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An optimized grey model for annual power load forecasting

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Cited by:

  1. Gazi Murat Duman & Elif Kongar, 2023. "ESG Modeling and Prediction Uncertainty of Electronic Waste," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
  2. Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
  3. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
  4. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
  5. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
  6. Yang, Tao & Pan, Yiqun & Yang, Yikun & Lin, Meishun & Qin, Bingyue & Xu, Peng & Huang, Zhizhong, 2017. "CO2 emissions in China's building sector through 2050: A scenario analysis based on a bottom-up model," Energy, Elsevier, vol. 128(C), pages 208-223.
  7. Jin-peng Liu & Chang-ling Li, 2017. "The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
  8. Alexandra L’Heureux & Katarina Grolinger & Miriam A. M. Capretz, 2022. "Transformer-Based Model for Electrical Load Forecasting," Energies, MDPI, vol. 15(14), pages 1-23, July.
  9. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2016. "Ant Lion Optimization Algorithm for Renewable Distributed Generations," Energy, Elsevier, vol. 116(P1), pages 445-458.
  10. Hammad Mahmoud A. & Jereb Borut & Rosi Bojan & Dragan Dejan, 2020. "Methods and Models for Electric Load Forecasting: A Comprehensive Review," Logistics, Supply Chain, Sustainability and Global Challenges, Sciendo, vol. 11(1), pages 51-76, February.
  11. Xie, Xuemei & Liu, Xiaojie & Blanco, Cristina, 2023. "Evaluating and forecasting the niche fitness of regional innovation ecosystems: A comparative evaluation of different optimized grey models," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
  12. 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.
  13. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
  14. Haoran Zhao & Sen Guo, 2021. "Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 9(14), pages 1-32, July.
  15. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
  16. Peng Jiang & Yi-Chung Hu & Wenbao Wang & Hang Jiang & Geng Wu, 2020. "Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting," Mathematics, MDPI, vol. 8(6), pages 1-12, June.
  17. Guo-Feng Fan & Li-Ling Peng & Xiangjun Zhao & Wei-Chiang Hong, 2017. "Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model," Energies, MDPI, vol. 10(11), pages 1-22, October.
  18. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
  19. Xie, Wanli & Wu, Wen-Ze & Liu, Chong & Zhao, Jingjie, 2020. "Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction," Energy, Elsevier, vol. 202(C).
  20. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
  21. Dadkhah, Mojtaba & Jahangoshai Rezaee, Mustafa & Zare Chavoshi, Ahmad, 2018. "Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed," Energy, Elsevier, vol. 148(C), pages 775-788.
  22. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
  23. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
  24. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
  25. Qiang Xiao & Hongshuang Wang, 2022. "Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(11), pages 1-14, June.
  26. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).
  27. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
  28. Minglu Ma & Zhuangzhuang Wang, 2019. "Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models," Energies, MDPI, vol. 13(1), pages 1-15, December.
  29. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
  30. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
  31. Shengzeng Li & Yiwen Zhong & Jiaxiang Lin, 2022. "AWS-DAIE: Incremental Ensemble Short-Term Electricity Load Forecasting Based on Sample Domain Adaptation," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
  32. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
  33. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
  34. de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).
  35. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
  36. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
  37. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
  38. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
  39. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
  40. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  41. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.
  42. 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.
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