1. Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network. (2023). Mastorocostas, Paris ; Kandilogiannakis, George ; Hilas, Constantinos ; Voulodimos, Athanasios.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:10:p:4227-:d:1151909.

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2. An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems. (2022). Seydi, Vahid ; Sharifi, Arash ; Falahiazar, Alireza.
   In: Journal of Combinatorial Optimization.
   RePEc:spr:jcomop:v:44:y:2022:i:1:d:10.1007_s10878-022-00860-3.

   Full description at Econpapers || Download paper

3. A Hybrid Forecast Model for Household Electric Power by Fusing Landmark-Based Spectral Clustering and Deep Learning. (2022). Wang, Zhiteng ; Shi, Jiarong.
   In: Sustainability.
   RePEc:gam:jsusta:v:14:y:2022:i:15:p:9255-:d:874132.

   Full description at Econpapers || Download paper

4. Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources. (2022). Azib, Toufik ; Bethoux, Olivier ; Alves, Francisco ; Ceschia, Adriano.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:9:p:3364-:d:808861.

   Full description at Econpapers || Download paper

5. A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting. (2022). Masood, Zaki ; Choi, Yonghoon ; Gantassi, Rahma.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:7:p:2623-:d:786419.

   Full description at Econpapers || Download paper

6. Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting. (2021). Bao, Yukun ; Huang, Yanmei ; Zhang, Xiaoyuan ; Deng, Changrui.
   In: Energies.
   RePEc:gam:jeners:v:14:y:2021:i:13:p:4036-:d:588369.

   Full description at Econpapers || Download paper

7. Dual-Frequency Output of Wireless Power Transfer System with Single Inverter Using Improved Differential Evolution Algorithm. (2020). Snael, Vaclav ; Jin, Nan ; Bie, Lizhong ; Guo, Leilei ; Zhang, Jitao ; Wu, Jie ; Tao, Jiagui.
   In: Energies.
   RePEc:gam:jeners:v:13:y:2020:i:9:p:2209-:d:353398.

   Full description at Econpapers || Download paper

8. Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram. (2020). Wang, Zhenpo ; Yang, Ying ; Zhang, Weige ; Wei, Shaoyuan.
   In: Energies.
   RePEc:gam:jeners:v:13:y:2020:i:23:p:6227-:d:451544.

   Full description at Econpapers || Download paper

9. PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting. (2020). Refaat, Shady S ; Massaoudi, Mohamed ; Chihi, Ines ; Abu-Rub, Haitham ; Oueslati, Fakhreddine S.
   In: Energies.
   RePEc:gam:jeners:v:13:y:2020:i:20:p:5464-:d:431259.

   Full description at Econpapers || Download paper

10. Optimal Sizing of Fuel Cell Hybrid Power Sources with Reliability Consideration. (2020). Azib, Toufik ; Bethoux, Olivier ; Alves, Francisco ; Ceschia, Adriano.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:13:p:3510-:d:381597.

    Full description at Econpapers || Download paper

1. Al-Musaylh M.S.; Deo R.C.; Adamowski J.F.; Li Y. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Adv. Eng. Inf. 2018, 35, 1-16.
   Paper not yet in RePEc: Add citation now
   

2. Al-Musaylh M.S.; Deo R.C.; Li Y. Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting. Appl. Energy 2018, 217, 422-439.

3. An Y.; Zhou Y.; Li R. Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods. Energies 2019, 12.

4. Bartz-Beielstein T.; Limbourg P.; Mehnen J. Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques. Proceedings of the 2003 Congress on Evolutionary Computation (CEC’03 IEEE), Canberra, Australia, 8–12 December 2003, ; pp. 1780-1787.
   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. Bento P.M.R.; Pombo J.A.N.; Calado M.R.A. A bat optimized neural network and wavelet transform approach for short-term price forecasting. Appl. Energy 2018, 210, 88-97.

