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Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

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  1. Asif Reza Anik & Sanzidur Rahman, 2021. "Commercial Energy Demand Forecasting in Bangladesh," Energies, MDPI, vol. 14(19), pages 1-22, October.
  2. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
  3. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
  4. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
  5. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
  6. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
  7. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
  8. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
  9. Agbessi Akuété Pierre & Salami Adekunlé Akim & Agbosse Kodjovi Semenyo & Birregah Babiga, 2023. "Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches," Energies, MDPI, vol. 16(12), pages 1-12, June.
  10. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
  11. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
  12. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  13. Wai-Ming To & Peter Ka Chun Lee & Tsz-Ming Lai, 2017. "Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong," Energies, MDPI, vol. 10(7), pages 1-16, June.
  14. Abdoulaye Camara & Wang Feixing & Liu Xiuqin, 2016. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(5), pages 231-231, April.
  15. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
  16. Peng Jiang & Jun Dong & Hui Huang, 2019. "Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm," Energies, MDPI, vol. 12(7), pages 1-24, April.
  17. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
  18. Yung Po Tsang & Wai Chi Wong & G. Q. Huang & Chun Ho Wu & Y. H. Kuo & King Lun Choy, 2020. "A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry," Energies, MDPI, vol. 13(15), pages 1-23, July.
  19. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Adamowski, Jan F. & Li, Yan, 2019. "Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
  20. Baris Yuce & Monjur Mourshed & Yacine Rezgui, 2017. "A Smart Forecasting Approach to District Energy Management," Energies, MDPI, vol. 10(8), pages 1-22, July.
  21. David Bienvenido-Huertas & Carlos Rubio-Bellido & Juan Luis Pérez-Ordóñez & Fernando Martínez-Abella, 2019. "Estimating Adaptive Setpoint Temperatures Using Weather Stations," Energies, MDPI, vol. 12(7), pages 1-47, March.
  22. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
  23. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
  24. Nyoni, Thabani, 2019. "Modeling and forecasting demand for electricity in Zimbabwe using the Box-Jenkins ARIMA technique," MPRA Paper 96903, University Library of Munich, Germany.
  25. Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
  26. Yea-Kuang Chan & Jyh-Cherng Gu, 2012. "Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants," Energies, MDPI, vol. 5(1), pages 1-18, January.
  27. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
  28. Uddin, Gazi Salah & Hasan, Md. Bokhtiar & Phoumin, Han & Taghizadeh-Hesary, Farhad & Ahmed, Ali & Troster, Victor, 2023. "Exploring the critical demand drivers of electricity consumption in Thailand," Energy Economics, Elsevier, vol. 125(C).
  29. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
  30. Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
  31. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
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