citation now
  21. Jiao, R.; Huang, X.; Ma, X.; Han, L.; Tian, W. A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-Term Wind Power Forecasting. IEEE Access 2018, 6, 17851–17858. [CrossRef]
    Paper not yet in RePEc: Add citation now
  22. Jurj, D.I.; Micu, D.D.; Muresan, A. Overview of Electrical Energy Forecasting Methods and Models in Renewable Energy. In Proceedings of the International Conference and Exposition on Electrical and Power Engineering, Iasi, Romania, 18–19 October 2018; pp. 87–90.
    Paper not yet in RePEc: Add citation now
  23. Kalogirou, S.A. Solar Energy Engineering: Processes and Systems; Academic Press: Cambridge, MA, USA, 2009.
    Paper not yet in RePEc: Add citation now
  24. Kandemir, C.; Bayrak, M. Fotovoltaik sistemler şebekeye bağlı olduğunda oluşan sorunlar. In Proceedings of the 6th Enerji Verimliliği, Kalitesi Sempozyumu ve Sergisi, Sakarya, Türkiye, 4–6 June 2015.
    Paper not yet in RePEc: Add citation now
  25. Khair, U.; Fahmi, H.; Hakim, A.; Rahim, R. Forecasting error calculation with mean absolute deviation and mean absolute percentage error. J. Phys. Conf. Ser. 2017, 930, 012002. [CrossRef]
    Paper not yet in RePEc: Add citation now
  26. Kim, T.-Y.; Cho, S.-B. Optimizing CNN-LSTM neural networks with PSO for anomalous query access control. Neurocomputing 2021, 456, 666–677. [CrossRef]
    Paper not yet in RePEc: Add citation now
  27. Kollia, I.; Kollias, S. A Deep Learning Approach for Load Demand Forecasting of Power Systems. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, Bangalore, India, 18–21 November 2018; pp. 912–919. [CrossRef]
    Paper not yet in RePEc: Add citation now
  28. Kong, W.; Dong, Z.; Luo, F.; Meng, K.; Zhang, W.; Wang, F.; Zhao, X. Effect of automatic hyperparameter tuning for residential load forecasting via deep learning. In Proceedings of the 2017 Australasian Universities Power Engineering Conference (AUPEC 2017), Melbourne, Australia, 19–22 November 2017; pp. 1–6. [CrossRef]
    Paper not yet in RePEc: Add citation now
  29. Kumar, S.; Hussain, L.; Banarjee, S.; Reza, M. Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster. In Proceedings of the 5th International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, India, 12–13 January 2018; pp. 1–4. [CrossRef]
    Paper not yet in RePEc: Add citation now
  30. Kuster, C.; Rezgui, Y.; Mourshed, M. Electrical load forecasting models: A critical systematic review. Sustain. Cities Soc. 2017, 35, 257–270. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
    Paper not yet in RePEc: Add citation now
  31. Li, L.; Ota, K.; Dong, M. When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid. IEEE Commun. Mag. 2017, 55, 46–51. [CrossRef]
    Paper not yet in RePEc: Add citation now
  32. Migabo, M.E.; Djouani, K.; Kurien, A.M.; Olwal, T.O. A stochastic energy consumption model for wireless sensor networks using GBR techniques. In Proceedings of the AFRICON 2015, Addis Ababa, Ethiopia, 14–17 September 2015; pp. 1–5. [CrossRef] Sustainability 2024, 16, 2894 26 of 26
    Paper not yet in RePEc: Add citation now
  33. Mirjalili, M.A.; Aslani, A.; Zahedi, R.; Soleimani, M. A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system. Sustain. Energy Res. 2023, 10, 8. [CrossRef]
    Paper not yet in RePEc: Add citation now
  34. Mustaqeem; Ishaq, M.; Kwon, S. Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach. IEEE Access 2021, 9, 94262–94271. [CrossRef]
    Paper not yet in RePEc: Add citation now
  35. Nichiforov, C.; Stamatescu, G.; Stamatescu, I.; Calofir, V.; Fagarasan, I.; Iliescu, S.S. Deep learning techniques for load forecasting in large commercial buildings. In Proceedings of the 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC 2018), Sinaia, Romania, 10–12 October 2018; pp. 492–497. [CrossRef]
    Paper not yet in RePEc: Add citation now
  36. Oyucu, S.; Doğan, F.; Aksöz, A.; Biçer, E. Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles. Appl. Sci. 2024, 14, 2306. [CrossRef]
