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Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

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  1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
  2. Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
  3. Ghofrani, M. & Ghayekhloo, M. & Arabali, A. & Ghayekhloo, A., 2015. "A hybrid short-term load forecasting with a new input selection framework," Energy, Elsevier, vol. 81(C), pages 777-786.
  4. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
  5. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
  6. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
  7. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
  8. Mohamed El-Hendawi & Hossam A. Gabbar & Gaber El-Saady & El-Nobi A. Ibrahim, 2018. "Control and EMS of a Grid-Connected Microgrid with Economical Analysis," Energies, MDPI, vol. 11(1), pages 1-20, January.
  9. Baris Yuce & Monjur Mourshed & Yacine Rezgui, 2017. "A Smart Forecasting Approach to District Energy Management," Energies, MDPI, vol. 10(8), pages 1-22, July.
  10. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Reis, Agnaldo J.R. & Enayatifar, Rasul & Souza, Marcone J.F. & Guimarães, Frederico G., 2016. "A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment," Applied Energy, Elsevier, vol. 169(C), pages 567-584.
  11. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
  12. Ricardo Vazquez & Hortensia Amaris & Monica Alonso & Gregorio Lopez & Jose Ignacio Moreno & Daniel Olmeda & Javier Coca, 2017. "Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project," Energies, MDPI, vol. 10(2), pages 1-23, February.
  13. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
  14. Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
  15. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
  16. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
  17. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
  18. Stefan Arens & Karen Derendorf & Frank Schuldt & Karsten Von Maydell & Carsten Agert, 2018. "Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household," Energies, MDPI, vol. 11(11), pages 1-19, October.
  19. Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019. "Energy saving estimation for plug and lighting load using occupancy analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1143-1161.
  20. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a combined model based on multi-objective optimization for electrical load forecasting," Energy, Elsevier, vol. 119(C), pages 1057-1074.
  21. Karimi, M. & Karami, H. & Gholami, M. & Khatibzadehazad, H. & Moslemi, N., 2018. "Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method," Energy, Elsevier, vol. 144(C), pages 928-940.
  22. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
  23. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
  24. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
  25. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
  26. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2019. "Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting," Energies, MDPI, vol. 12(24), pages 1-23, December.
  27. Yoldaş, Yeliz & Önen, Ahmet & Muyeen, S.M. & Vasilakos, Athanasios V. & Alan, İrfan, 2017. "Enhancing smart grid with microgrids: Challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 205-214.
  28. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
  29. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
  30. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
  31. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
  32. Majzoobi, Alireza & Khodaei, Amin, 2017. "Application of microgrids in providing ancillary services to the utility grid," Energy, Elsevier, vol. 123(C), pages 555-563.
  33. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
  34. Anand, M.P. & Golshannavaz, Sajjad & Ongsakul, Weerakorn & Rajapakse, Athula, 2016. "Incorporating short-term topological variations in optimal energy management of MGs considering ancillary services by electric vehicles," Energy, Elsevier, vol. 112(C), pages 241-253.
  35. Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
  36. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
  37. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
  38. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
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