1. Deep generative models in energy system applications: Review, challenges, and future directions. (2025). King, Ryan N ; Emami, Patrick ; Zhang, Xiangyu ; Cortiella, Alexandre ; Glaws, Andrew.
   In: Applied Energy.
   RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024437.

   Full description at Econpapers || Download paper

2. A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models. (2023). Georgilakis, Pavlos S ; Kousounadis-Knousen, Markos A ; Bazionis, Ioannis K ; Catthoor, Francky.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:15:p:5600-:d:1202094.

   Full description at Econpapers || Download paper

3. A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems. (2022). Alabi, Tobi Michael ; Lu, Lin ; Agbajor, Favour D ; Aghimien, Emmanuel I ; Gopaluni, Bhushan ; Adeoye, Adebusola R ; Yang, Zaiyue.
   In: Renewable Energy.
   RePEc:eee:renene:v:194:y:2022:i:c:p:822-849.

   Full description at Econpapers || Download paper

1. Abdel-Nasser M.; Mahmoud K. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl. 2019, 31, 2727-2740.
   Paper not yet in RePEc: Add citation now
   
2. Alahakoon D.; Yu X. Advanced analytics for harnessing the power of smart meter big data. Proceedings of the 2013 IEEE International Workshop on Inteligent Energy Systems (IWIES), Vienna, Austria, 14 November 2013, ; pp. 40-45.
   Paper not yet in RePEc: Add citation now
   
3. Almahairi A.; Rajeswar S.; Sordoni A.; Bachman P.; Courville A. Augmented CycleGAN: Learning many-to-many mappings from unpaired data. arXiv 2018.
   Paper not yet in RePEc: Add citation now
   
4. Banaee H.; Loutfi A. Data–driven rule mining and representation of temporal patterns in physiological sensor data. IEEE J. Biomed. Health Inform. 2015, 19, 1557-1566.
   Paper not yet in RePEc: Add citation now
   

5. Bedi J.; Toshniwal D. Deep learning framework to forecast electricity demand. Appl. Energy 2019, 238, 1312-1326.

6. Bertsimas D.; Gupta V.; Kallus N. Data-driven robust optimization. Math. Program. 2018, 167, 235-292.
   Paper not yet in RePEc: Add citation now
   

7. Cai M.; Pipattanasomporn M.; Rahman S. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. Appl. Energy 2019, 236, 1078-1088.

8. Chen H.; Chiang R.H.L.; Storey V.C. Business intelligence and analytics: From big data to big impact. MIS Q. 2012, 36, 1165.
   Paper not yet in RePEc: Add citation now
   
9. Choi Y.; Choi M.; Kim M.; Ha J.W.; Kim S.; Choo J. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018, ; pp. 8789-8797.
   Paper not yet in RePEc: Add citation now
   
10. Elamvazuthi I.; Ahamed Khan M.K.A.; Bin Shaari S.B.; Sinnadurai R.; Amudha M. Electrical power consumption monitoring using a real-time system. Proceedings of the 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT), Kuala Lumpur, Malaysia, 6–9 October 2012, ; pp. 295-298.
    Paper not yet in RePEc: Add citation now
    
11. He X.; Ai Q.; Qiu R.C.; Huang W.; Piao L.; Liu H. A big data architecture design for smart grids based on random matrix theory. IEEE Trans. Smart Grid 2015, 8, 674-686.
    Paper not yet in RePEc: Add citation now
    
12. Lee C.H.; Wu C.H. A novel big data modeling method for improving driving range estimation of EVs. IEEE Access 2015, 3, 1980-1993.
    Paper not yet in RePEc: Add citation now
    
13. Leo Kumar S.P. State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Eng. Appl. Artif. Intell. 2017, 65, 294-329.
    Paper not yet in RePEc: Add citation now
    
14. Radford A.; Metz L.; Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015.
    Paper not yet in RePEc: Add citation now
    
15. Ramachandran S.; Wang M. Near-real-time ocean color data processing using ancillary data from the global forecast system model. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1485-1495.
    Paper not yet in RePEc: Add citation now
    
