1. A Local-Temporal Convolutional Transformer for Day-Ahead Electricity Wholesale Price Forecasting. (2025). Roussac, Craig A ; Berry, Adam ; Tian, Hongda ; Zhang, Bowen.
   In: Sustainability.
   RePEc:gam:jsusta:v:17:y:2025:i:12:p:5533-:d:1680043.

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2. Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review. (2024). Mumtahina, Umme ; Wolfs, Peter ; Alahakoon, Sanath.
   In: Mathematics.
   RePEc:gam:jmathe:v:12:y:2024:i:21:p:3353-:d:1506840.

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3. Auditory-circuit-motivated deep network with application to short-term electricity price forecasting. (2024). Liang, Yan ; Gao, Xiao-Zhi ; Du, Pei ; Wu, Han.
   In: Energy.
   RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031237.

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4. From day-ahead to mid and long-term horizons with econometric electricity price forecasting models. (2024). Ghelasi, Paul ; Ziel, Florian.
   In: Papers.
   RePEc:arx:papers:2406.00326.

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5. Systemic analysis of a manufacturing process based on a small scale bakery. (2023). Wolniak, Radosaw ; Drozd, Radosaw ; Piwnik, Jan.
   In: Quality & Quantity: International Journal of Methodology.
   RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01408-7.

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6. Ensemble learning approach for advanced metering infrastructure in future smart grids. (2023). Masood, Sabeen ; Saeed, Muhammad Hamza ; Ahmed, Qazi Arbab ; Rahman, Saifur ; Abdushkour, Hesham ; Gommosani, Mohammad E ; Shamji, V R ; Faraj, Salim Nasar ; Irfan, Muhammad ; Althobiani, Faisal ; Ayub, Nasir.
   In: PLOS ONE.
   RePEc:plo:pone00:0289672.

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7. A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics. (2023). Rabelo, Luis ; Gutierrez-Franco, Edgar ; Ibrahim, Bibi ; Sarmiento, Alfonso T.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:13:p:5225-:d:1189011.

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8. The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study. (2022). Pourhaji, Nazila ; Asadpour, Mohammad ; Elkamel, Ali ; Ahmadian, Ali.
   In: Sustainability.
   RePEc:gam:jsusta:v:14:y:2022:i:5:p:3063-:d:765213.

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9. Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network. (2022). Chen, Zhiwen ; Peng, Xiangang ; Wu, Kaitong ; Quan, Huan ; Su, Haokun ; Liu, Hanyu.
   In: Mathematics.
   RePEc:gam:jmathe:v:10:y:2022:i:14:p:2366-:d:856787.

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10. Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid. (2022). Ali, Usman ; Mohsin, Syed Muhammad ; Aslam, Sheraz ; Ayub, Nasir ; Mustafa, Kainat.
    In: Forecasting.
    RePEc:gam:jforec:v:4:y:2022:i:4:p:51-948:d:979660.

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11. Methods of Forecasting Electric Energy Consumption: A Literature Review. (2022). Klyuev, Roman V ; Mengxu, QI ; Gavrina, Oksana A ; Martyushev, Nikita V ; Morgoev, Irbek D ; Efremenkov, Egor A ; Morgoeva, Angelika D.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:23:p:8919-:d:984103.

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1. Abdullah, A.; Sopian, W.; Arasid, W.; Nandiyanto, A.; Danuwijaya, A.; Abdullah, C. Short-term peak load forecasting using PSO-ANN methods: The case of Indonesia. J. Eng. Sci. Technol. 2018, 13, 2395–2404.
   Paper not yet in RePEc: Add citation now
   
2. Abedinia, O.; Amjady, N.; Zareipour, H. A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 2016, 32, 62–74. [CrossRef]
   Paper not yet in RePEc: Add citation now
   

3. Ahmad, W.; Ayub, N.; Ali, T.; Irfan, M.; Awais, M.; Shiraz, M.; Glowacz, A. Towards short term electricity load forecasting using improved support vector machine and extreme learning machine. Energies 2020, 13, 2907. [CrossRef]

4. Aimal, S.; Javaid, N.; Rehman, A.; Ayub, N.; Sultana, T.; Tahir, A. Data analytics for electricity load and price forecasting in the smart grid. In Proceedings of the Workshops of the International Conference on Advanced Information Networking and Applications, Matsue, Japan, 27–29 March 2019; pp. 582–591.
   Paper not yet in RePEc: Add citation now
   
