Visualized calculation of regional power grid power data based on multiple linear regression equation

[1] Akhter, M. N., Mekhilef, S., Mokhlis, H., & Shah, N. M. Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renewable Power Generation., 2019. 13(7): 1009–1023 AkhterM. N. MekhilefS. MokhlisH. ShahN. M. Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques IET Renewable Power Generation 2019 13 7 1009 1023 Search in Google Scholar

[2] Zhen, Z., Liu, J., Zhang, Z., Wang, F., Chai, H., Yu, Y.,... & Lin, Y. Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image. IEEE Transactions on Industry Applications., 2020. 56(4): 3385–3396 ZhenZ. LiuJ. ZhangZ. WangF. ChaiH. YuY. LinY. Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image IEEE Transactions on Industry Applications 2020 56 4 3385 3396 Search in Google Scholar

[3] Jewell, J., Vinichenko, V., Nacke, L., & Cherp, A. Prospects for powering past coal. Nature Climate Change., 2019. 9(8): 592–597 JewellJ. VinichenkoV. NackeL. CherpA. Prospects for powering past coal Nature Climate Change 2019 9 8 592 597 Search in Google Scholar

[4] Touchent, K., Hammouch, Z. & Mekkaoui, T. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives. Applied Mathematics and Nonlinear Sciences., 2020. 5(2): 35–48 TouchentK. HammouchZ. MekkaouiT. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives Applied Mathematics and Nonlinear Sciences 2020 5 2 35 48 Search in Google Scholar

[5] Aghili, A. Complete Solution For The Time Fractional Diffusion Problem With Mixed Boundary Conditions by Operational Method. Applied Mathematics and Nonlinear Sciences., 2020. 6(1): 9–20 AghiliA. Complete Solution For The Time Fractional Diffusion Problem With Mixed Boundary Conditions by Operational Method Applied Mathematics and Nonlinear Sciences 2020 6 1 9 20 Search in Google Scholar

[6] Giorgi, F., Raffaele, F., & Coppola, E. The response of precipitation characteristics to global warming from climate projections. Earth System Dynamics., 2019. 10(1): 73–89 GiorgiF. RaffaeleF. CoppolaE. The response of precipitation characteristics to global warming from climate projections Earth System Dynamics 2019 10 1 73 89 Search in Google Scholar

[7] Abdel-Nasser, M., & Mahmoud, K. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications., 2019. 31(7): 2727–2740 Abdel-NasserM. MahmoudK. Accurate photovoltaic power forecasting models using deep LSTM-RNN Neural Computing and Applications 2019 31 7 2727 2740 Search in Google Scholar

[8] Mehr, A. D., Nourani, V., Khosrowshahi, V. K., & Ghorbani, M. A. A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology., 2019. 16(1): 335–346 MehrA. D. NouraniV. KhosrowshahiV. K. GhorbaniM. A. A hybrid support vector regression–firefly model for monthly rainfall forecasting International Journal of Environmental Science and Technology 2019 16 1 335 346 Search in Google Scholar

[9] Mehr, A. D., Nourani, V., Khosrowshahi, V. K., & Ghorbani, M. A. A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology., 2019. 16(1): 335–346 MehrA. D. NouraniV. KhosrowshahiV. K. GhorbaniM. A. A hybrid support vector regression–firefly model for monthly rainfall forecasting International Journal of Environmental Science and Technology 2019 16 1 335 346 Search in Google Scholar

[10] Zhang, Z., & Hong, W. C. Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dynamics., 2019. 98(2): 1107–1136 ZhangZ. HongW. C. Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm Nonlinear Dynamics 2019 98 2 1107 1136 Search in Google Scholar

[11] Sanikhani, H., Kisi, O., Maroufpoor, E., & Yaseen, Z. M. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theoretical and Applied Climatology., 2019. 135(1): 449–462 SanikhaniH. KisiO. MaroufpoorE. YaseenZ. M. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios Theoretical and Applied Climatology 2019 135 1 449 462 Search in Google Scholar

[12] Ramon, J., Lledo, L., Torralba, V., Soret, A., & Doblas-Reyes, F. J. What global reanalysis best represents near-surface winds?. Quarterly Journal of the Royal Meteorological Society., 2019. 145(724): 3236–3251 RamonJ. LledoL. TorralbaV. SoretA. Doblas-ReyesF. J. What global reanalysis best represents near-surface winds? Quarterly Journal of the Royal Meteorological Society 2019 145 724 3236 3251 Search in Google Scholar