    Paper not yet in RePEc: Add citation now
  37. Paluszek, M.E.H.; Thomas, S. Practical MATLAB Deep Learning; Apress: Berkeley, CA, USA, 2020; Volume 5.
    Paper not yet in RePEc: Add citation now
  38. pdf (accessed on 9 March 2023).
    Paper not yet in RePEc: Add citation now
  39. Rajabi, R.; Estebsari, A. Deep learning based forecasting of individual residential loads using recurrence plots. In Proceedings of the 2019 IEEE Milan PowerTech, PowerTech, Milan, Italy, 23–27 June 2019; pp. 1–5. [CrossRef]
    Paper not yet in RePEc: Add citation now
  40. Süleyman, A.D.A.K.; Cangi, H.; Yılmaz, A.S. Fotovoltaik Sistemin Çıkış Gücünün Sıcaklık ve Işımaya Bağlı Matematiksel Modellemesi ve Simülasyonu. Int. J. Eng. Res. Dev. 2019, 11, 316–327.
    Paper not yet in RePEc: Add citation now
  41. Song, Z.; Cao, Z.; Wan, C.; Xu, S. An Ensemble Wavelet Deep Learning Approach for Short-term Load Forecasting. In Proceedings of the 2019 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), Chengdu, China, 21–24 May 2019; pp. 1–6.
    Paper not yet in RePEc: Add citation now
  42. Toubeau, J.F.; Bottieau, J.; Vallee, F.; Greve, D.Z. Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets. IEEE Trans. Power Syst. 2019, 34, 1203–1215. [CrossRef]
    Paper not yet in RePEc: Add citation now
  43. Wang, S.; Sun, Y.; Zhai, S.; Hou, D.; Wang, P.; Wu, X. Ultra-short-term wind power forecasting based on deep belief network. In Proceedings of the Chinese Control Conference (CCC), Guangzhou, China, 27–30 July 2019; pp. 7479–7483. [CrossRef]
    Paper not yet in RePEc: Add citation now
  44. Wang, Y.J.; Lu, C.; Li, D.W. Short term load forecasting based on fuzzy clustering. Appl. Mech. Mater. 2014, 672–674, 1413–1420. [CrossRef]
    Paper not yet in RePEc: Add citation now
  45. Woo, S.; Park, J.; Park, J. Predicting Wind Turbine Power and Load Outputs by Multi-task Convolutional LSTM Model. In Proceedings of the IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018; pp. 1–5.
    Paper not yet in RePEc: Add citation now
  46. World Solar Energy Efficiency Map. EVANERGY. Available online: https://en.evanergy.com.tr/detay/407-world-solar-energyefficiency -map (accessed on 29 September 2023).
    Paper not yet in RePEc: Add citation now
  47. Wu, J. Introduction to convolutional neural networks. Natl. Key Lab. Nov. Softw. Technol. 2017, 5, 1–31.
    Paper not yet in RePEc: Add citation now
  48. Yang, P.; Zhao, L.; Li, Z. A practical method of unit commitment considering wind power. In Proceedings of the 2010 World Non-Grid-Connected Wind Power Energy Conference (WNWEC), Nanjing, China, 5–7 November 2010; pp. 84–90. [CrossRef]
    Paper not yet in RePEc: Add citation now
  49. Zazo, R.; Lozano-Diez, A.; Gonzalez-Dominguez, J.; Toledano, D.T.; Gonzalez-Rodriguez, J. Language identification in short utterances using long short-term memory (LSTM) recurrent neural networks. PLoS ONE 2016, 11, e0146917. [CrossRef] [PubMed]
    Paper not yet in RePEc: Add citation now
  50. Zhang, D.; Gong, Y. The comparison of LightGBM and XGBoost coupling factor analysis and prediagnosis of acute liver failure. IEEE Access 2020, 8, 220990–221003. [CrossRef]
    Paper not yet in RePEc: Add citation now
  51. Zhang, W.; Quan, H.; Srinivasan, D. An Improved Quantile Regression Neural Network for Probabilistic Load Forecasting. IEEE Trans. Smart Grid 2019, 10, 4425–4434. [CrossRef]
    Paper not yet in RePEc: Add citation now
  52. Zhu, J.; Yang, Z.; Chang, Y.; Guo, Y.; Zhu, K.; Zhang, J. A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction. In Proceedings of the 2019 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), Chengdu, China, 21–24 May 2019; pp. 3531–3536.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. Strategies of a Wind–Solar–Storage System in Jiangxi Province Using the LEAP–NEMO Framework for Achieving Carbon Peaking Goals. (2025). Wattana, Supannika ; Lei, Mingze ; Chen, Tao ; Yang, Caixia ; Xiao, Yao.
    In: Energies.
    RePEc:gam:jeners:v:18:y:2025:i:5:p:1135-:d:1599625.