5. Al-Betar, M.A.; Alyasseri, Z.A.A.; Awadallah, M.A.; Doush, I.A. Coronavirus herd immunity optimizer (CHIO). Neural Comput. Appl. 2021, 33, 5011–5042. [CrossRef] [PubMed]
   Paper not yet in RePEc: Add citation now
   
6. Albahli, S.; Shiraz, M.; Ayub, N. Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model. IEEE Access 2020, 8, 200971–200981. [CrossRef]
   Paper not yet in RePEc: Add citation now
   

7. Aslam, S.; Herodotou, H.; Mohsin, S.M.; Javaid, N.; Ashraf, N.; Aslam, S. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renew. Sustain. Energy Rev. 2021, 144, 110992. [CrossRef]

8. Aslam, S.; Iqbal, Z.; Javaid, N.; Khan, Z.A.; Aurangzeb, K.; Haider, S.I. Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes. Energies 2017, 10, 2065. [CrossRef] Sustainability 2021, 13, 12653 26 of 28

9. Aurangzeb, K.; Aslam, S.; Mohsin, S.M.; Alhussein, M. A fair pricing mechanism in smart grids for low energy consumption users. IEEE Access 2021, 9, 22035–22044. [CrossRef]
   Paper not yet in RePEc: Add citation now
   

10. Ayub, N.; Irfan, M.; Awais, M.; Ali, U.; Ali, T.; Hamdi, M.; Alghamdi, A.; Muhammad, F. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies 2020, 13, 5193. [CrossRef]

11. Bouktif, S.; Fiaz, A.; Ouni, A.; Serhani, M. Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 2018, 11, 1636. [CrossRef]

12. Carmichael, R.; Gross, R.; Hanna, R.; Rhodes, A.; Green, T. The Demand Response Technology Cluster: Accelerating UK residential consumer engagement with time-of-use tariffs, electric vehicles and smart meters via digital comparison tools. Renew. Sustain. Energy Rev. 2021, 139, 110701. [CrossRef]

13. Chen, K.; Chen, K.; Wang, Q.; He, Z.; Hu, J.; He, J. Short-term load forecasting with deep residual networks. IEEE Trans. Smart Grid 2018, 10, 3943–3952. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
14. Chitsaz, H.; Zamani-Dehkordi, P.; Zareipour, H.; Parikh, P. Electricity price forecasting for operational scheduling of behind-themeter storage systems. IEEE Trans. Smart Grid 2017, 9, 6612–6622. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
15. Cupp, J.; Beehler, M. Implementing smart grid communications. TECHBriefs 2008, 4, 5–8.
    Paper not yet in RePEc: Add citation now
    
16. Deng, Z.; Wang, B.; Xu, Y.; Xu, T.; Liu, C.; Zhu, Z. Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting. IEEE Access 2019, 7, 88058–88071. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

17. Dileep, G. A survey on smart grid technologies and applications. Renew. Energy 2020, 146, 2589–2625. [CrossRef]

18. Dong, Y.; Zhang, Z.; Hong, W.C. A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting. Energies 2018, 11, 1009. [CrossRef]

19. Eapen, R.; Simon, S. Performance analysis of combined similar day and day ahead short term electrical load forecasting using sequential hybrid neural networks. IETE J. Res. 2019, 65, 216–226. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

20. Fallah, S.; Deo, R.; Shojafar, M.; Conti, M.; Shamshirband, S. Computational intelligence approaches for energy load forecasting in smart energy management grids: State of the art, future challenges, and research directions. Energies 2018, 11, 596. [CrossRef]

21. Fan, G.F.; Peng, L.L.; Hong, W.C. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. Appl. Energy 2018, 224, 13–33. [CrossRef]

22. Ghadimi, N.; Akbarimajd, A.; Shayeghi, H.; Abedinia, O. Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 2018, 161, 130–142. [CrossRef]

23. Ghasemi, A.; Shayeghi, H.; Moradzadeh, M.; Nooshyar, M. A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. Appl. Energy 2016, 177, 40–59. [CrossRef] Sustainability 2021, 13, 12653 27 of 28

