create a website

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

Cited: 1

Citations received by this document

Cites: 45

References cited by this document

Cocites: 50

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting. (2025). Yan, Wenqiang ; Cui, Lei ; Zhang, Yuyang.
    In: Energies.
    RePEc:gam:jeners:v:18:y:2025:i:6:p:1365-:d:1609428.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. Ağbulut, Ü. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustain. Prod. Consum. 2022, 29, 141–157. [CrossRef]
    Paper not yet in RePEc: Add citation now
  2. Akay, D.; Atak, M. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy 2007, 32, 1670–1675. [CrossRef]

  3. Al-Musaylh, M.S.; Deo, R.C.; Li, Y.; Adamowski, J.F. 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. [CrossRef]

  4. An, N.; Zhao, W.; Wang, J.; Shang, D.; Zhao, E. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 2013, 49, 279–288. [CrossRef]
    Paper not yet in RePEc: Add citation now
  5. Barman, M.; Choudhury, N.B. A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India. Sustain. Cities Soc. 2020, 61, 102311. [CrossRef]
    Paper not yet in RePEc: Add citation now
  6. Bedi, J.; Toshniwal, D. Deep learning framework to forecast electricity demand. Appl. Energy 2019, 238, 1312–1326. [CrossRef]

  7. Bedi, J.; Toshniwal, D. Empirical mode decomposition based deep learning for electricity demand forecasting. IEEE Access 2018, 6, 49144–49156. [CrossRef]
    Paper not yet in RePEc: Add citation now
  8. Blood, E.A.; Krogh, B.H.; Ilic, M.D. Electric power system static state estimation through Kalman filtering and load forecasting. In Proceedings of the Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008.
    Paper not yet in RePEc: Add citation now
  9. Boudraa, A.O.; Cexus, J.C. EMD-Based Signal Filtering. IEEE Trans. Instrum. Meas. 2007, 56, 2196–2202. [CrossRef]
    Paper not yet in RePEc: Add citation now
  10. Calik, N.; Güneş, F.; Koziel, S.; Pietrenko-Dabrowska, A.; Belen, M.A.; Mahouti, P. Deep-learning-based precise characterization of microwave transistors using fully-automated regression surrogates. Sci. Rep. 2023, 13, 1445. [CrossRef] [PubMed]
    Paper not yet in RePEc: Add citation now
  11. Chang, Y.; Choi, Y.; Kim, C.S.; Miller, J.I.; Park, J.Y. Forecasting regional long-run energy demand: A functional coefficient panel approach. Energy Econ. 2021, 96, 105117. [CrossRef]

  12. Chaturvedi, S.; Rajasekar, E.; Natarajan, S.; McCullen, N. A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. Energy Policy 2022, 168, 113097. [CrossRef] Energies 2024, 17, 4377 16 of 16

  13. Deo, R.C.; Wen, X.; Qi, F. A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl. Energy 2016, 168, 568–593. [CrossRef]