    Full description at Econpapers || Download paper

  2. Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review. (2025). Yuan, Yuxing ; Du, Tao ; Cao, Hang ; Yan, SU ; Zhang, Lei.
    In: Energies.
    RePEc:gam:jeners:v:18:y:2025:i:10:p:2465-:d:1653434.

    Full description at Econpapers || Download paper

  3. Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models. (2025). Luo, Yuman ; Yang, Shaomei.
    In: Energy.
    RePEc:eee:energy:v:316:y:2025:i:c:s0360544225001872.

    Full description at Econpapers || Download paper

  4. A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey. (2024). Oyucu, Saadin ; Guler, Merve ; Guerrero, Josep M ; Unsal, Derya Betul ; Aksoz, Ahmet.
    In: Sustainability.
    RePEc:gam:jsusta:v:16:y:2024:i:7:p:2894-:d:1367360.

    Full description at Econpapers || Download paper

  5. A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems. (2024). Kiasari, Mahmoud ; Ghaffari, Mahdi ; Aly, Hamed H.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:16:p:4128-:d:1459360.

    Full description at Econpapers || Download paper

  6. A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects. (2024). Cheng, XU ; Liu, Xiufeng ; Wang, Renfang ; Zhang, Liang ; Shi, Kaikai ; Qiu, Hong.
    In: Renewable Energy.
    RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005640.

    Full description at Econpapers || Download paper

  7. A novel adaptively combined model based on induced ordered weighted averaging for wind power forecasting. (2024). Zhang, Ning ; Liu, Mingyang ; Ye, Lin ; Di, Jingyi ; Yang, Jianbin ; Wang, Cheng ; Gao, ZE ; Lu, Peng.
    In: Renewable Energy.
    RePEc:eee:renene:v:226:y:2024:i:c:s0960148124004154.

    Full description at Econpapers || Download paper

  8. Forecast-based stochastic optimization for a load powered by wave energy. (2024). Dillon, Trent ; Lawson, Michael ; Polagye, Brian ; Maurer, Benjamin.
    In: Renewable Energy.
    RePEc:eee:renene:v:226:y:2024:i:c:s0960148124003951.

    Full description at Econpapers || Download paper

  9. Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses. (2024). Sun, Chuanwang ; Zhao, Zhengtang ; Li, Qianwen ; Xu, Mengjie.
    In: Energy.
    RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028421.

    Full description at Econpapers || Download paper

  10. Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models. (2024). Akgun, Omer Burak ; Sen, Mustafa ; Gulay, Emrah.
    In: Energy.
    RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029602.

