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A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

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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. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
  3. Obara, Shin’ya & Utsugi, Yuta & Ito, Yuzi & Morel, Jorge & Okada, Masaki, 2015. "A study on planning for interconnected renewable energy facilities in Hokkaido, Japan," Applied Energy, Elsevier, vol. 146(C), pages 313-327.
  4. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
  5. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
  6. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
  7. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
  8. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
  9. Zhang Yue & Arash Farnoosh, 2018. "Analysing the Dynamic Impact of Electricity Futures on Revenue and Risks of Renewable Energy in China," Working Papers hal-03188814, HAL.
  10. Zhou, Huimin & Dang, Yaoguo & Yang, Yingjie & Wang, Junjie & Yang, Shaowen, 2023. "An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles," Energy, Elsevier, vol. 263(PC).
  11. Jianfang Cao & Hongyan Cui & Hao Shi & Lijuan Jiao, 2016. "Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
  12. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
  13. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
  14. Ma, Teng & Li, Ming-Jia & Xu, Jin-Liang & Cao, Feng, 2019. "Thermodynamic analysis and performance prediction on dynamic response characteristic of PCHE in 1000 MW S-CO2 coal fired power plant," Energy, Elsevier, vol. 175(C), pages 123-138.
  15. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
  16. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
  17. Jinying Li & Jianfeng Shi & Jinchao Li, 2016. "Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China," Energies, MDPI, vol. 9(8), pages 1-17, August.
  18. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
  19. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
  20. Hongxia Ge & Siteng Li & Rongjun Cheng & Zhenlei Chen, 2022. "Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
  21. Yaguang Kong & Chenfeng Xie & Song Zheng & Peng Jiang & Meng Guan & Fang Wang, 2019. "Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park," Complexity, Hindawi, vol. 2019, pages 1-18, May.
  22. Liu, Guixian & Dong, Xiucheng & Jiang, Qingzhe & Dong, Cong & Li, Jiaman, 2018. "Natural gas consumption of urban households in China and corresponding influencing factors," Energy Policy, Elsevier, vol. 122(C), pages 17-26.
  23. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
  24. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
  25. Yang Changwei & Li Zonghao & Guo Xueyan & Yu Wenying & Jin Jing & Zhu Liang, 2019. "Application of BP Neural Network Model in Risk Evaluation of Railway Construction," Complexity, Hindawi, vol. 2019, pages 1-12, June.
  26. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
  27. Deyun Wang & Yanling Liu & Hongyuan Luo & Chenqiang Yue & Sheng Cheng, 2017. "Day-Ahead PM 2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution," IJERPH, MDPI, vol. 14(7), pages 1-22, July.
  28. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
  29. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
  30. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
  31. Wu, Zhuochun & Zhao, Xiaochen & Ma, Yuqing & Zhao, Xinyan, 2019. "A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting," Applied Energy, Elsevier, vol. 237(C), pages 896-909.
  32. Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2020. "Application of Artificial Neural Networks for Natural Gas Consumption Forecasting," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
  33. Zhou, Weijie & Wu, Xiaoli & Ding, Song & Pan, Jiao, 2020. "Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China," Energy, Elsevier, vol. 200(C).
  34. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
  35. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
  36. Yu, Weichao & Huang, Weihe & Wen, Yunhao & Li, Yichen & Liu, Hongfei & Wen, Kai & Gong, Jing & Lu, Yanan, 2021. "An integrated gas supply reliability evaluation method of the large-scale and complex natural gas pipeline network based on demand-side analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
  37. Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
  38. Renxi Gong & Xianglong Li, 2023. "A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism," Energies, MDPI, vol. 16(6), pages 1-24, March.
  39. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
  40. Zhihua Chen & Hui Wang & Tongxia Li & Ieongcheng Si, 2021. "Demand for Storage and Import of Natural Gas in China until 2060: Simulation with a Dynamic Model," Sustainability, MDPI, vol. 13(15), pages 1-19, August.
  41. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).
  42. Zhang, Yue & Farnoosh, Arash, 2019. "Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China," Energy Policy, Elsevier, vol. 132(C), pages 678-690.
  43. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
  44. 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.
  45. Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
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  47. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
  48. Chen, Ying & Xu, Xiuqin & Koch, Thorsten, 2020. "Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model," Applied Energy, Elsevier, vol. 262(C).
  49. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
  50. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
  51. Deng, Yanqiao & Ma, Xin & Zhang, Peng & Cai, Yubin, 2022. "Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization," Energy, Elsevier, vol. 260(C).
  52. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
  53. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.
  54. Zhou Ruhan & Nurulafiqah Nadzirah Binti Mansor & Hazlee Azil Illias, 2023. "Identification of Inrush Current Using a GSA-BP Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
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