  14. Dluhopolskyi, O.; Kozlovskyi, S.; Popovskyi, Y.; Lutkovska, S.; Butenko, V.; Popovskyi, T.; Mazur, H.; Kozlovskyi, A. Formation of the model of sustainable economic development of renewable energy. ECONOMICS-Innov. Econ. Res. J. 2023, 11, 51–78. [CrossRef]
    Paper not yet in RePEc: Add citation now
  15. Gebremeskel, D.H.; Ahlgren, E.O.; Beyene, G.B. Long-term evolution of energy and electricity demand forecasting: The case of Ethiopia. Energy Strategy Rev. 2021, 36, 100671. [CrossRef]
    Paper not yet in RePEc: Add citation now
  16. Hippert, H.S.; Pedreira, C.E.; Souza, R.C. Neural networks for short-term load forecasting: A review and evaluation. IEEE Trans. Power Syst. 2001, 16, 44–55. [CrossRef]
    Paper not yet in RePEc: Add citation now
  17. Homod, R.Z.; Togun, H.; Abd, H.J.; Sahari, K.S. A novel hybrid modelling structure fabricated by using Takagi-Sugeno fuzzy to forecast HVAC systems energy demand in real-time for Basra city. Sustain. Cities Soc. 2020, 56, 102091. [CrossRef]
    Paper not yet in RePEc: Add citation now
  18. Hu, Y.-C. Energy demand forecasting using a novel remnant GM (1, 1) model. Soft Comput. 2020, 24, 13903–13912. [CrossRef]
    Paper not yet in RePEc: Add citation now
  19. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R Soc. Lond. Ser. A Math. Phys. Sci. 1971, 454, 903–995. [CrossRef]
    Paper not yet in RePEc: Add citation now
  20. Karthika, S.; Margaret, V.; Balaraman, K. Hybrid short term load forecasting using ARIMA-SVM. In Proceedings of the Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, 21–22 April 2017.
    Paper not yet in RePEc: Add citation now
  21. Kazemi, M.; Salehpour, S.Y.; Shahbaazy, F.; Behzadpoor, S.; Pirouzi, S.; Jafarpour, S. Participation of energy storage-based flexible hubs in day-ahead reserve regulation and energy markets based on a coordinated energy management strategy. Int. Trans. Electr. Energy Syst. 2022, 2022, 6481531. [CrossRef]
    Paper not yet in RePEc: Add citation now
  22. Kazemzadeh, M.-R.; Amjadian, A.; Amraee, T. A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. Energy 2020, 204, 117948. [CrossRef]
    Paper not yet in RePEc: Add citation now
  23. Khalafian, F.; Iliaee, N.; Diakina, E.; Parsa, P.; Alhaider, M.M.; Masali, M.H.; Pirouzi, S.; Zhu, M. Capabilities of compressed air energy storage in the economic design of renewable off-grid system to supply electricity and heat costumers and smart charging-based electric vehicles. J. Energy Storage 2024, 78, 109888. [CrossRef]
    Paper not yet in RePEc: Add citation now
  24. Ko, C.N.; Lee, C.M. Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 2013, 49, 413–422. [CrossRef]

  25. Kong, W.; Dong, Z.Y.; Jia, Y.; Hill, D.J.; Xu, Y.; Zhang, Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 2017, 10, 841–851. [CrossRef]
    Paper not yet in RePEc: Add citation now
  26. Liang, H.; Pirouzi, S. Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources. Energy 2024, 293, 130745. [CrossRef]
    Paper not yet in RePEc: Add citation now
  27. Liu, Y.; Liao, S. Granularity selection for cross-validation of SVM. Inf. Sci. 2017, 378, 475–483. [CrossRef]
    Paper not yet in RePEc: Add citation now
  28. Liu, Y.; Liao, S. Kernel selection with spectral perturbation stability of kernel matrix. Sci. China Inf. Sci. 2014, 57, 1–10. [CrossRef]
    Paper not yet in RePEc: Add citation now
  29. Maaouane, M.; Chennaif, M.; Zouggar, S.; Krajačić, G.; Duić, N.; Zahboune, H.; ElMiad, A.K. Using neural network modelling for estimation and forecasting of transport sector energy demand in developing countries. Energy Convers. Manag. 2022, 258, 115556. [CrossRef]
    Paper not yet in RePEc: Add citation now
  30. Norouzi, M.; Aghaei, J.; Niknam, T.; Pirouzi, S.; Lehtonen, M. Bi-level fuzzy stochastic-robust model for flexibility valorizing of renewable networked microgrids. Sustain. Energy Grids Netw. 2022, 31, 100684. [CrossRef]
    Paper not yet in RePEc: Add citation now
  31. Norouzi, M.; Aghaei, J.; Pirouzi, S.; Niknam, T.; Fotuhi-Firuzabad, M. Flexibility pricing of integrated unit of electric spring and EVs parking in microgrids. Energy 2022, 239, 122080. [CrossRef]
    Paper not yet in RePEc: Add citation now
  32. Patrizi, N.; LaTouf, S.K.; Tsiropoulou, E.E.; Papavassiliou, S. Prosumer-centric self-sustained smart grid systems. IEEE Syst. J. 2022, 16, 6042–6053.
    Paper not yet in RePEc: Add citation now
  33. Peng, J.; Kimmig, A.; Niu, Z.; Wang, J.; Liu, X.; Ovtcharova, J. A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework. Appl. Energy 2021, 299, 117321. [CrossRef]

