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A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management

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Cited by:

  1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  2. Lago, Jesus & De Ridder, Fjo & Vrancx, Peter & De Schutter, Bart, 2018. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, Elsevier, vol. 211(C), pages 890-903.
  3. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
  4. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
  5. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2016. "State estimation of medium voltage distribution networks using smart meter measurements," Applied Energy, Elsevier, vol. 184(C), pages 207-218.
  6. Jin, Xiaolong & Wu, Jianzhong & Mu, Yunfei & Wang, Mingshen & Xu, Xiandong & Jia, Hongjie, 2017. "Hierarchical microgrid energy management in an office building," Applied Energy, Elsevier, vol. 208(C), pages 480-494.
  7. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2018. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system," Renewable Energy, Elsevier, vol. 122(C), pages 533-550.
  8. Aqdas Naz & Muhammad Umar Javed & Nadeem Javaid & Tanzila Saba & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids," Energies, MDPI, vol. 12(5), pages 1-30, March.
  9. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
  10. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
  11. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
  12. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
  13. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
  14. Wang, Jian & Yang, Zhongshan, 2021. "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, Elsevier, vol. 171(C), pages 1418-1435.
  15. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
  16. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
  17. Laurentiu-Mihai Ionescu & Nicu Bizon & Alin-Gheorghita Mazare & Nadia Belu, 2020. "Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy," Mathematics, MDPI, vol. 8(7), pages 1-26, July.
  18. 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.
  19. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
  20. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
  21. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
  22. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
  23. Vasudharini Sridharan & Mingjian Tuo & Xingpeng Li, 2022. "Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model," Energies, MDPI, vol. 15(20), pages 1-16, October.
  24. Mehdi Khashei & Zahra Hajirahimi, 2017. "Performance evaluation of series and parallel strategies for financial time series forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-24, December.
  25. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
  26. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
  27. Mbungu, Nsilulu T. & Bansal, Ramesh C. & Naidoo, Raj M. & Bettayeb, Maamar & Siti, Mukwanga W. & Bipath, Minnesh, 2020. "A dynamic energy management system using smart metering," Applied Energy, Elsevier, vol. 280(C).
  28. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
  29. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
  30. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
  31. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
  32. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
  33. Ghasemi-Marzbali, Ali, 2020. "Multi-area multi-source automatic generation control in deregulated power system," Energy, Elsevier, vol. 201(C).
  34. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
  35. Cui, Jia & Yu, Renzhe & Zhao, Dongbo & Yang, Junyou & Ge, Weichun & Zhou, Xiaoming, 2019. "Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods," Applied Energy, Elsevier, vol. 247(C), pages 480-491.
  36. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
  37. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.
  38. Lee, Eunjung & Lee, Kyungeun & Lee, Hyoseop & Kim, Euncheol & Rhee, Wonjong, 2019. "Defining virtual control group to improve customer baseline load calculation of residential demand response," Applied Energy, Elsevier, vol. 250(C), pages 946-958.
  39. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
  40. Cui, Qiong & Ma, Peipei & Huang, Lei & Shu, Jie & Luv, Jie & Lu, Lin, 2020. "Effect of device models on the multiobjective optimal operation of CCHP microgrids considering shiftable loads," Applied Energy, Elsevier, vol. 275(C).
  41. 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.
  42. Pedro Bento & Hugo Nunes & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast Scheme," Energies, MDPI, vol. 12(5), pages 1-25, March.
  43. 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.
  44. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
  45. Sajawal ur Rehman Khan & Israa Adil Hayder & Muhammad Asif Habib & Mudassar Ahmad & Syed Muhammad Mohsin & Farrukh Aslam Khan & Kainat Mustafa, 2022. "Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids," Energies, MDPI, vol. 16(1), pages 1-16, December.
  46. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez & Pedro Conde, 2016. "Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market," Energies, MDPI, vol. 9(9), pages 1-24, September.
  47. Muqaddas Naz & Zafar Iqbal & Nadeem Javaid & Zahoor Ali Khan & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2018. "Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(2), pages 1-25, February.
  48. Muruganantham, B. & Gnanadass, R. & Padhy, N.P., 2017. "Challenges with renewable energy sources and storage in practical distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 125-134.
  49. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
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