24. Ghassemi, A.; Bavarian, S.; Lampe, L. Cognitive radio for smart grid communications. In Proceedings of the 2010 First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA, 4–6 October 2010; pp. 297–302. Sustainability 2021, 13, 12653 28 of 28
    Paper not yet in RePEc: Add citation now
    
25. Ghosal, A.; Conti, M. Key management systems for smart grid advanced metering infrastructure: A survey. IEEE Commun. Surv. Tutor. 2019, 21, 2831–2848. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
26. Hor, C.L.; Watson, S.; Majithia, S. Analyzing the impact of weather variables on monthly electricity demand. IEEE Trans. Power Syst. 2005, 20, 2078–2085. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
27. Huang, G.B.; Zhu, Q.Y.; Siew, C.K. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70, 489–501. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

28. Jin, X.B.; Zheng, W.Z.; Kong, J.L.; Wang, X.Y.; Bai, Y.T.; Su, T.L.; Lin, S. Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization. Energies 2021, 14, 1596. [CrossRef]

29. Jindal, A.; Singh, M.; Kumar, N.; Response, C.A. Scheme for Peak Load Reduction in Smart Grid. IEEE Trans. Ind. Electron. 2018, 65, 8993–9004. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

30. Kabalci, Y. A survey on smart metering and smart grid communication. Renew. Sustain. Energy Rev. 2016, 57, 302–318. [CrossRef]

31. Kai, C.; Li, H.; Xu, L.; Li, Y.; Jiang, T. Energy-efficient device-to-device communications for green smart cities. IEEE Trans. Ind. Inform. 2018, 14, 1542–1551. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

32. Keles, D.; Scelle, J.; Paraschiv, F.; Fichtner, W. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy 2016, 162, 218–230. [CrossRef]

33. Ko, J.; Terzis, A.; Dawson-Haggerty, S.; Culler, D.; Hui, J.; Levis, P. Connecting low-power and lossy networks to the internet. IEEE Commun. Mag. 2011, 49, 96–101.
    Paper not yet in RePEc: Add citation now
    
34. Koohi-Fayegh, S.; Rosen, M. A review of energy storage types, applications and recent developments. J. Energy Storage 2020, 27, 101047. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

35. Kuo, P.H.; Huang, C.J. An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 2018, 10, 1280. [CrossRef]

36. Lago, J.; De Ridder, F.; De Schutter, B. Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 2018, 221, 386–405. [CrossRef]

37. Lago, J.; De Ridder, F.; Vrancx, P.; De Schutter, B. Forecasting day-ahead electricity prices in Europe: The importance of considering market integration. Appl. Energy 2018, 211, 890–903. [CrossRef]

38. Lahouar, A.; Slama, J. Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manag. 2015, 103, 1040–1051. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

39. Li, Y.; Kubicki, S.; Guerriero, A.; Rezgui, Y. Review of building energy performance certification schemes towards future improvement. Renew. Sustain. Energy Rev. 2019, 113, 109244. [CrossRef]

40. Li, Z.L.; Xia, J.; Liu, A.; Li, P. States prediction for solar power and wind speed using BBA-SVM. IET Renew. Power Gener. 2019, 13, 1115–1122. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

41. Liang, Y.; Niu, D.; Hong, W.C. Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 2019, 166, 653–663. [CrossRef]

42. Liu, Y.; Wang, W.; Ghadimi, N. Electricity load forecasting by an improved forecast engine for building level consumers. Energy 2017, 139, 18–30. [CrossRef]

43. Liu, Y.; Yuen, C.; Huang, S.; Hassan, N.; Wang, X.; Xie, S. Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid. IEEE J. Sel. Top. Signal Process. 2014, 8, 1084–1097. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
44. Ma, Z.; Zhong, H.; Xie, L.; Xia, Q.; Kang, C. Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: An ERCOT case study. J. Mod. Power Syst. Clean Energy 2018, 6, 281–291. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

45. Mahmood, A.; Javaid, N.; Razzaq, S. A review of wireless communications for smart grid. Renew. Sustain. Energy Rev. 2015, 41, 248–260. [CrossRef]