  34. Pirouzi, S. Transmission and Distribution, Network-constrained unit commitment-based virtual power plant model in the day-ahead market according to energy management strategy. IET Gener. Transm. Distrib. 2023, 17, 4958–4974. [CrossRef]
    Paper not yet in RePEc: Add citation now
  35. Qiu, X.; Ren, Y.; Suganthan, P.N.; Amaratunga, G.A. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 2017, 54, 246–255. [CrossRef]
    Paper not yet in RePEc: Add citation now
  36. Qu, Z.; Xu, C.; Yang, F.; Ling, F.; Pirouzi, S. Market clearing price-based energy management of grid-connected renewable energy hubs including flexible sources according to thermal, hydrogen, and compressed air storage systems. J. Energy Storage 2023, 69,
    Paper not yet in RePEc: Add citation now
  37. Rao, C.; Zhang, Y.; Wen, J.; Xiao, X.; Goh, M. Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model. Energy 2023, 263, 125955. [CrossRef]

  38. Sahabuddin, M.; Khan, I. Analysis of demand, generation, and emission for long-term sustainable power system planning using LEAP: The case of Bangladesh. J. Renew. Sustain. Energy 2023, 15, 035503. [CrossRef] Energies 2024, 17, 4377 15 of 16
    Paper not yet in RePEc: Add citation now
  39. Shao, Z.; Chao, F.; Yang, S.L.; Zhou, K.L. A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting. Renew. Sustain. Energy Rev. 2016, 75, 123–136. [CrossRef]
    Paper not yet in RePEc: Add citation now
  40. Takeda, H.; Tamura, Y.; Sato, S. Using the ensemble Kalman filter for electricity load forecasting and analysis. Energy 2016, 104, 184–198. [CrossRef]

  41. Wu, C.H.; Tzeng, G.H.; Goo, Y.J.; Fang, W.C. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst. Appl. 2007, 32, 397–408. [CrossRef]
    Paper not yet in RePEc: Add citation now
  42. Xiong, T.; Bao, Y.; Hu, Z. Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework. Int. J. Electr. Power Energy Syst. 2014, 63, 353–362. [CrossRef]
    Paper not yet in RePEc: Add citation now
  43. Xu, G.; Wang, W. Forecasting China′s natural gas consumption based on a combination model. J. Nat. Gas Chem. 2010, 19, 493–496. [CrossRef]
    Paper not yet in RePEc: Add citation now
  44. Zhang, X.; Yu, X.; Ye, X.; Pirouzi, S. Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method. Energy 2023, 278, 128054. [CrossRef]

  45. Zhu, S.; Wang, J.; Zhao, W.; Wang, J. A seasonal hybrid procedure for electricity demand forecasting in China. Appl. Energy 2011, 88, 3807–3815. [CrossRef]

Cocites

Documents in RePEc which have cited the same bibliography

  1. China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model. (2016). Qin, Shanshan ; Zhou, Qingping ; Wu, Jie ; Wang, Jianzhou ; Jiang, Haiyan.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:53:y:2016:i:c:p:1149-1167.

    Full description at Econpapers || Download paper

  2. Continuous fractional-order grey model and electricity prediction research based on the observation error feedback. (2016). Yang, Yang ; Xue, Dingyu.
    In: Energy.
    RePEc:eee:energy:v:115:y:2016:i:p1:p:722-733.

    Full description at Econpapers || Download paper

  3. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors. (2016). Liu, Xiuli ; Moreno, Blanca ; Garcia, Ana Salome.
    In: Energy.
    RePEc:eee:energy:v:115:y:2016:i:p1:p:1042-1054.

    Full description at Econpapers || Download paper

  4. A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation. (2016). Yu, Ziqiang ; Weng, Shilie ; Zhang, Huisheng ; Zhou, Dengji.
    In: Energy.
    RePEc:eee:energy:v:109:y:2016:i:c:p:420-429.

