Research and Development of Line Loss Management and Load Forecasting System for Electric Power Enterprises Based on New Energy Consumption Technology Optimization
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Salahuddin, M., Alam, K., Ozturk, I., & Sohag, K. (2018). The effects of electricity consumption, economic growth, financial development and foreign direct investment on CO2 emissions in Kuwait. Renewable and sustainable energy reviews, 81, 2002-2010.SalahuddinM.AlamK.OzturkI.SohagK. (2018). The effects of electricity consumption, economic growth, financial development and foreign direct investment on CO2 emissions in Kuwait. Renewable and sustainable energy reviews, 81, 2002-2010.Search in Google Scholar
Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, 73, 1104-1122.YildizB.BilbaoJ. I.SproulA. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, 73, 1104-1122.Search in Google Scholar
Li, B., & Wang, W. (2024). Knowledge-Graph-Based Integrated Line Loss Evaluation Management System. Applied Sciences, 14(20), 9462.LiB.WangW. (2024). Knowledge-Graph-Based Integrated Line Loss Evaluation Management System. Applied Sciences, 14(20), 9462.Search in Google Scholar
Yang, Y., Ye, Q., Tung, L. J., Greenleaf, M., & Li, H. (2017). Integrated size and energy management design of battery storage to enhance grid integration of large-scale PV power plants. IEEE Transactions on industrial electronics, 65(1), 394-402.YangY.YeQ.TungL. J.GreenleafM.LiH. (2017). Integrated size and energy management design of battery storage to enhance grid integration of large-scale PV power plants. IEEE Transactions on industrial electronics, 65(1), 394-402.Search in Google Scholar
Khonjelwayo, B., & Nthakheni, T. (2021). Determining the causes of electricity losses and the role of management in curbing them: A case study of City of Tshwane Metropolitan Municipality, South Africa. Journal of Energy in Southern Africa, 32(4), 45-57.KhonjelwayoB.NthakheniT. (2021). Determining the causes of electricity losses and the role of management in curbing them: A case study of City of Tshwane Metropolitan Municipality, South Africa. Journal of Energy in Southern Africa, 32(4), 45-57.Search in Google Scholar
Long, H., Chen, C., Gu, W., Xie, J., Wang, Z., & Li, G. (2020). A data-driven combined algorithm for abnormal power loss detection in the distribution network. IEEE Access, 8, 24675-24686.LongH.ChenC.GuW.XieJ.WangZ.LiG. (2020). A data-driven combined algorithm for abnormal power loss detection in the distribution network. IEEE Access, 8, 24675-24686.Search in Google Scholar
Zia, M. F., Elbouchikhi, E., & Benbouzid, M. (2018). Microgrids energy management systems: A critical review on methods, solutions, and prospects. Applied energy, 222, 1033-1055.ZiaM. F.ElbouchikhiE.BenbouzidM. (2018). Microgrids energy management systems: A critical review on methods, solutions, and prospects. Applied energy, 222, 1033-1055.Search in Google Scholar
Vengadesan, A. (2021). Transmission congestion management through optimal placement and sizing of TCSC devices in a deregulated power network. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 5390-5403.VengadesanA. (2021). Transmission congestion management through optimal placement and sizing of TCSC devices in a deregulated power network. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 5390-5403.Search in Google Scholar
Hussain, I., Khan, F., Ahmad, I., Khan, S., & Saeed, M. (2021). Power loss reduction via distributed generation system injected in a radial feeder. Mehran University Research Journal Of Engineering & Technology, 40(1), 160-168.HussainI.KhanF.AhmadI.KhanS.SaeedM. (2021). Power loss reduction via distributed generation system injected in a radial feeder. Mehran University Research Journal Of Engineering & Technology, 40(1), 160-168.Search in Google Scholar
Yingchun, W. A. N. G., Dengping, T. A. N. G., & Xingpeng, L. I. (2020). Research on Economic Settlement of Negative Value Network Loss Based on Transmission Lines. Journal of Electrical Engineering, 15(1), 89-94.YingchunW. A. N. G.DengpingT. A. N. G.XingpengL. I. (2020). Research on Economic Settlement of Negative Value Network Loss Based on Transmission Lines. Journal of Electrical Engineering, 15(1), 89-94.Search in Google Scholar
Noor, S., Yang, W., Guo, M., van Dam, K. H., & Wang, X. (2018). Energy demand side management within micro-grid networks enhanced by blockchain. Applied energy, 228, 1385-1398.NoorS.YangW.GuoM.van DamK. H.WangX. (2018). Energy demand side management within micro-grid networks enhanced by blockchain. Applied energy, 228, 1385-1398.Search in Google Scholar
Byrne, R. H., Nguyen, T. A., Copp, D. A., Chalamala, B. R., & Gyuk, I. (2017). Energy management and optimization methods for grid energy storage systems. IEEE Access, 6, 13231-13260.ByrneR. H.NguyenT. A.CoppD. A.ChalamalaB. R.GyukI. (2017). Energy management and optimization methods for grid energy storage systems. IEEE Access, 6, 13231-13260.Search in Google Scholar
Bai, L., Wang, J., Wang, C., Chen, C., & Li, F. (2017). Distribution locational marginal pricing (DLMP) for congestion management and voltage support. IEEE Transactions on Power Systems, 33(4), 4061-4073.BaiL.WangJ.WangC.ChenC.LiF. (2017). Distribution locational marginal pricing (DLMP) for congestion management and voltage support. IEEE Transactions on Power Systems, 33(4), 4061-4073.Search in Google Scholar