7. Ceci M.; Corizzo R.; Malerba D. Spatial autocorrelation and entropy for renewable energy forecasting. Data Min. Knowl. Discov. 2019, 33, 698-729.
   Paper not yet in RePEc: Add citation now
   
8. Coello C.A.C.; Pulido G.T.; Lechuga M.S. Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 2004, 8, 256-279.
   Paper not yet in RePEc: Add citation now
   
9. Corizzo R.; Pio G.; Ceci M. DENCAST: Distributed density-based clustering for multi-target regression. J. Big Data 2019, 6, 43.
   Paper not yet in RePEc: Add citation now
   
10. Eseye A.T.; Lehtonen M.; Tukia T. Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems. IEEE Access 2019, 7, 91463-91475.
    Paper not yet in RePEc: Add citation now
    

11. Foley A.M.; Leahy P.G.; Marvuglia A. Current methods and advances in forecasting of wind power generation. Renew. Energy 2012, 37, 1-8.

12. Hatori T.; Sato-Ilic M. A Fuzzy Clustering Method Using the Relative Structure of the Belongingness of Objects to Clusters. Procedia Comput. Sci. 2014, 35, 994-1002.
    Paper not yet in RePEc: Add citation now
    
13. Jin Y.; Sendhoff B. Pareto-based multiobjective machine learning: An overview and case studies. IEEE Trans. Syst. Man Cybern. Part C 2008, 38, 397-415.
    Paper not yet in RePEc: Add citation now
    
14. Johannesen N.J.; Kolhe M.; Goodwin M. Relative evaluation of regression tools for urban area electrical energy demand forecasting. J. Clean. Prod. 2019, 218, 555-564.
    Paper not yet in RePEc: Add citation now
    
15. Koroglu S.; Sergeant P.; Umurkan N. Comparison of Analytical, Finite Element and Neural Network Methods to Study Magnetic Shielding. Simul. Model. Pract. Theory 2010, 18, 206-216.
    Paper not yet in RePEc: Add citation now
    
16. La Rosa M.; Rabinovich M.I.; Huerta R. Slow regularization through chaotic oscillation transfer in an unidirectional chain of Hindmarsh–Rose models. Phys. Lett. A 2000, 266, 88-93.
    Paper not yet in RePEc: Add citation now
    
17. Li S.; Wang P.; Goel L. Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr. Power Syst. Res. 2015, 122, 96-103.
    Paper not yet in RePEc: Add citation now
    

18. Liu P.; Zheng P.; Chen Z. Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting. Energies 2019, 12.

19. Liu T.; Jin Y.; Gao Y. A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization. Energies 2019, 12.

20. Majkowski A.; Kołodziej M.; Rak R.J. Joint Time-Frequency and Wavelet Analysis—An Introduction. Metrol. Meas. Syst. 2014, 21, 741-758.
    Paper not yet in RePEc: Add citation now
    

21. Meng M.; Wang L.; Shang W. Decomposition and forecasting analysis of China’s household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models. Energy 2018, 165, 143-152.

22. Moore J. Application of Particle Swarm to Multiobjective Optimization; Technical Report; Department of Computer Science and Software Engineering, Auburn University: Auburn, AL, USA, 1999.
    Paper not yet in RePEc: Add citation now
    
23. Pelleg D.; Moore A. X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 2000.
    Paper not yet in RePEc: Add citation now
    
24. Pulido G.T.; Coello C.A.C. Using Clustering Techniques to Improve the Performance of a Multi-Objective Particle Swarm Optimizer. Genetic and Evolutionary Computation Conference; Springer: Berlin/Heidelberg, Germany, 2004; pp. 225-237.
    Paper not yet in RePEc: Add citation now
    

25. Shah I.; Iftikhar H.; Ali S.; Wang D. Short-Term Electricity Demand Forecasting Using Components Estimation Technique. Energies 2019, 12.

26. Shi J.; Guo J.; Zheng S. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew. Sustain. Energy Rev. 2012, 16, 3471-3480.