    Full description at Econpapers || Download paper

  5. An optimized grey model for annual power load forecasting. (2016). Guo, Sen ; Zhao, Huiru.
    In: Energy.
    RePEc:eee:energy:v:107:y:2016:i:c:p:272-286.

    Full description at Econpapers || Download paper

  6. Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. (2016). Gunay, Merdem .
    In: Energy Policy.
    RePEc:eee:enepol:v:90:y:2016:i:c:p:92-101.

    Full description at Econpapers || Download paper

  7. Dynamic Pricing of Electricity: A Survey of Related Research. (2015). Mitra, Krishnendranath ; Dutta, Goutam.
    In: IIMA Working Papers.
    RePEc:iim:iimawp:13724.

    Full description at Econpapers || Download paper

  8. A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. (2015). Tutun, Salih ; Caniyilmaz, Erdal ; Chou, Chun-An.
    In: Energy.
    RePEc:eee:energy:v:93:y:2015:i:p2:p:2406-2422.

    Full description at Econpapers || Download paper

  9. Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey. (2015). Ozkara, Yucel ; Atak, Mehmet.
    In: Energy.
    RePEc:eee:energy:v:93:y:2015:i:p1:p:495-510.

    Full description at Econpapers || Download paper

  10. Shelter and residential building energy consumption within the 450 ppm CO2eq constraints in different climate zones. (2015). Bohne, Rolf Andre ; Huang, Lizhen ; Lohne, Jardar.
    In: Energy.
    RePEc:eee:energy:v:90:y:2015:i:p1:p:965-979.

    Full description at Econpapers || Download paper

  11. Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China. (2015). Liu, Youzhu ; Dai, Yongwu ; Xu, Weijun ; Gu, Ren .
    In: Economic Modelling.
    RePEc:eee:ecmode:v:45:y:2015:i:c:p:127-135.

    Full description at Econpapers || Download paper

  12. Forecasting energy consumption in China following instigation of an energy-saving policy. (2014). Xie, Naiming ; Pearman, Alan.
    In: Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards.
    RePEc:spr:nathaz:v:74:y:2014:i:2:p:639-659.

    Full description at Econpapers || Download paper

  13. Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections. (2014). Aydin, Gokhan.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:35:y:2014:i:c:p:382-389.

    Full description at Econpapers || Download paper

  14. Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. (2014). Reisel, John R. ; Kialashaki, Arash .
    In: Energy.
    RePEc:eee:energy:v:76:y:2014:i:c:p:749-760.

    Full description at Econpapers || Download paper

  15. Forecasting the annual electricity consumption of Turkey using an optimized grey model. (2014). Es, Huseyin Avni ; Hamzacebi, Coskun.
    In: Energy.
    RePEc:eee:energy:v:70:y:2014:i:c:p:165-171.

    Full description at Econpapers || Download paper

  16. Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach. (2014). Ozturk, Ilhan ; Arisoy, Ibrahim.
    In: Energy.
    RePEc:eee:energy:v:66:y:2014:i:c:p:959-964.

    Full description at Econpapers || Download paper

  17. Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. (2014). Ardehali, M. M. ; Ardakani, F. J..
    In: Energy.
    RePEc:eee:energy:v:65:y:2014:i:c:p:452-461.

    Full description at Econpapers || Download paper

  18. Scenario analysis of nonresidential natural gas consumption in Italy. (2014). Bianco, Vincenzo ; Tagliafico, Luca A. ; Scarpa, Federico.
    In: Applied Energy.
    RePEc:eee:appene:v:113:y:2014:i:c:p:392-403.

    Full description at Econpapers || Download paper

  19. A genetic algorithm-based grey method for forecasting food demand after snow disasters: an empirical study. (2013). Wang, Zheng-Xin.
    In: Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards.
    RePEc:spr:nathaz:v:68:y:2013:i:2:p:675-686.

    Full description at Econpapers || Download paper

  20. Vision 2023: Forecasting Turkeys natural gas demand between 2013 and 2030. (2013). Melikoglu, Mehmet.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:22:y:2013:i:c:p:393-400.