16. Sridhar S.; Hahn A.; Govindarasu M. Cyber-physical system security for the electric power grid. Proc. IEEE 2012, 100, 210-224.
    Paper not yet in RePEc: Add citation now
    
17. Sun H.; Guo Q.; Zhang B.; Wu W.; Wang B.; Shen X.; Wang J. Integrated energy management system: Concept, design, and demonstration in China. IEEE Electr. Mag. 2018, 6, 42-50.
    Paper not yet in RePEc: Add citation now
    
18. Sun Q.; Wang D.; Ma D.; Huang B. Multi-objective energy management for we-energy in Energy Internet using reinforcement learning. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 27 November–1 December 2017, ; pp. 1-6.
    Paper not yet in RePEc: Add citation now
    
19. Van Hasselt H.; Guez A.; Silver D. Deep reinforcement learning with double Q-learning. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–17 February 2016, . Renewable Power Plants.
    Paper not yet in RePEc: Add citation now
    
20. Wang D.; Liu L.; Jia H.; Wang W.; Zhi Y.; Meng Z.; Zhou B. Review of key problems related to integrated energy distribution systems. CSEE J. Power Energy Syst. 2018, 4, 130-145.
    Paper not yet in RePEc: Add citation now
    

21. Wang H.Z.; Wang G.B.; Li G.Q.; Peng J.C.; Liu Y.T. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl. Energy 2016, 182, 80-93.

22. Wu X.; Zhu X.; Wu G.Q.; Ding W. Data mining with big data. IEEE Trans. Knowl. Data Eng. 2014, 26, 97-107.
    Paper not yet in RePEc: Add citation now
    

23. Xiong R.; Cao J.; Yu Q. Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl. Energy 2018, 211, 538-548.

24. Zhao J.; Mathieu M.; LeCun Y. Energy-based generative adversarial network. arXiv 2016.
    Paper not yet in RePEc: Add citation now
    
25. Zhou S.; Hu Z.; Gu W. Artificial intelligence based smart energy community management: A reinforcement learning approach. CSEE J. Power Energy Syst. 2019, 5, 1-10.
    Paper not yet in RePEc: Add citation now
    

1. Multi-objective optimization of crude oil-supply portfolio based on interval prediction data. (2022). Li, jianping ; Sun, Xiaolei ; Hao, Jun.
   In: Annals of Operations Research.
   RePEc:spr:annopr:v:309:y:2022:i:2:d:10.1007_s10479-020-03701-w.

   Full description at Econpapers || Download paper

2. Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study. (2022). Ciechulski, Tomasz ; Osowski, Stanislaw ; Szmurlo, Robert ; Siwek, Krzysztof.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:9:p:3265-:d:805761.

   Full description at Econpapers || Download paper

3. Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting. (2022). Zhao, Jinlou ; Zhang, Jindai.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:10:p:3553-:d:814143.

   Full description at Econpapers || Download paper

4. A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks. (2022). Jasiński, Tomasz ; Jasiski, Tomasz.
   In: Renewable and Sustainable Energy Reviews.
   RePEc:eee:rensus:v:159:y:2022:i:c:s136403212200140x.

   Full description at Econpapers || Download paper

5. A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China. (2022). Zhang, Zhiwei ; Zhou, Wenhao ; Li, Hailin.
   In: Mathematics and Computers in Simulation (MATCOM).
   RePEc:eee:matcom:v:200:y:2022:i:c:p:128-147.

   Full description at Econpapers || Download paper

6. Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN). (2022). Tan, Mustafa ; Emeksiz, Cem.
   In: Energy.
   RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006880.

   Full description at Econpapers || Download paper

7. Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network. (2022). Liu, Yezheng ; Lu, Shixiang ; Xu, Qifa ; Jiang, Cuixia ; Kusiak, Andrew.
   In: Energy.
   RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032047.

   Full description at Econpapers || Download paper

8. A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. (2022). Natarajan, Sukumar ; Chaturvedi, Shobhit ; McCullen, Nick ; Rajasekar, Elangovan.
   In: Energy Policy.
   RePEc:eee:enepol:v:168:y:2022:i:c:s0301421522003226.