27. Singh S.; Yassine A. Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies 2018, 11.

28. Tian C.; Hao Y. A novel nonlinear combined forecasting system for short-term load forecasting. Energies 2018, 11.

29. Trull Ó.; García-Díaz J.C.; Troncoso A. Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies 2019, 12.

30. Velsink H. Time Series Analysis of 3D Coordinates Using Nonstochastic Observations. J. Appl. Geod. 2016, 10, 5-16.
    Paper not yet in RePEc: Add citation now
    
31. Verma A.; Karan A.; Mathur A.; Chethan S. Analysis of time-series method for demand forecasting. J. Eng. Appl. Sci. 2017, 12, 3102-3107.
    Paper not yet in RePEc: Add citation now
    

32. Wang J.; Zhu S.; Zhang W. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 2010, 35, 1671-1678.

33. Werbos P.J. Backpropagation through time: What it does and how to do it. Proc. IEEE 1990, 78, 1550-1560.
    Paper not yet in RePEc: Add citation now
    

34. Xiao L.; Wang J.; Hou R. A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 2015, 82, 524-549.

35. Yang A.; Li W.; Yang X. Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines. Knowl. Based Syst. 2019, 163, 159-173.
    Paper not yet in RePEc: Add citation now
    
36. Yixian L.I.U.; Roberts M.C.; Sioshansi R. A vector autoregression weather model for electricity supply and demand modeling. J. Mod. Power Syst. Clean Energy 2018, 6, 763-776.
    Paper not yet in RePEc: Add citation now
    
37. Yunishafira A. Determining the Appropriate Demand Forecasting Using Time Series Method: Study Case at Garment Industry in Indonesia. KnE Soc. Sci. 2018, 3, 553-564.
    Paper not yet in RePEc: Add citation now
    

38. Zhang J.; Wei Y.M.; Li D.; Tan Z.; Zhou J. Short term electricity load forecasting using a hybrid model. Energy 2018, 158, 774-781.

39. 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.
    Paper not yet in RePEc: Add citation now
    

40. Zhang Y.; Wang J.; Lu H. Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting. Energies 2019, 12.

1. A Hybrid Forecasting Model for Electricity Demand in Sustainable Power Systems Based on Support Vector Machine. (2024). Sun, Yalu ; Li, Xuejun ; Song, Wenqin ; Jiang, Minghua ; Cai, Deyu.
   In: Energies.
   RePEc:gam:jeners:v:17:y:2024:i:17:p:4377-:d:1469066.

   Full description at Econpapers || Download paper

2. Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting. (2024). Ghimire, Sujan ; Salcedo-Sanz, Sancho ; Deo, Ravinesh C ; Casillas-Perez, David.
   In: Applied Energy.
   RePEc:eee:appene:v:353:y:2024:i:pa:s030626192301423x.

   Full description at Econpapers || Download paper

3. Power grid operation optimization and forecasting using a combined forecasting system. (2023). Liu, Zhenkun ; Zhang, Lifang ; Wang, Jianzhou.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:42:y:2023:i:1:p:124-153.

   Full description at Econpapers || Download paper

4. Information flow dynamics between geopolitical risk and major asset returns. (2023). Umar, Zaghum ; Bossman, Ahmed ; Vo, Xuan Vinh ; Choi, Sun-Yong.
   In: PLOS ONE.
   RePEc:plo:pone00:0284811.

   Full description at Econpapers || Download paper

5. Machine-Learning-Based Electric Power Forecasting. (2023). Zhu, Yuyu ; Wang, XU ; Chen, Gang.
   In: Sustainability.
   RePEc:gam:jsusta:v:15:y:2023:i:14:p:11299-:d:1198382.

   Full description at Econpapers || Download paper

6. An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand. (2023). Pannakkong, Warut ; Huynh, Van-Nam ; Aswanuwath, Lalitpat ; Buddhakulsomsiri, Jirachai ; Karnjana, Jessada.
   In: Energies.
   RePEc:gam:jeners:v:17:y:2023:i:1:p:78-:d:1305656.