46. Morley, S.; Brito, T.; Welling, D. Measures of model performance based on the log accuracy ratio. Space Weather 2018, 16, 69–88. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
47. Mujeeb, S.; Javaid, N.; Akbar, M.; Khalid, R.; Nazeer, O.; Khan, M. Big data analytics for price and load forecasting in smart grids. In Proceedings of the International Conference on Broadband and Wireless Computing, Communication and Applications, Taichung, Taiwan, 27–29 October 2018; pp. 77–87.
    Paper not yet in RePEc: Add citation now
    

48. Pérez-Chacón, R.; Luna-Romera, J.M.; Troncoso, A.; Martínez-lvarez, F.; Riquelme, J.C. Big data analytics for discovering electricity consumption patterns in smart cities. Energies 2018, 11, 683. [CrossRef]

49. Patil, M.; Deshmukh, S.; Agrawal, R. Electric power price forecasting using data mining techniques. In Proceedings of the 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), Pune, India, 24–26 February 2017; pp. 217–223.
    Paper not yet in RePEc: Add citation now
    
50. Rafiei, M.; Niknam, T.; Khooban, M.H. Probabilistic forecasting of hourly electricity price by generalization of ELM for usage in improved wavelet neural network. IEEE Trans. Ind. Inform. 2016, 13, 71–79. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
51. Rahimi, F.; Ipakchi, A. Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid 2010, 1, 82–88. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

52. Raviv, E.; Bouwman, K.; Van Dijk, D. Forecasting day-ahead electricity prices: Utilizing hourly prices. Energy Econ. 2015, 50, 227–239. [CrossRef]

53. Rojas-Domínguez, A.; Padierna, L.C.; Valadez, J.M.C.; Puga-Soberanes, H.J.; Fraire, H.J. Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access 2017, 6, 7164–7176. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
54. Shayeghi, H.; Ghasemi, A.; Moradzadeh, M.; Nooshyar, M. Simultaneous day-ahead forecasting of electricity price and load in smart grids. Energy Convers. Manag. 2015, 95, 371–384. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
55. Siano, P. Demand response and smart grids—A survey. Renew. Sustain. Energy Rev. 2014, 30, 461–478. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

56. Ugurlu, U.; Oksuz, I.; Tas, O. Electricity price forecasting using recurrent neural networks. Energies 2018, 11, 1255. [CrossRef]

57. Vaccaro, A.; Villacci, D. Performance analysis of low earth orbit satellites for power system communication. Electric Power Syst. Res. 2005, 73, 287–294. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
58. Vadari, S. Electric System Operations: Evolving to the Modern Grid; Artech House: Braga, Portugal, 2020.
    Paper not yet in RePEc: Add citation now
    
59. Varshney, H.; Sharma, A.; Kumar, R. A hybrid approach to price forecasting incorporating exogenous variables for a day ahead electricity market. In Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 4–6 July 2016; pp. 1–6.
    Paper not yet in RePEc: Add citation now
    
60. Wang, J.; Liu, F.; Song, Y.; Zhao, J. A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl. Soft Comput. 2016, 48, 281–297. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
61. Wang, K.; Xu, C.; Zhang, Y.; Guo, S.; Zomaya, A. Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data 2017, 5, 34–45. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
62. Wang, L.; Zhang, Z.; Chen, J. Short-term electricity price forecasting with stacked denoising autoencoders. IEEE Trans. Power Syst. 2016, 32, 2673–2681. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
63. Wang, Z.; Wang, Y.; Zeng, R.; Srinivasan, R.; Ahrentzen, S. Random Forest based hourly building energy prediction. Energy Build. 2018, 171, 11–25. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

64. Yang, W.; Wang, J.; Niu, T.; Du, P. A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Appl. Energy 2019, 235, 1205–1225. [CrossRef]

65. Yang, W.; Wang, J.; Niu, T.; Du, P. A novel system for multi-step electricity price forecasting for electricity market management. Appl. Soft Comput. 2020, 88, 106029. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
66. Zhang, X.; Wang, J.; Zhang, K. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electr. Power Syst. Res. 2017, 146, 270–285. [CrossRef]
    Paper not yet in RePEc: Add citation now
    
67. Zhou, L.; Wu, D.; Chen, J.; Dong, Z. Greening the smart cities: Energy-efficient massive content delivery via D2D communications. IEEE Trans. Ind. Inform. 2017, 14, 1626–1634. [CrossRef]
    Paper not yet in RePEc: Add citation now
    

1. Very Short-Term Load Forecasting Model for Large Power System Using GRU-Attention Algorithm. (2025). Song, Kyung-Bin ; Yoon, Sung-Guk ; Kim, Tae-Geun.
   In: Energies.
   RePEc:gam:jeners:v:18:y:2025:i:13:p:3229-:d:1683566.