   Full description at Econpapers || Download paper

9. A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies. (2022). Onel, Onur ; Matthews, Logan R ; Pistikopoulos, Efstratios N ; Hallermann, Detlef R ; Floudas, Christodoulos A ; Niziolek, Alexander M ; Baratsas, Stefanos G ; Sorescu, Sorin M.
   In: Applied Energy.
   RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011260.

   Full description at Econpapers || Download paper

10. Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting. (2021). Kusaf, Mehmet ; Salman, Diaa.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:24:p:13609-:d:698734.

    Full description at Econpapers || Download paper

11. A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV. (2021). Steens, Thomas ; Grottke, Matthias ; von Maydell, Karsten ; Telle, Jan-Simon ; di Modica, Gian-Luca ; Agert, Carsten ; Engel, Bernd ; Hanke, Benedikt.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:12:p:3576-:d:575802.

    Full description at Econpapers || Download paper

12. A deep learning framework for building energy consumption forecast. (2021). Somu, Nivethitha ; Ramamritham, Krithi ; Raman, Gauthama.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:137:y:2021:i:c:s1364032120308753.

    Full description at Econpapers || Download paper

13. Can electricity demand lead to air pollution? A spatio-temporal analysis of electricity demand with climatic conditions. (2021). Bedi, Jatin ; Toshniwal, Durga.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:136:y:2021:i:c:s1364032120307000.

    Full description at Econpapers || Download paper

14. Improved efficiency maximization strategy for vehicular dual-stack fuel cell system considering load state of sub-stacks through predictive soft-loading. (2021). Liu, Zhixiang ; Zeng, Tao ; Chan, Siew Hwa ; Wu, QI ; Deng, Chenghao ; Zhang, Caizhi.
    In: Renewable Energy.
    RePEc:eee:renene:v:179:y:2021:i:c:p:929-944.

    Full description at Econpapers || Download paper

15. Energy consumption prediction model with deep inception residual network inspiration and LSTM. (2021). Salam, Abdulwahed ; el Hibaoui, Abdelaaziz.
    In: Mathematics and Computers in Simulation (MATCOM).
    RePEc:eee:matcom:v:190:y:2021:i:c:p:97-109.

    Full description at Econpapers || Download paper

16. Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization. (2021). Ji, Zhengsen ; Xu, Xiaomin ; Li, Wanying ; Niu, Dongxiao ; Liu, DA.
    In: Energy.
    RePEc:eee:energy:v:234:y:2021:i:c:s0360544221013931.

    Full description at Econpapers || Download paper

17. Real-time power prediction approach for turbine using deep learning techniques. (2021). Xie, Yonghui ; Xia, Xinlei ; Liu, Tianyuan ; Sun, Lei ; Zhang, DI.
    In: Energy.
    RePEc:eee:energy:v:233:y:2021:i:c:s0360544221013785.

    Full description at Econpapers || Download paper

18. Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network. (2021). Yuan, Shan ; Cheng, Ming ; Cao, Junxing ; Wang, Jun.
    In: Energy.
    RePEc:eee:energy:v:233:y:2021:i:c:s036054422101330x.

    Full description at Econpapers || Download paper

19. Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory. (2021). Lin, ZI ; Liu, Xiaolei.
    In: Energy.
    RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007040.

    Full description at Econpapers || Download paper

20. Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach. (2021). Zhao, Yu-Feng ; Pei, Ling-Ling ; Chen, Hai-Bao .
    In: Energy.
    RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002012.

    Full description at Econpapers || Download paper

21. A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19. (2021). Ma, Xin ; Lu, Hongfang.
    In: Energy.
    RePEc:eee:energy:v:219:y:2021:i:c:s036054422032675x.

    Full description at Econpapers || Download paper

22. Analysis and forecast of Chinas energy consumption structure. (2021). Su, Bin ; Luo, Song ; Zeng, Sheng ; Tao, Qingmei ; Zhang, Minglong ; Liu, Jun ; Gao, Yuan.
    In: Energy Policy.
    RePEc:eee:enepol:v:159:y:2021:i:c:s030142152100495x.