   Full description at Econpapers || Download paper

7. A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting. (2023). Pannakkong, Warut ; Huynh, Van-Nam ; Aswanuwath, Lalitpat ; Buddhakulsomsiri, Jirachai ; Karnjana, Jessada.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:4:p:1860-:d:1067428.

   Full description at Econpapers || Download paper

8. Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting. (2023). Yaseen, Zaher Mundher ; Xiang, Yong ; Prasad, Ramendra ; Ali, Mumtaz ; Jamei, Mehdi.
   In: Renewable Energy.
   RePEc:eee:renene:v:205:y:2023:i:c:p:731-746.

   Full description at Econpapers || Download paper

9. A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment. (2023). Zhao, Zhizhen ; Butala, Mark ; Pesantez, Jorge E ; Lee, Christopher ; Li, Binbin ; Stillwell, Ashlynn S.
   In: Energy.
   RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025367.

   Full description at Econpapers || Download paper

10. A novel decomposition-based approach for non-stationary hub-height wind speed modelling. (2023). Yang, Zihao ; Dong, Sheng.
    In: Energy.
    RePEc:eee:energy:v:283:y:2023:i:c:s0360544223024751.

    Full description at Econpapers || Download paper

11. Carbon price forecasting based on secondary decomposition and feature screening. (2023). Li, Jingmiao ; Liu, Dehong.
    In: Energy.
    RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223011775.

    Full description at Econpapers || Download paper

12. A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction. (2023). Nguyen-Huy, Thong ; Ghimire, Sujan ; Salcedo-Sanz, Sancho ; Deo, Ravinesh C ; Al-Musaylh, Mohanad S ; Casillas-Perez, David.
    In: Energy.
    RePEc:eee:energy:v:275:y:2023:i:c:s0360544223008241.

    Full description at Econpapers || Download paper

13. Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions. (2023). Qin, Yawei ; Liu, Yang ; Wu, Xianguo ; Xu, Wen.
    In: Applied Energy.
    RePEc:eee:appene:v:339:y:2023:i:c:s0306261923002982.

    Full description at Econpapers || Download paper

14. Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence. (2022). Dai, Yeming ; Yang, Xinyu ; Leng, Mingming.
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:182:y:2022:i:c:s0040162522003821.

    Full description at Econpapers || Download paper

15. Ex-post analysis of energy subsidy removal through integrated energy systems modelling. (2022). Mamipour, S ; Ghahremani, M ; Fattahi, M ; Bazilian, M D ; Glynn, J ; Gallachoir, B O ; Aryanpur, V.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:158:y:2022:i:c:s1364032122000454.

    Full description at Econpapers || Download paper

16. Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm. (2022). Ji, Zhengsen ; Gao, Tian ; Sun, Lijie ; Niu, Dongxiao.
    In: Energy.
    RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222022125.

    Full description at Econpapers || Download paper

17. Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting. (2022). Bao, Yukun ; Huang, Yanmei ; Hasan, Najmul ; Deng, Changrui.
    In: Energy.
    RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024932.

    Full description at Econpapers || Download paper

18. Prediction of transportation energy demand: Multivariate Adaptive Regression Splines. (2021). Odur, Muhammed Yasin ; Sahraei, Mohammad Ali ; Duman, Hakan ; Eyduran, Ecevit.
    In: Energy.
    RePEc:eee:energy:v:224:y:2021:i:c:s036054422100339x.

    Full description at Econpapers || Download paper

19. 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.

    Full description at Econpapers || Download paper

20. Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition. (2021). Abdulla, Shahab ; Deo, Ravinesh C ; Raj, Nawin ; Ali, Mumtaz ; Huynh, Anh Ngoc-Lan.
    In: Applied Energy.
    RePEc:eee:appene:v:298:y:2021:i:c:s030626192100619x.

    Full description at Econpapers || Download paper

21. Time series generative adversarial network controller for long-term smart generation control of microgrids. (2021). Yin, Linfei ; Zhang, Bin.
    In: Applied Energy.
    RePEc:eee:appene:v:281:y:2021:i:c:s0306261920314975.