   Full description at Econpapers || Download paper

2. Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects. (2024). Arafat, M Y ; Hossain, M J ; Alam, Md Morshed.
   In: Renewable and Sustainable Energy Reviews.
   RePEc:eee:rensus:v:190:y:2024:i:pa:s1364032123009462.

   Full description at Econpapers || Download paper

3. A hybrid approach based machine learning models in electricity markets. (2024). Gomez, William ; Lo, Shih-Che ; Wang, Fu-Kwun.
   In: Energy.
   RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033820.

   Full description at Econpapers || Download paper

4. A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection. (2024). Li, Xuefeng ; Lin, Qingcheng ; Xiao, Hui ; Cai, Huiling ; Liu, Hanwei.
   In: Energy.
   RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031183.

   Full description at Econpapers || Download paper

5. An optimal solutions-guided deep reinforcement learning approach for online energy storage control. (2024). Shi, Jian ; Wu, Jiaman ; Xu, Gaoyuan ; Wang, Dan ; Lu, Chenbei ; Han, Zhu.
   In: Applied Energy.
   RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002988.

   Full description at Econpapers || Download paper

6. Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP. (2024). Ye, Xianming ; Naidoo, Raj ; van Zyl, Corne.
   In: Applied Energy.
   RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014435.

   Full description at Econpapers || Download paper

7. Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM. (2023). Li, XI ; Yang, Xiaopeng ; Wang, Pengzhan ; Xu, Fengliang ; Gu, BO.
   In: Sustainability.
   RePEc:gam:jsusta:v:15:y:2023:i:8:p:6538-:d:1121748.

   Full description at Econpapers || Download paper

8. Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle. (2023). Zhang, Ziyan ; Lu, Xinwei ; Chao, Xiangyun ; Shen, Junhao ; Wu, Feng ; Zeng, Fanlong ; Yao, Dongwei.
   In: Sustainability.
   RePEc:gam:jsusta:v:15:y:2023:i:5:p:4607-:d:1087815.

   Full description at Econpapers || Download paper

9. Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids. (2023). Marzouk, Radwa ; Salem, Nermin M ; Eldesouki, Mohamed I ; Alabdulkreem, Eatedal ; Abdelmageed, Amgad Atta ; Zamani, Abu Sarwar ; Gaddah, Abdulbaset ; Motwakel, Abdelwahed.
   In: Sustainability.
   RePEc:gam:jsusta:v:15:y:2023:i:2:p:1524-:d:1034138.

   Full description at Econpapers || Download paper

10. Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques. (2023). Qaiyum, Sana ; Kshirsagar, Pravin R ; Chakrabarti, Prasun ; Margala, Martin ; Irshad, Kashif.
    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2023:i:14:p:11081-:d:1194904.

    Full description at Econpapers || Download paper

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    In: Future Internet.
    RePEc:gam:jftint:v:15:y:2023:i:2:p:76-:d:1069521.

    Full description at Econpapers || Download paper

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    In: Forecasting.
    RePEc:gam:jforec:v:5:y:2023:i:1:p:17-335:d:1087297.

    Full description at Econpapers || Download paper

13. Load Forecasting Techniques and Their Applications in Smart Grids. (2023). Fouda, Mostafa M ; Mahmoud, Mohamed ; Ibrahem, Mohamed I ; Metwally, Khaled ; Habbak, Hany.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:3:p:1480-:d:1055616.

    Full description at Econpapers || Download paper

14. A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection. (2023). Kumar, Varun ; Gangatharan, Sivasankar ; Tripathi, Saurabh Mani ; Elavarasan, Rajvikram Madurai ; Houran, Mohamad Abou ; Srivastava, Ankit Kumar ; Pandey, Ajay Shekhar.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:2:p:867-:d:1033102.

