IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v26y2001i1p1-14.html
   My bibliography  Save this item

Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ming Meng & Dongxiao Niu & Wei Sun, 2011. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction," Energies, MDPI, vol. 4(10), pages 1-13, September.
  2. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
  3. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
  4. Tsai, Bi-Huei & Chang, Chih-Jen & Chang, Chun-Hsien, 2016. "Elucidating the consumption and CO2 emissions of fossil fuels and low-carbon energy in the United States using Lotka–Volterra models," Energy, Elsevier, vol. 100(C), pages 416-424.
  5. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
  6. Ediger, Volkan S. & Akar, Sertac & Ugurlu, Berkin, 2006. "Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model," Energy Policy, Elsevier, vol. 34(18), pages 3836-3846, December.
  7. Tanrisever, Fehmi & Derinkuyu, Kursad & Heeren, Michael, 2013. "Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case," Energy, Elsevier, vol. 58(C), pages 247-257.
  8. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
  9. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
  10. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
  11. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
  12. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
  13. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
  14. Dagher, Leila & Ruble, Isabella, 2011. "Modeling Lebanon’s electricity sector: Alternative scenarios and their implications," Energy, Elsevier, vol. 36(7), pages 4315-4326.
  15. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
  16. Wang, Tai-Yue & Huang, Chien-Yu, 2007. "Improving forecasting performance by employing the Taguchi method," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1052-1065, January.
  17. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
  18. Rallapalli, Srinivasa Rao & Ghosh, Sajal, 2012. "Forecasting monthly peak demand of electricity in India—A critique," Energy Policy, Elsevier, vol. 45(C), pages 516-520.
  19. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
  20. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
  21. T. O. Benli, 2016. "A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey," Papers 1607.05660, arXiv.org.
  22. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
  23. Swasti R. Khuntia & Jose L. Rueda & Mart A.M.M. Van der Meijden, 2018. "Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model," Energies, MDPI, vol. 11(12), pages 1-19, November.
  24. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
  25. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  26. García-Ascanio, Carolina & Maté, Carlos, 2010. "Electric power demand forecasting using interval time series: A comparison between VAR and iMLP," Energy Policy, Elsevier, vol. 38(2), pages 715-725, February.
  27. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
  28. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
  29. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
  30. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
  31. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
  32. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
  33. Sumer, Kutluk Kagan & Goktas, Ozlem & Hepsag, Aycan, 2009. "The application of seasonal latent variable in forecasting electricity demand as an alternative method," Energy Policy, Elsevier, vol. 37(4), pages 1317-1322, April.
  34. Varma, Rashmi & Sushil,, 2019. "Bridging the electricity demand and supply gap using dynamic modeling in the Indian context," Energy Policy, Elsevier, vol. 132(C), pages 515-535.
  35. Khodr, Hiba & Uherova Hasbani, Katarina, 2013. "The dynamics of energy policy in Lebanon when research, politics, and policy fail to intersect," Energy Policy, Elsevier, vol. 60(C), pages 629-642.
  36. Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
  37. Abosedra, Salah & Dah, Abdallah & Ghosh, Sajal, 2009. "Electricity consumption and economic growth, the case of Lebanon," Applied Energy, Elsevier, vol. 86(4), pages 429-432, April.
  38. Fan, Jingjing & Wang, Jianliang & Liu, Mingming & Sun, Wangmin & Lan, Zhixuan, 2022. "Scenario simulations of China's natural gas consumption under the dual-carbon target," Energy, Elsevier, vol. 252(C).
  39. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
  40. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  41. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
  42. Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
  43. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
  44. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
  45. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
  46. Xiao, Liye & Wang, Jianzhou & Hou, Ru & Wu, Jie, 2015. "A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting," Energy, Elsevier, vol. 82(C), pages 524-549.
  47. Saab, Samer S. & Zouein, Pierrette P., 2001. "Forecasting passenger load for a fixed planning horizon," Journal of Air Transport Management, Elsevier, vol. 7(6), pages 361-372.
  48. Badurally Adam, N.R. & Elahee, M.K. & Dauhoo, M.Z., 2011. "Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process," Energy, Elsevier, vol. 36(12), pages 6763-6769.
  49. Bismark Ameyaw & Li Yao, 2018. "Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique," Sustainability, MDPI, vol. 10(7), pages 1-20, July.
  50. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
  51. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P. & Bouzerdoum, A., 2017. "Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment," Applied Energy, Elsevier, vol. 205(C), pages 790-801.
  52. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.
  53. Liu, Yongge & Hou, Jian & Zhao, Haifeng & Liu, Xiaoyu & Xia, Zhizeng, 2019. "Numerical simulation of simultaneous exploitation of geothermal energy and natural gas hydrates by water injection into a geothermal heat exchange well," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 467-481.
  54. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
  55. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
  56. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.
  57. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.