    Full description at Econpapers || Download paper

22. Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms. (2020). Li, Yan ; Deo, Ravinesh C ; Al-Musaylh, Mohanad S.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:9:p:2307-:d:354549.

    Full description at Econpapers || Download paper

23. 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

24. Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network. (2020). Abdulla, Shahab ; Deo, Ravinesh C ; Raj, Nawin ; Ali, Mumtaz ; An-Vo, Duc-Anh ; Huynh, Anh Ngoc-Lan.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:14:p:3517-:d:381918.

    Full description at Econpapers || Download paper

25. Machine learning models to quantify and map daily global solar radiation and photovoltaic power. (2020). Feng, YU ; Hao, Weiping ; Cui, Ningbo ; Gao, Lili ; Li, Haoru ; Gong, Daozhi.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:118:y:2020:i:c:s136403211930601x.

    Full description at Econpapers || Download paper

26. 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

27. Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting. (2020). Niu, Xinsong ; Zhang, Wenyu ; Wang, Jianzhou.
    In: Applied Energy.
    RePEc:eee:appene:v:277:y:2020:i:c:s0306261920310734.

    Full description at Econpapers || Download paper

28. A novel composite electricity demand forecasting framework by data processing and optimized support vector machine. (2020). Gao, Yuyang ; Li, Ranran ; Jiang, Ping ; Liu, Ningning.
    In: Applied Energy.
    RePEc:eee:appene:v:260:y:2020:i:c:s0306261919319300.

    Full description at Econpapers || Download paper

29. A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices. (2019). Qian, Zijie ; Zhou, Yingrui ; Li, Taiyong ; Shi, Jiayi.
    In: Complexity.
    RePEc:hin:complx:4392785.

    Full description at Econpapers || Download paper

30. A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization. (2019). Gao, Yuyang ; Jin, YU ; Liu, Tongxiang.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:8:p:1520-:d:225002.

    Full description at Econpapers || Download paper

31. Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm. (2019). Kim, Sung Won ; Alaghmand, Sina ; Maleki, Niloofar ; Rezaie-Balf, Mohammad ; Chung, Il-Moon ; Babaie-Miri, Fatemeh ; Ashrafian, Ali.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:8:p:1416-:d:222304.

    Full description at Econpapers || Download paper

32. A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction. (2019). Zhang, Yiyi ; Lu, Mingzhen ; Huang, Jia ; Li, Bin.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:20:p:3820-:d:274717.

    Full description at Econpapers || Download paper

33. Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction. (2019). Ghimire, Sujan ; Deo, Ravinesh C ; Raj, Nawin.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:12:p:2407-:d:242248.

    Full description at Econpapers || Download paper

34. Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. (2019). Li, Yan ; Deo, Ravinesh C ; Al-Musaylh, Mohanad S ; Adamowski, Jan F.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:113:y:2019:i:c:47.

    Full description at Econpapers || Download paper

35. Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction. (2019). Ghimire, Sujan ; Deo, Ravinesh C ; Raj, Nawin.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:113:y:2019:i:c:12.

    Full description at Econpapers || Download paper

36. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition. (2019). Prasad, Ramendra ; Ali, Mumtaz.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:104:y:2019:i:c:p:281-295.

    Full description at Econpapers || Download paper

37. Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach. (2019). Deo, Ravinesh C ; Ahin, Mehmet ; Mi, Jianchun ; Adamowski, Jan F.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:104:y:2019:i:c:p:235-261.

    Full description at Econpapers || Download paper

38. Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. (2019). Ghimire, Sujan ; Deo, Ravinesh C ; Raj, Nawin.
    In: Applied Energy.
    RePEc:eee:appene:v:253:y:2019:i:c:5.

    Full description at Econpapers || Download paper

39. Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods. (2019). Zhou, Xiaoming ; Ge, Weichun ; Yu, Renzhe ; Yang, Junyou ; Cui, Jia ; Zhao, Dongbo.
    In: Applied Energy.
    RePEc:eee:appene:v:247:y:2019:i:c:p:480-491.

    Full description at Econpapers || Download paper

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