    Full description at Econpapers || Download paper

15. A Review for Green Energy Machine Learning and AI Services. (2023). Mehta, Yukta ; Xu, Rui ; Gao, Jerry ; Wu, Jane ; Lim, Benjamin.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:15:p:5718-:d:1207294.

    Full description at Econpapers || Download paper

16. Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models. (2023). Suresh, Vishnu ; Sikorski, Tomasz ; Janik, Przemysaw ; Aksan, Fachrizal.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:14:p:5381-:d:1194290.

    Full description at Econpapers || Download paper

17. Deep and Machine Learning Models to Forecast Photovoltaic Power Generation. (2023). Cantillo-Luna, Sergio ; Moreno-Chuquen, Ricardo ; Anders, George ; Celeita, David.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:10:p:4097-:d:1147209.

    Full description at Econpapers || Download paper

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    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:10:p:4060-:d:1145829.

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19. A hybrid framework for forecasting power generation of multiple renewable energy sources. (2023). Liang, Yongtu ; Liao, QI ; Kleme, Jii Jaromir ; Zheng, Jianqin ; Du, Jian ; Wang, Bohong.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:172:y:2023:i:c:s1364032122009273.

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20. Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models. (2023). Marinakis, Vangelis ; Doukas, Haris ; Spiliotis, Evangelos ; Sarmas, Elissaios ; Stamatopoulos, Efstathios.
    In: Renewable Energy.
    RePEc:eee:renene:v:216:y:2023:i:c:s0960148123009035.

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21. Sustainable energies and machine learning: An organized review of recent applications and challenges. (2023). Asadi, Somayeh ; Ifaei, Pouya ; Nazari-Heris, Morteza ; Yoo, Changkyoo ; Tayerani, Amir Saman.
    In: Energy.
    RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033187.

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22. Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach. (2023). Lee, Seonho ; Joo, Junghee ; Kim, Ji Won ; Han, Jeehoon ; Byun, Jaewon ; Hwangbo, Soonho.
    In: Applied Energy.
    RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011467.

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23. Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme. (2023). Serrano-Arevalo, Tania Itzel ; Ponce-Ortega, Jose Maria ; Lopez-Flores, Francisco Javier ; Ramirez-Marquez, Cesar ; Raya-Tapia, Alma Yunuen.
    In: Applied Energy.
    RePEc:eee:appene:v:348:y:2023:i:c:s0306261923009613.

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24. Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions. (2023). Du, Zhimin ; Zhu, XU ; Jin, Xinqiao ; Liang, Xinbin ; Chen, Siliang.
    In: Applied Energy.
    RePEc:eee:appene:v:344:y:2023:i:c:s0306261923006086.

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25. Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting. (2023). Liang, Yan ; Heng, Jiani ; Wu, Han.
    In: Applied Energy.
    RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003598.

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26. A Brief Review of Microgrid Surveys, by Focusing on Energy Management System. (2022). Abdi, Hamdi.
    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2022:i:1:p:284-:d:1013650.

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27. A Comparison of Recent Requirements Gathering and Management Tools in Requirements Engineering for IoT-Enabled Sustainable Cities. (2022). Lee, Scott Uk-Jin ; Younus, Muhammad Usman ; Nadeem, Muhammad Asgher.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:4:p:2427-:d:753919.

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28. Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources. (2022). Maqsood, Tahir ; Mohsin, Syed Muhammad ; Madani, Sajjad Ahmed.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:23:p:16317-:d:995526.

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29. Line Overload Alleviations in Wind Energy Integrated Power Systems Using Automatic Generation Control. (2022). Ullah, Zahid ; Aslam, Sheraz ; Asghar, Rafiq ; Yafoz, Ayman ; Basit, Abdul.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:19:p:11810-:d:919551.

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30. Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid. (2022). Ali, Usman ; Mohsin, Syed Muhammad ; Aslam, Sheraz ; Ayub, Nasir ; Mustafa, Kainat.
    In: Forecasting.
    RePEc:gam:jforec:v:4:y:2022:i:4:p:51-948:d:979660.

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31. Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids. (2022). Mohsin, Syed Muhammad ; Ur, Sajawal ; Habib, Muhammad Asif ; Ahmad, Mudassar ; Mustafa, Kainat ; Hayder, Israa Adil ; Khan, Farrukh Aslam.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2022:i:1:p:276-:d:1016182.

