1. Study on hierarchical model of hydroelectric unit commitment based on similarity schedule and quadratic optimization approach. (2024). Yang, Yuqi ; Shen, Keyan ; Huang, Jingwei ; Qin, Hui ; Jia, Benjun.
   In: Energy.
   RePEc:eee:energy:v:305:y:2024:i:c:s0360544224020036.

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2. Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources. (2022). Krechowicz, Maria ; Poczeta, Katarzyna.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:23:p:9146-:d:991550.

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1. Abbas, Z.; Al-Shishtawy, A.; Girdzijauskas, S.; Vlassov, V. Short-term traffic prediction using long short-term memory neural networks. In Proceedings of the 2018 IEEE International Congress on Big Data (BigData Congress), Seattle, WA, USA, 10–13 December 2018; Volume 1, pp. 57–65.
   Paper not yet in RePEc: Add citation now
   
2. Abd Al-Azeem Hussieny, O.; El-Beltagy, M.A.; El-Tantawy, S. Forecasting of renewable energy using ANN, GPANN and ANFIS (A comparative study and performance analysis). In Proceedings of the 2nd Novel Intelligent Leading Emerging Sciences Conference (NILES), Giza, Egypt, 24–26 October 2020; pp. 54–59.
   Paper not yet in RePEc: Add citation now
   

3. Abdi, H. Profit-based unit commitment problem: A review of models, methods, challenges, and future directions total revenue. Renew. Sustain. Energy Rev. 2021, 138, 110504. [CrossRef]

4. Abdou, I.; Tkiouat, M. Unit commitment problem in electrical power system: A literature review. Int. J. Electr. Comput. Eng. 2018, 8, 1357–1372. [CrossRef]
   Paper not yet in RePEc: Add citation now
   
5. Alqunun, K. Optimal Unit commitment problem considering stochastic wind energy penetration. Eng. Technol. Appl. Sci. Res. 2020, 10, 6316–6322. [CrossRef]
   Paper not yet in RePEc: Add citation now
   
6. Alvarez, G.E.; Marcovecchio, M.G.; Aguirre, P. Security constrained unit commitment scheduling: A new MILP formulation for solving transmission constraints. Comput. Chem. Eng. 2018, 115, 455–473. [CrossRef]
   Paper not yet in RePEc: Add citation now
   
7. Ananthan, D. Unit commitment solution using particle swarm optimisation (PSO). IOSR J. Eng. 2014, 4, 48–55. [CrossRef]
   Paper not yet in RePEc: Add citation now
   
8. Arora, V.; Chanana, S. Solution to unit commitment problem using Lagrangian relaxation and Mendel’s GA method. In Proceedings of the 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES), Sultanpur, India, 11–12 March 2016; Volume 2016, pp. 126–129.
   Paper not yet in RePEc: Add citation now
   

9. Bedi, J.; Toshniwal, D. Deep learning framework to forecast electricity demand. Appl. Energy 2019, 238, 1312–1326. [CrossRef]

10. Blanchard, T.; Samanta, B. Wind speed forecasting using neural networks. Wind Eng. 2020, 44, 33–48. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
11. Boqtob, O.; El Moussaoui, H.; El Markhi, H.; Lamhamdi, T. Optimal robust unit commitment of microgrid using hybrid particle swarm optimization with sine cosine acceleration coefficients. Int. J. Renew. Energy Res. 2019, 9, 1125–1134.
    Paper not yet in RePEc: Add citation now
    
12. Ding, Y. Wind Time Series Dataset; CRC Press: Boca Raton, FL, USA, 2021. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
13. Dong, D.; Sheng, Z.; Yang, T. Wind power prediction based on recurrent neural network with long short-term memory units. In Proceedings of the 2018 International Conference on Renewable Energy and Power Engineering (REPE), Toronto, ON, Canada, 24–26 November 2018; Volume 2019, pp. 34–38.
    Paper not yet in RePEc: Add citation now
    

14. Furukakoi, M.; Adewuyi, O.B.; Matayoshi, H.; Howlader, A.M.; Senjyu, T. Multi objective unit commitment with voltage stability and PV uncertainty. Appl. Energy. 2018, 228, 618–623. [CrossRef]