    Full description at Econpapers || Download paper

  11. From consumer to prosumer: A model-based analysis of costs and benefits of grid-connected residential PV-battery systems. (2024). Benalcazar, Pablo ; Kalka, Maciej ; Kamiski, Jacek.
    In: Energy Policy.
    RePEc:eee:enepol:v:191:y:2024:i:c:s0301421524001873.

    Full description at Econpapers || Download paper

  12. Probabilistic solar power forecasting: An economic and technical evaluation of an optimal market bidding strategy. (2024). van Sark, W. G. H. J. M., ; Alskaif, T A ; Visser, L R ; Kleissl, J ; Khurram, A.
    In: Applied Energy.
    RePEc:eee:appene:v:370:y:2024:i:c:s0306261924009565.

    Full description at Econpapers || Download paper

  13. Scenario-based ultra-short-term rolling optimal operation of a photovoltaic-energy storage system under forecast uncertainty. (2024). Pang, Xiulan ; Xu, Ximeng ; Li, Xiaofeng ; Zhang, Pengfei ; Liu, LU ; Ma, Chao.
    In: Applied Energy.
    RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017890.

    Full description at Econpapers || Download paper

  14. Wind Power Forecasting Based on WaveNet and Multitask Learning. (2023). Cao, Xinwei ; Li, Shuai ; Wang, Hao ; Peng, Chen ; Liao, Bolin.
    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2023:i:14:p:10816-:d:1190709.

    Full description at Econpapers || Download paper

  15. A Review of Energy Management Systems and Organizational Structures of Prosumers. (2023). Miljenovi, Nemanja ; Nidarec, Matej ; Kneevi, Goran ; Ljivac, Damir ; Sumper, Andreas.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:7:p:3179-:d:1113489.

    Full description at Econpapers || Download paper

  16. A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence. (2023). Cyrino, Fernando Luiz ; Marques, Andre Luis ; Iung, Anderson Mitterhofer.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:3:p:1013-:d:1038118.

    Full description at Econpapers || Download paper

  17. Consumption–Production Profile Categorization in Energy Communities. (2023). Rozas, Wolfram ; Pastor-Vargas, Rafael ; Carpio, Jose ; Garcia-Vico, Angel Miguel.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:19:p:6996-:d:1255507.

    Full description at Econpapers || Download paper

  18. An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks. (2023). Radhoush, Sepideh ; Nehrir, Hashem ; Whitaker, Bradley M.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:16:p:5972-:d:1216849.

    Full description at Econpapers || Download paper

  19. Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead. (2023). Leonowicz, Zbigniew ; Shahzad, Sulman ; Akhtar, Saima ; Gono, Radomir ; Jasiski, Micha ; Kilic, Heybet ; Ullah, Hafiz Sami ; Zaheer, Asad.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:10:p:4060-:d:1145829.

    Full description at Econpapers || Download paper

  20. Comparison of statistical and optimization models for projecting future PV installations at a sub-national scale. (2023). Wen, Xin ; Heinisch, Verena ; Muller, Jonas ; Sasse, Jan-Philipp ; Trutnevyte, Evelina.
    In: Energy.
    RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027809.

    Full description at Econpapers || Download paper

  21. Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification. (2023). Du, Ruoyun ; Yu, Min ; Sun, Lijie ; Wang, Keke ; Niu, Dongxiao.
    In: Energy.
    RePEc:eee:energy:v:275:y:2023:i:c:s0360544223007429.

    Full description at Econpapers || Download paper

  22. Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network. (2023). Opur, Onur ; Nakip, Mert ; Biyik, Emrah ; Guzeli, Cuneyt.
    In: Applied Energy.
    RePEc:eee:appene:v:340:y:2023:i:c:s0306261923003781.

    Full description at Econpapers || Download paper

  23. Short-term electricity load forecasting—A systematic approach from system level to secondary substations. (2023). Madeira, Sara C ; Pinheiro, Marco G ; Francisco, Alexandre P.
    In: Applied Energy.
    RePEc:eee:appene:v:332:y:2023:i:c:s0306261922017500.

