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Short-Term Electricity Demand Forecasting Using Components Estimation Technique

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  1. Sibonelo Motepe & Ali N. Hasan & Thokozani Shongwe, 2022. "Forecasting the Total South African Unplanned Capability Loss Factor Using an Ensemble of Deep Learning Techniques," Energies, MDPI, vol. 15(7), pages 1-18, March.
  2. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
  3. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
  4. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
  5. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
  6. Yi Yang & Zhihao Shang & Yao Chen & Yanhua Chen, 2020. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting," Energies, MDPI, vol. 13(3), pages 1-19, January.
  7. Antonio Gabaldón & María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez, 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives," Energies, MDPI, vol. 15(24), pages 1-5, December.
  8. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
  9. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
  10. Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024. "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 186-194, July.
  11. Muhammad Ahmar & Fahad Ali & Yuexiang Jiang & Mamdooh Alwetaishi & Sherif S. M. Ghoneim, 2022. "Households’ Energy Choices in Rural Pakistan," Energies, MDPI, vol. 15(9), pages 1-23, April.
  12. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
  13. Chatum Sankalpa & Somsak Kittipiyakul & Seksan Laitrakun, 2022. "Forecasting Short-Term Electricity Load Using Validated Ensemble Learning," Energies, MDPI, vol. 15(22), pages 1-30, November.
  14. Hasnain Iftikhar & Nadeela Bibi & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan," Energies, MDPI, vol. 16(6), pages 1-17, March.
  15. Hasnain Iftikhar & Murad Khan & Zardad Khan & Faridoon Khan & Huda M Alshanbari & Zubair Ahmad, 2023. "A Comparative Analysis of Machine Learning Models: A Case Study in Predicting Chronic Kidney Disease," Sustainability, MDPI, vol. 15(3), pages 1-13, February.
  16. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  17. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
  18. Jha, Amit Prakash & Mahajan, Aarushi & Singh, Sanjay Kumar & Kumar, Piyush, 2022. "Renewable energy proliferation for sustainable development: Role of cross-border electricity trade," Renewable Energy, Elsevier, vol. 201(P1), pages 1189-1199.
  19. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
  20. Yunsun Kim & Sahm Kim, 2021. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
  21. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
  22. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
  23. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
  24. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.
  25. Maciej Slowik & Wieslaw Urban, 2022. "Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant," Energies, MDPI, vol. 15(9), pages 1-16, May.
  26. Roozbeh Sadeghian Broujeny & Safa Ben Ayed & Mouadh Matalah, 2023. "Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling," Energies, MDPI, vol. 16(10), pages 1-21, May.
  27. Ali K k & Erg n Y kseltan & Mustafa Hekimo lu & Esra Agca Aktunc & Ahmet Y cekaya & Ay e Bilge, 2022. "Forecasting Hourly Electricity Demand Under COVID-19 Restrictions," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 73-85.
  28. Fang Guo & Shangyun Deng & Weijia Zheng & An Wen & Jinfeng Du & Guangshan Huang & Ruiyang Wang, 2022. "Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM," Energies, MDPI, vol. 15(22), pages 1-20, November.
  29. Nazila Pourhaji & Mohammad Asadpour & Ali Ahmadian & Ali Elkamel, 2022. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
  30. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
  31. Nahid Sultana & S. M. Zakir Hossain & Salma Hamad Almuhaini & Dilek Düştegör, 2022. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand," Energies, MDPI, vol. 15(9), pages 1-26, May.
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