15. Gaddam, R.R. Optimal Unit Commitment Using Swarm Intelligence for Secure Operation of Solar Energy Integrated Smart Grid; Power Systems Research Center; International Institute of Information Technology: Hyderabad, India, 2013.
    Paper not yet in RePEc: Add citation now
    
16. Hu, G.; Yang, L. The parallel interior point for solving the continuous optimization problem of unit commitment. In Proceedings of the 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, China, 15–17 October 2016; pp. 1333–1338.
    Paper not yet in RePEc: Add citation now
    

17. Huang, W.T.; Yao, K.C.; Chen, M.K.; Wang, F.Y.; Zhu, C.H.; Chang, Y.R.; Lee, Y.D.; Ho, Y.H. Derivation and application of a new transmission loss formula for power system economic dispatch. Energies 2018, 11, 417. [CrossRef]

18. Kamboj, V.K. A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput. Appl. 2016, 27, 1643–1655. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
19. Kamh, M.; Abdelaziz, A.Y.; Mekhamer, S.F.; Badr, M.A. Modified augmented hopfield neural network for optimal thermal unit commitment. In Proceedings of the 2009 IEEE Power & Energy Society General Meeting, Calgary, AB, Canada, 26–30 July 2009; pp. 1–8.
    Paper not yet in RePEc: Add citation now
    
20. Kokare, M.B.; Tade, S.V. Application of Artificial Bee Colony Method for Unit Commitment. In Proceedings of the 2018 4th International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 16–18 August 2018; pp. 1–6.
    Paper not yet in RePEc: Add citation now
    
21. Li, L.L.; Zhao, X.; Tseng, M.L.; Tan, R.R. Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. J. Clean. Prod. 2020, 242, 118447. [CrossRef] Sustainability 2021, 13, 13609 22 of 22
    Paper not yet in RePEc: Add citation now
    
22. Liu, C.; Jin, Z.; Gu, J.; Qiu, C. Short-term load forecasting using a long short-term memory network. In Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Torino, Italy, 26–29 September 2017; Volume 2018, pp. 1–6.
    Paper not yet in RePEc: Add citation now
    
23. Liu, M.; Quilumba, F.; Lee, W.-J. Dispatch scheduling for a wind farm with hybrid energy storage based on wind and LMP forecasting. IEEE Trans. Ind. Appl. 2015, 51, 1970–1977. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
24. Logenthiran, T.; Srinivasan, D. Particle swarm optimization for unit commitment problem. In Proceedings of the 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, Singapore, 14–17 June 2010; Volume 2010, pp. 642–647. Sustainability 2021, 13, 13609 21 of 22
    Paper not yet in RePEc: Add citation now
    
25. Ma, Z.; Zhong, H.; Xia, Q.; Kang, C.; Wang, Q.; Cao, X. A unit commitment algorithm with relaxation-based neighborhood search and improved relaxation inducement. IEEE Trans. Power Syst. 2020, 35, 3800–3809. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
26. Madraswala, H.S. Modified genetic algorithm solution to unit commitment problem. In Proceedings of the 2017 International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, 27–28 January 2017; pp. 1–6.
    Paper not yet in RePEc: Add citation now
    
27. Majidi, H.; Emadaleslami, M.; Haghifam, M.R. A new binary-coded approach to the unit commitment problem using grey wolf optimization. In Proceedings of the 2020 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, 4–6 August 2020; pp. 1–5.
    Paper not yet in RePEc: Add citation now
    

28. McLarty, D.; Panossian, N.; Jabbari, F.; Traverso, A. Dynamic economic dispatch using complementary quadratic pro-gramming. Energy 2019, 166, 755–764. [CrossRef]

29. Mehranpour, A.; Ramezani, M. Unit commitment in the presence of photovoltaic cells. In Proceedings of the 1st Conference on Applied Research in Electrical Engineering (AREE), Ahvaz, Iran, 27 January 2020; Volume 2020, pp. 1–9.
    Paper not yet in RePEc: Add citation now
    

30. Ming, B.; Liu, P.; Guo, S.; Cheng, L.; Zhou, Y.; Gao, S.; Li, H. Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China. Appl. Energy 2018, 228, 1341–1352. [CrossRef]