    Full description at Econpapers || Download paper

  24. A Hybrid Algorithm for Short-Term Wind Power Prediction. (2022). Xiong, Zhenhua ; Zhuo, Yixin ; Chen, Yan ; Huang, Kui ; Ban, Guihua.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:19:p:7314-:d:933880.

    Full description at Econpapers || Download paper

  25. Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization. (2022). Zhao, Ning ; You, Fengqi.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:161:y:2022:i:c:s1364032122003343.

    Full description at Econpapers || Download paper

  26. A review of behind-the-meter solar forecasting. (2022). Doubleday, Kate ; Feng, Cong ; Erdener, Burcin Cakir ; Hodge, Bri-Mathias ; Florita, Anthony.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:160:y:2022:i:c:s1364032122001472.

    Full description at Econpapers || Download paper

  27. An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints. (2022). Schuhler, Thierry ; Polleux, Louis ; Sandoval-Moreno, John ; Marmorat, Jean-Paul ; Guerassimoff, Gilles.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:155:y:2022:i:c:s136403212101220x.

    Full description at Econpapers || Download paper

  28. Evaluation of opaque deep-learning solar power forecast models towards power-grid applications. (2022). Wei, Zhinong ; Cheng, Lilin ; Zang, Haixiang ; Sun, Guoqiang ; Zhang, Fengchun.
    In: Renewable Energy.
    RePEc:eee:renene:v:198:y:2022:i:c:p:960-972.

    Full description at Econpapers || Download paper

  29. Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control. (2022). Du, Yang ; Chen, Xiaoyang ; Fang, Lurui ; Yan, KE ; Lim, Eng Gee.
    In: Renewable Energy.
    RePEc:eee:renene:v:195:y:2022:i:c:p:147-166.

    Full description at Econpapers || Download paper

  30. Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction. (2022). Ogunkunle, Oyetola ; Adeleke, Oluwatobi ; Ahmed, Noor A ; Olatunji, Kehinde O ; Madyira, Daniel M ; Adebayo, Ademola O.
    In: Renewable Energy.
    RePEc:eee:renene:v:189:y:2022:i:c:p:288-303.

    Full description at Econpapers || Download paper

  31. Examining wind energy deployment pathways in complex macro-economic and political settings using a fuzzy cognitive map-based method. (2022). Zare, Sara Ghaboulian ; Stewart, Rodney A ; Alipour, Mohammad ; Hafezi, Mehdi ; Rahman, Anisur.
    In: Energy.
    RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019216.

    Full description at Econpapers || Download paper

  32. Optimization-driven uncertainty forecasting: Application to day-ahead commitment with renewable energy resources. (2022). Kwon, Soongeol ; Karimi, Sajad.
    In: Applied Energy.
    RePEc:eee:appene:v:326:y:2022:i:c:s0306261922011862.

    Full description at Econpapers || Download paper

  33. Sizing ramping reserve using probabilistic solar forecasts: A data-driven method. (2022). Zhang, Jie ; Spyrou, Evangelia ; Feng, Cong ; Siebenschuh, Carlo ; Li, Binghui ; Hobbs, Benjamin F ; Krishnan, Venkat.
    In: Applied Energy.
    RePEc:eee:appene:v:313:y:2022:i:c:s0306261922002574.

    Full description at Econpapers || Download paper

  34. Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix. (2021). Alhumaid, Yousef ; Khalid, Muhammad ; Khan, Khalid ; Alismail, Fahad.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:11:p:5878-:d:560792.

    Full description at Econpapers || Download paper

  35. Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques. (2021). Poncela, Pilar ; Poncela-Blanco, Marta.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:5:p:1446-:d:512064.

    Full description at Econpapers || Download paper

  36. Prediction of Solar Power Using Near-Real Time Satellite Data. (2021). Kay, Merlinde ; Prasad, Abhnil Amtesh.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:18:p:5865-:d:636858.

    Full description at Econpapers || Download paper

  37. A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting. (2021). Thil, Stephane ; Grieu, Stephane ; Gbemou, Shab ; Eynard, Julien ; Guillot, Emmanuel.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:11:p:3192-:d:565352.