    Full description at Econpapers || Download paper

  21. Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm. (2013). Zhu, Xiaoxi ; Ma, Weimin ; Wang, Miaomiao.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:38:y:2013:i:4:p:613-620.

    Full description at Econpapers || Download paper

  22. Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case. (2013). Heeren, Michael ; Derinkuyu, Kursad ; Tanrisever, Fehmi.
    In: Energy.
    RePEc:eee:energy:v:58:y:2013:i:c:p:247-257.

    Full description at Econpapers || Download paper

  23. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. (2013). Zhao, Weigang ; An, Ning ; Wang, Jianzhou ; Shang, Duo.
    In: Energy.
    RePEc:eee:energy:v:49:y:2013:i:c:p:279-288.

    Full description at Econpapers || Download paper

  24. Prediction of CO2 Emissions in Iran using Grey and ARIMA Models. (2013). Lotfalipour, Mohammad ; Bastam, Morteza ; Falahi, Mohammad Ali.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2013-03-4.

    Full description at Econpapers || Download paper

  25. Energy models for demand forecasting—A review. (2012). Suganthi, L. ; Samuel, Anand A..
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:16:y:2012:i:2:p:1223-1240.

    Full description at Econpapers || Download paper

  26. Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case. (2012). Chen, Wen-Chih ; Li, Der-Chiang ; Chang, Che-Jung.
    In: Omega.
    RePEc:eee:jomega:v:40:y:2012:i:6:p:767-773.

    Full description at Econpapers || Download paper

  27. Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China. (2012). Su, Zhongyue ; Zhao, ZE ; Wang, Jianzhou.
    In: Omega.
    RePEc:eee:jomega:v:40:y:2012:i:5:p:525-532.

    Full description at Econpapers || Download paper

  28. An optimal hybrid model for atomic power generation prediction in Japan. (2012). Masuda, Shiro ; Nagai, Masatake ; Li, Guo-Dong.
    In: Energy.
    RePEc:eee:energy:v:45:y:2012:i:1:p:655-661.

    Full description at Econpapers || Download paper

  29. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. (2012). Fu, Hsin-Chia ; Pao, Hsiao-Tien ; Tseng, Cheng-Lung .
    In: Energy.
    RePEc:eee:energy:v:40:y:2012:i:1:p:400-409.

    Full description at Econpapers || Download paper

  30. A hybrid procedure for energy demand forecasting in China. (2012). Yu, Shi-Wei ; Zhu, Ke-Jun .
    In: Energy.
    RePEc:eee:energy:v:37:y:2012:i:1:p:396-404.

    Full description at Econpapers || Download paper

  31. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China. (2012). Wang, Yuanyuan ; Dong, Yao ; Zhao, GE.
    In: Energy Policy.
    RePEc:eee:enepol:v:48:y:2012:i:c:p:284-294.

    Full description at Econpapers || Download paper

  32. A PSO–GA optimal model to estimate primary energy demand of China. (2012). YU, Shiwei ; Wei, Yi-Ming ; Wang, Ke.
    In: Energy Policy.
    RePEc:eee:enepol:v:42:y:2012:i:c:p:329-340.

    Full description at Econpapers || Download paper

  33. Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. (2012). Lee, Yi-Shian ; Tong, Lee-Ing .
    In: Applied Energy.
    RePEc:eee:appene:v:94:y:2012:i:c:p:251-256.

    Full description at Econpapers || Download paper

  34. Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm). (2011). Assari, M. R. ; Ghalambaz, M. ; Behrang, M. A. ; Assareh, E. ; Noghrehabadi, A. R..
    In: Energy.
    RePEc:eee:energy:v:36:y:2011:i:9:p:5649-5654.

    Full description at Econpapers || Download paper

  35. Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. (2011). Pao, Hsiao-Tien ; Tsai, Chung-Ming.
    In: Energy.
    RePEc:eee:energy:v:36:y:2011:i:5:p:2450-2458.