    Full description at Econpapers || Download paper

23. A hybrid deep learning-based online energy management scheme for industrial microgrid. (2021). Lu, Renzhi ; Wei, Min ; Sun, Mingyang ; Jiang, Junhui ; Zhang, Hai-Tao ; Xiao, Feng ; Ding, Yuemin ; Bai, Ruichang.
    In: Applied Energy.
    RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011806.

    Full description at Econpapers || Download paper

24. Artificial Intelligence and emerging digital technologies in the energy sector. (2021). Lyu, Wenjing ; Liu, Jin.
    In: Applied Energy.
    RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009843.

    Full description at Econpapers || Download paper

25. Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods. (2021). Chen, Yan ; Ahin, Utkucan ; Balli, Serkan.
    In: Applied Energy.
    RePEc:eee:appene:v:302:y:2021:i:c:s0306261921009181.

    Full description at Econpapers || Download paper

26. Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression. (2021). Chen, Ying ; Goude, Yannig ; Xu, Xiuqin ; Yao, Qiwei.
    In: Applied Energy.
    RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008539.

    Full description at Econpapers || Download paper

27. Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting. (2021). Kumari, Pratima ; Toshniwal, Durga.
    In: Applied Energy.
    RePEc:eee:appene:v:295:y:2021:i:c:s0306261921005158.

    Full description at Econpapers || Download paper

28. A framework for predicting the production performance of unconventional resources using deep learning. (2021). Rui, Zhenhua ; Wang, Sen ; Feng, Qihong ; Javadpour, Farzam ; Qin, Chaoxu.
    In: Applied Energy.
    RePEc:eee:appene:v:295:y:2021:i:c:s0306261921004827.

    Full description at Econpapers || Download paper

29. An improved self-organizing incremental neural network model for short-term time-series load prediction. (2021). Wong, Yee Wan ; Rajkumar, Rajprasad Kumar ; Chong, Lee Wai ; Begam, Kasim Mumtaj.
    In: Applied Energy.
    RePEc:eee:appene:v:292:y:2021:i:c:s0306261921003949.

    Full description at Econpapers || Download paper

30. Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence. (2021). Sulbaran, Tulio ; Chaudhuri, Saptarshi ; Langar, Sandeep ; Baaaolu, Hakan ; Chakraborty, Debaditya ; Alam, Arafat.
    In: Applied Energy.
    RePEc:eee:appene:v:291:y:2021:i:c:s0306261921003093.

    Full description at Econpapers || Download paper

31. Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model. (2021). Safder, Usman ; Ifaei, Pouya ; Lim, Juin Yau ; Yoo, Changkyoo ; How, Bing Shen.
    In: Applied Energy.
    RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316883.

    Full description at Econpapers || Download paper

32. Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network. (2021). Shi, YU ; Song, Xianzhi.
    In: Applied Energy.
    RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920314811.

    Full description at Econpapers || Download paper

33. A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity. (2020). Son, Hyojoo ; Kim, Changwan.
    In: Sustainability.
    RePEc:gam:jsusta:v:12:y:2020:i:8:p:3103-:d:344794.

    Full description at Econpapers || Download paper

34. Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting. (2020). Mariani, Viviana Cocco ; Fraccanabbia, Naylene ; Santos, Leandro Dos ; Sauer, Joo Guilherme ; Ribeiro, Gabriel Trierweiler.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:9:p:2390-:d:356418.

    Full description at Econpapers || Download paper

35. A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm. (2020). Sun, Xiaolei ; Feng, Qianqian ; Hao, Jun.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:3:p:550-:d:312344.

    Full description at Econpapers || Download paper

36. Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting. (2020). Shang, Zhihao ; Yang, YI ; Chen, Yao.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:3:p:532-:d:311787.

    Full description at Econpapers || Download paper

37. Deep Learning Approach to Power Demand Forecasting in Polish Power System. (2020). Ciechulski, Tomasz ; Osowski, Stanisaw.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:22:p:6154-:d:449826.