31. Mohy-Ud-Din, G.; Vu, D.H.; Muttaqi, K.M.; Sutanto, D. An integrated energy management approach for the economic operation of industrial microgrids under uncertainty of renewable energy. IEEE Trans. Ind. Appl. 2020, 56, 1062–1073. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
32. Ning, C.; You, F. Data-driven adaptive robust unit commitment under wind power uncertainty: A Bayesian nonparametric approach. IEEE Trans. Power Syst. 2019, 34, 2409–2418. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
33. Palis, D.; Palis, S. Efficient unit commitment—A modified branch-and-bound approach. In Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore, 22–25 November 2016; Volume 2017, pp. 267–271.
    Paper not yet in RePEc: Add citation now
    

34. Pang, Z.; Niu, F.; O’Neill, Z. Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons. Renew. Energy 2020, 156, 279–289. [CrossRef]

35. Rachunok, B.; Staid, A.; Watson, J.P.; Woodruff, D.L.; Yang, D. Stochastic unit commitment performance considering monte carlo wind power scenarios. In Proceedings of the 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Boise, ID, USA, 24–28 June 2018; pp. 1–6.
    Paper not yet in RePEc: Add citation now
    
36. Reddy, S.; Kumar, R.; Panigrahi, B.K. Binary bat search algorithm for unit commitment problem in power system. In Proceedings of the 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), Dehradun, Indi, 18–19 December 2018; pp. 121–124.
    Paper not yet in RePEc: Add citation now
    
37. Rouhi, F.; Effatnejad, R. Unit commitment in power system t by combination of dynamic programming (DP), genetic algorithm (GA) and particle swarm optimization (PSO). Indian J. Sci. Technol. 2015, 8, 134–141. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
38. Shabbir, N.; Kutt, L.; Jawad, M.; Amadiahanger, R.; Iqbal, M.N.; Rosin, A. Wind Energy Forecasting Using Recurrent Neural Networks. In Proceedings of the 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE), Sofia, Bulgaria, 21–22 November 2019; pp. 1–5.
    Paper not yet in RePEc: Add citation now
    
39. Singh, A.; Khamparia, A. A hybrid whale optimization-differential evolution and genetic algorithm based approach to solve unit commitment scheduling problem: WODEGA. Sustain. Comput. Inform. Syst. 2020, 28, 100442. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
40. Sivaneasan, B.; Yu, C.Y.; Goh, K.P. Solar forecasting using ANN with fuzzy logic pre-processing. Energy Procedia 2017, 143, 727–732. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
41. Thakur, N.; Titare, L.S. Determination of unit commitment problem using dynamic programming. Int. J. Nov. Res. Electr. Mech. Eng. 2016, 3, 24–28.
    Paper not yet in RePEc: Add citation now
    
42. Vargas, D.V.; Murata, J.; Takano, H. Tackling unit commitment and load dispatch problems considering all constraints with evolutionary computation. arXiv 2019, arXiv:1903.09304.
    Paper not yet in RePEc: Add citation now
    
43. Wang, C.; Li, X.; Wang, Z.; Dong, X.; Liang, Z.; Liu, X.; Liang, J.; Han, X. Day-ahead unit commitment method considering time sequence feature of wind power forecast error. Int. J. Electr. Power Energy Syst. 2018, 98, 156–166. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
44. Wang, H.; Lei, Z.; Zhang, X.; Zhou, B.; Peng, J. A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 2019, 198, 111799. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
45. Wang, J.; Shahidehpour, M.; Li, Z. Security-constrained unit commitment with volatile wind power generation. IEEE Trans. Power Syst. 2008, 23, 1319–1327. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
46. Wulandhari, L.A.; Komsiyah, S.; Wicaksono, W. Bat algorithm implementation on economic dispatch optimization problem. Procedia Comput. Sci. 2018, 135, 275–282. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
47. Yang, Y.; Wu, W.; Wang, B.; Li, M. Analytical solution for stochastic unit commitment considering wind power uncertainty with gaussian mixture model. IEEE Trans. Power Syst. 2019, 35, 2769–2782. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

48. Zhang, Y.; Liu, Y.; Shu, S.; Zheng, F.; Huang, Z. A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources. Energy 2021, 216, 119171. [CrossRef]