    Full description at Econpapers || Download paper

  36. Turkish aggregate electricity demand: An outlook to 2020. (2011). Hunt, Lester ; Dilaver, Zafer.
    In: Energy.
    RePEc:eee:energy:v:36:y:2011:i:11:p:6686-6696.

    Full description at Econpapers || Download paper

  37. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. (2011). Yu, Lean ; Wang, Shuai ; Tang, Ling.
    In: Energy.
    RePEc:eee:energy:v:36:y:2011:i:11:p:6542-6554.

    Full description at Econpapers || Download paper

  38. Prospects of Indias energy and emissions for a long time frame. (2011). Rout, Ullash K..
    In: Energy Policy.
    RePEc:eee:enepol:v:39:y:2011:i:9:p:5647-5663.

    Full description at Econpapers || Download paper

  39. Modeling and forecasting of Turkeys energy consumption using socio-economic and demographic variables. (2011). zsahin, Talat Sukru ; Kankal, Murat ; Komurcu, Murat Ihsan ; Akpinar, Adem.
    In: Applied Energy.
    RePEc:eee:appene:v:88:y:2011:i:5:p:1927-1939.

    Full description at Econpapers || Download paper

  40. Modeling and prediction of Turkeys electricity consumption using Support Vector Regression. (2011). Kavaklioglu, Kadir .
    In: Applied Energy.
    RePEc:eee:appene:v:88:y:2011:i:1:p:368-375.

    Full description at Econpapers || Download paper

  41. A seasonal hybrid procedure for electricity demand forecasting in China. (2011). Zhao, Weigang ; Zhu, Suling ; Wang, Jujie.
    In: Applied Energy.
    RePEc:eee:appene:v:88:y:2011:i:11:p:3807-3815.

    Full description at Econpapers || Download paper

  42. Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. (2010). Jain, V. K. ; Kumar, Ujjwal.
    In: Energy.
    RePEc:eee:energy:v:35:y:2010:i:4:p:1709-1716.

    Full description at Econpapers || Download paper

  43. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. (2010). Assari, M. R. ; Behrang, M. A. ; Assareh, E. ; Ghanbarzadeh, A..
    In: Energy.
    RePEc:eee:energy:v:35:y:2010:i:12:p:5223-5229.

    Full description at Econpapers || Download paper

  44. Turkeys short-term gross annual electricity demand forecast by fuzzy logic approach. (2010). Kucukali, Serhat ; Baris, Kemal .
    In: Energy Policy.
    RePEc:eee:enepol:v:38:y:2010:i:5:p:2438-2445.

    Full description at Econpapers || Download paper

  45. Natural gas demand in Turkey. (2010). Erdoğdu, Erkan.
    In: Applied Energy.
    RePEc:eee:appene:v:87:y:2010:i:1:p:211-219.

    Full description at Econpapers || Download paper

  46. Analysis and forecasting of nonresidential electricity consumption in Romania. (2010). Bianco, Vincenzo ; Nardini, Sergio ; Manca, Oronzio ; Minea, Alina A..
    In: Applied Energy.
    RePEc:eee:appene:v:87:y:2010:i:11:p:3584-3590.

    Full description at Econpapers || Download paper

  47. Natural gas demand in Turkey. (2009). Erdoğdu, Erkan.
    In: MPRA Paper.
    RePEc:pra:mprapa:19091.

    Full description at Econpapers || Download paper

  48. Forecast of electricity consumption and economic growth in Taiwan by state space modeling. (2009). Pao, Hsiao-Tien .
    In: Energy.
    RePEc:eee:energy:v:34:y:2009:i:11:p:1779-1791.

    Full description at Econpapers || Download paper

  49. Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey. (2008). Haldenbilen, Soner ; Baskan, Ozgur ; Ceylan, Huseyin.
    In: Energy Policy.
    RePEc:eee:enepol:v:36:y:2008:i:7:p:2527-2535.

    Full description at Econpapers || Download paper

  50. Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. (2008). nler, Alper.
    In: Energy Policy.
    RePEc:eee:enepol:v:36:y:2008:i:6:p:1937-1944.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-10-06 01:52:22 || Missing content? Let us know

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