    Full description at Econpapers || Download paper

38. Multistep-ahead forecasting of coal prices using a hybrid deep learning model. (2020). Jianhua, Zhang ; Alameer, Zakaria ; Li, Kenli ; Ye, Haiwang ; Fathalla, Ahmed.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:65:y:2020:i:c:s0301420719305240.

    Full description at Econpapers || Download paper

39. Big data driven predictive production planning for energy-intensive manufacturing industries. (2020). Ge, Yuntian ; Zhang, Yingfeng ; Ma, Shuaiyin ; Li, Lin ; Lv, Jingxiang ; Yang, Haidong.
    In: Energy.
    RePEc:eee:energy:v:211:y:2020:i:c:s0360544220314274.

    Full description at Econpapers || Download paper

40. A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. (2020). Amraee, Turaj ; Kazemzadeh, Mohammad-Rasool ; Amjadian, Ali.
    In: Energy.
    RePEc:eee:energy:v:204:y:2020:i:c:s0360544220310550.

    Full description at Econpapers || Download paper

41. Improving solar forecasting using Deep Learning and Portfolio Theory integration. (2020). , Paulo ; Anderson, Marcello ; Fernandez-Ramirez, Luis M.
    In: Energy.
    RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301237.

    Full description at Econpapers || Download paper

42. Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. (2020). Garg, Vishal ; Rahman, Saifur ; Pipattanasomporn, Manisa ; Chitalia, Gopal.
    In: Applied Energy.
    RePEc:eee:appene:v:278:y:2020:i:c:s0306261920309223.

    Full description at Econpapers || Download paper

43. Machine learning driven smart electric power systems: Current trends and new perspectives. (2020). Dong, Wei ; Yang, Qiang ; Ibrahim, Muhammad Sohail.
    In: Applied Energy.
    RePEc:eee:appene:v:272:y:2020:i:c:s0306261920307492.

    Full description at Econpapers || Download paper

44. Investigating the economics of the power sector under high penetration of variable renewable energies. (2020). Nagatomi, YU ; Matsuo, Yuhji ; Shibata, Yoshiaki ; Fujii, Yasumasa ; Endo, Seiya ; Komiyama, Ryoichi.
    In: Applied Energy.
    RePEc:eee:appene:v:267:y:2020:i:c:s0306261919316435.

    Full description at Econpapers || Download paper

45. A novel improved model for building energy consumption prediction based on model integration. (2020). Feng, Wei ; Lu, Shilei ; Wang, Ran.
    In: Applied Energy.
    RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300738.

    Full description at Econpapers || Download paper

46. Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. (2020). Assimakopoulos, Vassilios ; Kourentzes, Nikolaos ; Petropoulos, Fotios ; Spiliotis, Evangelos.
    In: Applied Energy.
    RePEc:eee:appene:v:261:y:2020:i:c:s0306261919320264.

    Full description at Econpapers || Download paper

47. An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network. (2019). Hu, Zijian ; Zhong, Zhi ; Zhou, Suyang ; Jiang, Meng ; He, DI.
    In: Sustainability.
    RePEc:gam:jsusta:v:11:y:2019:i:23:p:6699-:d:291239.

    Full description at Econpapers || Download paper

48. Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection. (2019). Leigh, Seung-Bok ; Park, Kyungyong ; Lee, Joo Sang ; Jang, Jihoon ; Kim, Gahee ; Son, Eunjo.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:21:p:4187-:d:282965.

    Full description at Econpapers || Download paper

49. Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks. (2019). Hwangbo, Soonho ; Li, Qian ; Nam, Kijeon ; Rashidi, Jouan ; Loy-Benitez, Jorge ; Yoo, Changkyoo.
    In: Energy.
    RePEc:eee:energy:v:178:y:2019:i:c:p:277-292.

    Full description at Econpapers || Download paper

50. Deep learning for multi-scale smart energy forecasting. (2019). Ahmad, Tanveer ; Chen, Huanxin.
    In: Energy.
    RePEc:eee:energy:v:175:y:2019:i:c:p:98-112.

    Full description at Econpapers || Download paper

Report date: 2025-10-05 06:16:51 || Missing content? Let us know