Du, Y., Yin, X., Lai, J., Sun, G., Zhao, Z., & Ullah, Z. (2020, November). Energy router based minimum loss cost routing strategy in energy internet. In 2020 IEEE 1st China International Youth Conference on Electrical Engineering (CIYCEE) (pp. 1-6). IEEE.DuY.YinX.LaiJ.SunG.ZhaoZ.UllahZ. (2020, November). Energy router based minimum loss cost routing strategy in energy internet. In 2020 IEEE 1st China International Youth Conference on Electrical Engineering (CIYCEE) (pp. 1-6). IEEE.Search in Google Scholar
Klaimi, J., Rahim-Amoud, R., Merghem-Boulahia, L., & Jrad, A. (2018). A novel loss-based energy management approach for smart grids using multi-agent systems and intelligent storage systems. Sustainable cities and society, 39, 344-357.KlaimiJ.Rahim-AmoudR.Merghem-BoulahiaL.JradA. (2018). A novel loss-based energy management approach for smart grids using multi-agent systems and intelligent storage systems. Sustainable cities and society, 39, 344-357.Search in Google Scholar
Al-Shetwi, A. Q., Hannan, M. A., Jern, K. P., Mansur, M., & Mahlia, T. M. I. (2020). Grid-connected renewable energy sources: Review of the recent integration requirements and control methods. Journal of Cleaner Production, 253, 119831.Al-ShetwiA. Q.HannanM. A.JernK. P.MansurM.MahliaT. M. I. (2020). Grid-connected renewable energy sources: Review of the recent integration requirements and control methods. Journal of Cleaner Production, 253, 119831.Search in Google Scholar
Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., … & Wang, K. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659-670.ChenY.XuP.ChuY.LiW.WuY.NiL.WangK. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659-670.Search in Google Scholar
Haq, M. R., & Ni, Z. (2019). A new hybrid model for short-term electricity load forecasting. IEEE access, 7, 125413-125423.HaqM. R.NiZ. (2019). A new hybrid model for short-term electricity load forecasting. IEEE access, 7, 125413-125423.Search in Google Scholar
Kuster, C., Rezgui, Y., & Mourshed, M. (2017). Electrical load forecasting models: A critical systematic review. Sustainable cities and society, 35, 257-270.KusterC.RezguiY.MourshedM. (2017). Electrical load forecasting models: A critical systematic review. Sustainable cities and society, 35, 257-270.Search in Google Scholar
Hammad, M. A., Jereb, B., Rosi, B., & Dragan, D. (2020). Methods and models for electric load forecasting: a comprehensive review. Logist. Sustain. Transp, 11(1), 51-76.HammadM. A.JerebB.RosiB.DraganD. (2020). Methods and models for electric load forecasting: a comprehensive review. Logist. Sustain. Transp, 11(1), 51-76.Search in Google Scholar
Lindberg, K. B., Seljom, P., Madsen, H., Fischer, D., & Korpås, M. (2019). Long-term electricity load forecasting: Current and future trends. Utilities Policy, 58, 102-119.LindbergK. B.SeljomP.MadsenH.FischerD.KorpåsM. (2019). Long-term electricity load forecasting: Current and future trends. Utilities Policy, 58, 102-119.Search in Google Scholar
Zhang, J., Wei, Y. M., Li, D., Tan, Z., & Zhou, J. (2018). Short term electricity load forecasting using a hybrid model. Energy, 158, 774-781.ZhangJ.WeiY. M.LiD.TanZ.ZhouJ. (2018). Short term electricity load forecasting using a hybrid model. Energy, 158, 774-781.Search in Google Scholar
Mordjaoui, M., Haddad, S., Medoued, A., & Laouafi, A. (2017). Electric load forecasting by using dynamic neural network. International journal of hydrogen energy, 42(28), 17655-17663.MordjaouiM.HaddadS.MedouedA.LaouafiA. (2017). Electric load forecasting by using dynamic neural network. International journal of hydrogen energy, 42(28), 17655-17663.Search in Google Scholar
R. Mukesh, Sarat C. Dass, M. Vijay, S. Kiruthiga, M. Praveenkumar & M. Prashanth. (2024). Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model. Advances in Space Research(10),4865-4905.R.MukeshSarat C.DassM.VijayS.KiruthigaM.PraveenkumarM.Prashanth (2024). Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model. Advances in Space Research(10),4865-4905.Search in Google Scholar
Riswanda Ayu Dhiya’ulhaq, Anisya Safira, Indah Fahmiyah & Mohammad Ghani. (2024). Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety. MethodsX103031-103031.Riswanda AyuDhiya’ulhaqAnisyaSafiraIndahFahmiyahMohammadGhani. (2024). Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety. MethodsX103031-103031.Search in Google Scholar
Wenchao Gan, Ruilong Ma, Wenlong Zhao, Xiaoyan Peng, Hao Cui,Jia Yan… & Jin Chu. (2025). A VMD-LSTNet-Attention model for concentration prediction of mixed gases.Sensors and Actuators: B. Chemical136641-136641.WenchaoGanRuilongMaWenlongZhaoXiaoyanPengHaoCuiJiaYanJinChu. (2025). A VMD-LSTNet-Attention model for concentration prediction of mixed gases.Sensors and Actuators: B. Chemical136641-136641.Search in Google Scholar
Subramanian Malliga, Rajasekar Vani, V. E. Sathishkumar, Shanmugavadivel Kogilavani & Nandhini P. S‥ (2022). Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification. Electronics(24),4117-4117.SubramanianMalligaRajasekarVaniV. E.SathishkumarShanmugavadivelKogilavaniNandhiniP. S. (2022). Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification. Electronics(24),4117-4117.Search in Google Scholar