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32. Hydraulic Tests of the PZ0 Gear Micropump and the Importance Rank of Its Design and Operating Parameters. (2022). Osiski, Piotr ; Partyka, Marian A ; Deptua, Adam.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:9:p:3068-:d:799714.

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33. Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO. (2022). Xing, Zuoxia ; Zhang, Pengfei ; Liu, Jinglu ; Xu, Jian ; Cui, Jia ; Hu, BO.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:8:p:2767-:d:790394.

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34. Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation. (2022). Chen, YU ; Shao, Nan.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:6:p:2151-:d:771832.

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35. Review on Deep Learning Research and Applications in Wind and Wave Energy. (2022). Li, Hua ; Gu, Chengcheng .
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:4:p:1510-:d:752131.

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36. Optimal Power Sharing in Microgrids Using the Artificial Bee Colony Algorithm. (2022). Rahim, Sahar ; Khan, Rehan Ali ; Jiang, Quanyuan ; Ullah, Kalim ; Geng, Guangchao.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:3:p:1067-:d:739609.

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37. Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm. (2022). Zhang, Ziqi ; Chen, Ying.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:23:p:9197-:d:993217.

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38. 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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39. Machine Learning for Short-Term Load Forecasting in Smart Grids. (2022). Clavijo-Buritica, Nicolas ; Rabelo, Luis ; Gutierrez-Franco, Edgar ; Ibrahim, Bibi.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:21:p:8079-:d:958829.

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40. Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load. (2022). Lu, Zhilin ; Yang, Xinhe ; Luo, Shuxin ; Liu, Mingbo ; Peng, Hongqiao ; Zhu, Haojun ; Zeng, Guihua ; Jin, Bingjie.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:20:p:7584-:d:942138.

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41. The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting. (2022). Yudhanto, Yudho ; Sulandari, Winita ; Rodrigues, Paulo Canas.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:16:p:5838-:d:885936.

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42. Data-Intensive Computing in Smart Microgrids: Volume II. (2022). Aslam, Sheraz ; Herodotou, Herodotos.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:16:p:5833-:d:885827.

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43. Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges. (2022). Peyghami, Saeed ; Sandelic, Monika ; Blaabjerg, Frede ; Sangwongwanich, Ariya.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:159:y:2022:i:c:s1364032122000557.

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44. Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Koreas energy transition policy. (2022). Lee, Yoonjae ; Hwangbo, Soonho ; Ha, Byeongmin.
    In: Renewable Energy.
    RePEc:eee:renene:v:200:y:2022:i:c:p:69-87.

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45. A simple but accurate two-state model for nowcasting PV power. (2022). Badescu, Viorel ; Stefu, Nicoleta ; Paulescu, Marius ; Calinoiu, Delia ; Dughir, Ciprian ; Sabadus, Andreea.
    In: Renewable Energy.
    RePEc:eee:renene:v:195:y:2022:i:c:p:322-330.

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46. Medium and long-term electricity market trading strategy considering renewable portfolio standard in the transitional period of electricity market reform in Jiangsu, China. (2022). Jiang, YU ; Feng, Yingchun ; Li, Tianyu ; Chen, Tao ; Gao, Ciwei.
    In: Energy Economics.
    RePEc:eee:eneeco:v:107:y:2022:i:c:s0140988322000445.

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47. Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles. (2022). Seyfi, Mohammad ; Mehdinejad, Mehdi ; Shayanfar, Heidarali ; Mohammadi-Ivatloo, Behnam.
    In: Applied Energy.
    RePEc:eee:appene:v:321:y:2022:i:c:s0306261922006705.

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48. Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism. (2022). Zeiler, Wim ; Khan, Waqas ; Walker, Shalika ; Yu, Juo.
    In: Applied Energy.
    RePEc:eee:appene:v:319:y:2022:i:c:s0306261922006389.

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49. An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting. (2021). Ma, Yuxuan ; Shen, Yamin ; Deng, Simin ; Kuo, Ping-Huan ; Huang, Chiou-Jye.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:4:p:1694-:d:493434.