    Full description at Econpapers || Download paper

  38. Applications for solar irradiance nowcasting in the control of microgrids: A review. (2021). Blum, Niklas ; Moghbel, Moayed ; Shoeb, Md Asaduzzaman ; Calais, Martina ; Shafiullah, G M ; Nouri, Bijan ; Samu, Remember.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:147:y:2021:i:c:s1364032121004755.

    Full description at Econpapers || Download paper

  39. Optimal energy bidding for renewable plants: A practical application to an actual wind farm in Spain. (2021). Payan, Manuel Burgos ; Endemao-Ventura, Lazaro ; Roldan, Juan Manuel ; Riquelme, Jesus Manuel ; Gonzalez, Javier Serrano.
    In: Renewable Energy.
    RePEc:eee:renene:v:175:y:2021:i:c:p:1111-1126.

    Full description at Econpapers || Download paper

  40. Ultra-short-term combined prediction approach based on kernel function switch mechanism. (2021). Zhao, Yongning ; Qu, Ying ; Zhong, Wuzhi ; Lu, Peng ; Ye, Lin ; Zhai, Bingxu ; Tang, Yong.
    In: Renewable Energy.
    RePEc:eee:renene:v:164:y:2021:i:c:p:842-866.

    Full description at Econpapers || Download paper

  41. Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network. (2021). Tian, Xuan ; Ma, Wentao ; Fang, Shuai ; Wang, Peng ; Duan, Jiandong ; Liu, Haofan ; Cheng, Yulin ; Chang, Ying.
    In: Energy.
    RePEc:eee:energy:v:214:y:2021:i:c:s0360544220320879.

    Full description at Econpapers || Download paper

  42. A review of wind speed and wind power forecasting with deep neural networks. (2021). Wang, Yun ; Zou, Runmin ; Liu, Qianyi ; Zhang, Lingjun.
    In: Applied Energy.
    RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011053.

    Full description at Econpapers || Download paper

  43. A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application. (2020). Bueso, Maria C ; Paredes-Parra, Jose Miguel ; Mateo-Aroca, Antonio ; Molina-Garcia, Angel.
    In: Sustainability.
    RePEc:gam:jsusta:v:12:y:2020:i:6:p:2454-:d:334985.

    Full description at Econpapers || Download paper

  44. Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications. (2020). Dong, Z Y ; Begum, R A ; Ker, Pin Jern ; Faisal, M ; Hannan, M A ; Zhang, C.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:131:y:2020:i:c:s1364032120303130.

    Full description at Econpapers || Download paper

  45. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. (2020). Mishra, Y ; Sreeram, V ; Ahmed, R ; Arif, M D.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:124:y:2020:i:c:s1364032120300885.

    Full description at Econpapers || Download paper

  46. Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting. (2020). Sewdien, V N ; Preece, R ; van der Meijden, M ; Rueda, J L ; Rakhshani, E.
    In: Renewable Energy.
    RePEc:eee:renene:v:161:y:2020:i:c:p:878-892.

    Full description at Econpapers || Download paper

  47. Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system. (2020). Rasku, Topi ; Kiviluoma, Juha ; Miettinen, Jari ; Rinne, Erkka.
    In: Energy.
    RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323631.

    Full description at Econpapers || Download paper

  48. Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study. (2019). Hong, Sugwon ; Lee, Seung-Jae ; Aslam, Muhammad ; Kim, Hyung-Seung.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2019:i:1:p:147-:d:302641.

    Full description at Econpapers || Download paper

  49. Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data. (2019). Sigauke, Caston ; Mulaudzi, Sophie ; Mpfumali, Phathutshedzo ; Bere, Alphonce.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:18:p:3569-:d:268391.

    Full description at Econpapers || Download paper

  50. Wind power forecast based on improved Long Short Term Memory network. (2019). Gao, Zhiyu ; Zhang, Rongchang ; Jing, Huitian.
    In: Energy.
    RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319954.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-09-23 06:10:03 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.