49. Zhu, Y.; Liu, X.; Deng, R.; Zhai, Y. Memetic algorithm for solving monthly unit commitment problem considering uncertain wind power. J. Control Autom. Electr. Syst. 2020, 31, 511–520. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

1. Security-constrained unit commitment: Modeling, solutions and evaluations. (2025). Alavi-Eshkaftaki, Amin ; Najafabadi, Fatemeh Rajaei ; Hashemian, Seyed Nasrollah ; Khaleghizadeh, Ali ; Nezamabadi, Hossein ; Ramandi, Mostafa Yousefi ; Mozdawar, Seyed Alireza ; Hatziargyriou, Nikos D ; Dolatabadi, Sarineh Hacopian ; Latify, Mohammad Amin ; Mokhtari, Ali.
   In: Applied Energy.
   RePEc:eee:appene:v:390:y:2025:i:c:s0306261925005264.

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2. A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation. (2024). Hofmann, Rene ; Schwarzmayr, Paul ; Schwaiger, Michael ; Kasper, Lukas ; Javernik, Florian ; Birkelbach, Felix.
   In: Applied Energy.
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3. Mixed-Integer Conic Formulation of Unit Commitment with Stochastic Wind Power. (2023). Quan, Ran ; Huang, Liying ; Zheng, Haiyan.
   In: Mathematics.
   RePEc:gam:jmathe:v:11:y:2023:i:2:p:346-:d:1029916.

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4. A Survey of Combined Heat and Power-Based Unit Commitment Problem: Optimization Algorithms, Case Studies, Challenges, and Future Directions. (2023). Abdi, Hamdi.
   In: Mathematics.
   RePEc:gam:jmathe:v:11:y:2023:i:19:p:4170-:d:1253980.

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5. Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market. (2023). Nepomuceno, Lucas Santiago ; de Oliveira, Layon Mescolin ; da Silva, Ivo Chaves ; de Paula, Arthur Neves.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:23:p:7751-:d:1286926.

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   In: Renewable Energy.
   RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008005.

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   In: Energy.
   RePEc:eee:energy:v:285:y:2023:i:c:s0360544223029377.

   Full description at Econpapers || Download paper

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   In: Energy.
   RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023167.

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   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

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    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2022:i:1:p:284-:d:1013650.

    Full description at Econpapers || Download paper

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    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:9:p:3265-:d:805761.

    Full description at Econpapers || Download paper

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    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:10:p:3553-:d:814143.

    Full description at Econpapers || Download paper

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    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:161:y:2022:i:c:s1364032122003343.

    Full description at Econpapers || Download paper

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    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:159:y:2022:i:c:s136403212200140x.

    Full description at Econpapers || Download paper

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    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

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    In: Energy.
    RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006880.

    Full description at Econpapers || Download paper

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    In: Energy.
    RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032047.

    Full description at Econpapers || Download paper

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    In: Energy Policy.
    RePEc:eee:enepol:v:168:y:2022:i:c:s0301421522003226.

    Full description at Econpapers || Download paper

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    In: Applied Energy.
    RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011260.

    Full description at Econpapers || Download paper

20. 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

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    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:20:p:6658-:d:656355.

    Full description at Econpapers || Download paper

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    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:12:p:3576-:d:575802.

    Full description at Econpapers || Download paper

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    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:137:y:2021:i:c:s1364032120308753.

    Full description at Econpapers || Download paper

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    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:136:y:2021:i:c:s1364032120307000.

    Full description at Econpapers || Download paper

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    In: Renewable Energy.
    RePEc:eee:renene:v:179:y:2021:i:c:p:929-944.

    Full description at Econpapers || Download paper

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    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

27. 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

28. 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

29. 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

30. 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

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    In: Energy.
    RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002012.

    Full description at Econpapers || Download paper

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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.

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40. 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.

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41. 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.

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42. 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.

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43. 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.

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44. 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

45. 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.

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46. 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

47. 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.

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48. 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.

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49. 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.

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50. 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.

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51. 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.

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52. 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.

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53. 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

54. 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.

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55. 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

56. 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

57. 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

58. 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

59. 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

60. 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.

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61. 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

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