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50. Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid. (2021). Farooq, Umer ; Haider, Syed Irtaza ; Aslam, Shahzad ; Bukhsh, Rasool ; Ayub, Nasir ; Alvi, Muhammad Junaid ; Rukh, Gul ; Albogamy, Fahad R ; Azar, Ahmad Taher.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:22:p:12653-:d:680508.

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51. Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids. (2021). Khan, Sheraz ; Alkhammash, Hend I ; Rukh, Gul ; Hafeez, Ghulam ; Ali, Faheem ; Albogamy, Fahad R.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:20:p:11429-:d:657730.

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52. An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective. (2021). Alquthami, Thamer ; Rasheed, Muhammad B ; Awais, Muhammad ; Milyani, Ahmad H.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:11:p:6066-:d:563862.

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53. AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting. (2021). Baik, Sung Wook ; Khan, Samee Ullah ; Lee, Mi Young ; Ul, Ijaz ; Min, Fath U.
    In: Mathematics.
    RePEc:gam:jmathe:v:9:y:2021:i:19:p:2456-:d:648944.

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54. Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. (2021). Aslam, Sheraz ; Khan, Sajjad ; Mustafa, Iqra.
    In: Forecasting.
    RePEc:gam:jforec:v:3:y:2021:i:3:p:28-477:d:580060.

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55. Introduction to the Special Issue on Data-Intensive Computing in Smart Microgrids. (2021). Herodotou, Herodotos.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:9:p:2704-:d:550860.

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56. Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data. (2021). Kontogiannis, Dimitrios ; Bargiotas, Dimitrios ; Daskalopulu, Aspassia.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:3:p:752-:d:490715.

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57. A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms. (2021). Zhang, Wei ; Hou, Tingting ; Fang, Rengcun ; Tang, Jinrui ; Liu, Jianchao ; Ge, Ganheng ; Yang, Dongjun.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:22:p:7820-:d:685269.

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58. Design of Ensemble Forecasting Models for Home Energy Management Systems. (2021). Santos, Samira ; da Graa, Maria ; Bot, Karol ; Laouali, Inoussa ; Ruano, Antonio.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:22:p:7664-:d:680311.

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59. Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program. (2021). Yu, Zi-Xuan ; Li, Yuan-Kang ; Xu, Yi-Peng ; Aslam, Sheraz.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:15:p:4597-:d:604284.

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60. Artificial intelligence techniques for enabling Big Data services in distribution networks: A review. (2021). Barja-Martinez, Sara ; Villafafila-Robles, Roberto ; Lloret-Gallego, Pau ; Aragues-Pealba, Monica ; Munne-Collado, Ingrid ; Bullich-Massague, Eduard.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:150:y:2021:i:c:s1364032121007413.

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61. Renewable energy: Is it a global challenge or opportunity? Focusing on different income level countries through Panel Smooth Transition Regression Model. (2021). Raza, Syed ; Zhang, Qingyu ; Ullah, Asad.
    In: Renewable Energy.
    RePEc:eee:renene:v:177:y:2021:i:c:p:689-699.

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62. Integrating renewable energy into mining operations: Opportunities, challenges, and enabling approaches. (2021). Igogo, Tsisilile ; Lowder, Travis ; Awuah-Offei, Kwame ; Engel-Cox, Jill ; Newman, Alexandra.
    In: Applied Energy.
    RePEc:eee:appene:v:300:y:2021:i:c:s0306261921007790.

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63. Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions. (2021). Bellido-Jimenez, Juan Antonio ; Gualda, Javier Estevez ; Garcia-Marin, Amanda Penelope.
    In: Applied Energy.
    RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006358.

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64. Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling. (2020). Yan, Yiqi ; Liu, Shichao ; Cheng, Qiangqiang ; Alzayed, Mohamad ; Chaoui, Hicham ; Yang, Chunsheng.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:24:p:6489-:d:458887.

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65. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. (2020). Ali, Usman ; Ayub, Nasir ; Irfan, Muhammad ; Awais, Muhammad ; Hamdi, Mohammed ; Muhammad, Fazal ; Alghamdi, Abdullah.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:19:p:5193-:d:424040.

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66. Short-Term Load Forecasting for Spanish Insular Electric Systems. (2020). Juan, Jesus ; Caro, Eduardo.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:14:p:3645-:d:384861.

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