Exploration of multiple regression analysis and prediction methods for power grid carbon emission factors

[1] Shuai, C., Chen, X., Wu, Y., Tan, Y., Zhang, Y., & Shen, L. (2018). Identifying the key impact factors of carbon emission in China: Results from a largely expanded pool of potential impact factors. Journal of Cleaner Production, 175, 612-623. Search in Google Scholar

[2] Zhang, X., Luo, L., & Skitmore, M. (2015). Household carbon emission research: an analytical review of measurement, influencing factors and mitigation prospects. Journal of Cleaner Production, 103, 873-883. Search in Google Scholar

[3] St-Jacques, M., Bucking, S., & O’Brien, W. (2020). Spatially and temporally sensitive consumption-based emission factors from mixed-use electrical grids for building electrical use. Energy and Buildings, 224, 110249. Search in Google Scholar

[4] Aqachmar, Z., Sassi, H. B., Lahrech, K., & Barhdadi, A. (2021). Solar technologies for electricity production: An updated review. International journal of hydrogen energy, 46(60), 30790-30817. Search in Google Scholar

[5] Röder, J., Beier, D., Meyer, B., Nettelstroth, J., Stührmann, T., & Zondervan, E. (2020). Design of renewable and system-beneficial district heating systems using a dynamic emission factor for grid-sourced electricity. Energies, 13(3), 619. Search in Google Scholar

[6] Risner, J., & Sutherland, A. (2021). Static grid carbon factors–Can we do better?. Building Services Engineering Research and Technology, 42(3), 257-277. Search in Google Scholar

[7] Jha, R., Lee, S., Iyengar, S., Hajiesmaili, M. H., Irwin, D., & Shenoy, P. (2020, June). Emission-aware energy storage scheduling for a greener grid. In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (pp. 363-373). Search in Google Scholar

[8] Liu, J., Liao, R., Dong, F., Huang, C., Li, H., Liu, J., & Zhao, T. (2024). Low-carbon technology selection and carbon reduction potential assessment in the shipbuilding industry with dynamically changing grid emission factors. Journal of Cleaner Production, 441, 140707. Search in Google Scholar

[9] St-Jacques, M., Bucking, S., O’Brien, W., & Macdonald, I. (2024). Spatio-temporal electrical grid emission factors effects on calculated GHG emissions of buildings in mixed-grid environments. Science and Technology for the Built Environment, 30(1), 37-50. Search in Google Scholar

[10] Bu, L., Chen, X., Gan, L., Yu, K., Zhou, Y., Cao, J., & Cao, Y. (2023). Low‐carbon operation method of the building based on dynamic carbon emission factor of power system. IET Smart Grid, 6(1), 67-85. Search in Google Scholar

[11] Qu, S., Wang, H., Liang, S., Shapiro, A. M., Suh, S., Sheldon, S., ... & Xu, M. (2017). A Quasi-Input-Output model to improve the estimation of emission factors for purchased electricity from interconnected grids. Applied energy, 200, 249-259. Search in Google Scholar

[12] Kozhikov, M., Janelidze, P., Seitmukhanbet, A., Aiman, Y., Karymsakov, A., & Mkilima, T. (2024). Community-Centric Carbon Reduction Initiatives and Their Impact on Grid Emission Factors: A Case Study in Kazakhstan. Polish Journal of Environmental Studies, 33(4). Search in Google Scholar

[13] Zhang, X., Zhu, Q., & Zhang, X. (2023). Carbon emission intensity of final electricity consumption: assessment and decomposition of regional power grids in china from 2005 to 2020. Sustainability, 15(13), 9946. Search in Google Scholar

[14] Maji, D., Shenoy, P., & Sitaraman, R. K. (2022, November). CarbonCast: multi-day forecasting of grid carbon intensity. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 198-207). Search in Google Scholar

[15] Shen, X., Li, J., Yin, Y., Tang, J., Lin, W., & Zhou, M. (2024). Carbon Emission Factors Prediction of Power Grid by Using Graph Attention Network. Energy Engineering, 121(7), 1945-1961. Search in Google Scholar

[16] Jia, M., Zhang, Z., Zhang, L., Zhao, L., Lu, X., Li, L., ... & Wang, J. (2024). Optimization of electricity generation and assessment of provincial grid emission factors from 2020 to 2060 in China. Applied Energy, 373, 123838. Search in Google Scholar

[17] S Yang, W., Zhou, C., Song, J., Xuan, D., Yuan, Q., Zhu, G., & Zhang, P. (2024). Data-driven smart grid carbon emission control methods using graph-based power flow computing. Journal of Computational Methods in Sciences and Engineering, 24(4-5), 3245-3259. Search in Google Scholar

[18] Wen, L., & Cao, Y. (2020). Influencing factors analysis and forecasting of residential energy-related CO2 emissions utilizing optimized support vector machine. Journal of Cleaner Production, 250, 119492. Search in Google Scholar

[19] Shen, X., Tang, J., Li, J., Zhao, Y., Yin, Y., & Zhang, F. (2024, March). TimesNet: A algorithm for day-ahead forecast of dynamic carbon emission factors in power grids. In 2024 6th Asia Energy and Electrical Engineering Symposium (AEEES) (pp. 1393-1398). IEEE. Search in Google Scholar

[20] Yang, H., Tang, A., Zhang, K., Wang, T., Zhou, H., Li, X., ... & Peng, J. (2024). Regional power grid carbon emission change risk assessment based on dynamic carbon emission factors. CSEE Journal of Power and Energy Systems. Search in Google Scholar

[21] Zhang, Y., Guo, B., Liu, C., Li, H., & Xu, W. (2023, July). Graph Convolution Network for Carbon Emission Factor Forecast of Power Grid. In 2023 5th International Conference on Power and Energy Technology (ICPET) (pp. 893-898). IEEE. Search in Google Scholar

[22] Ren, F., & Long, D. (2021). Carbon emission forecasting and scenario analysis in Guangdong Province based on optimized Fast Learning Network. Journal of Cleaner Production, 317, 128408. Search in Google Scholar

[23] Xin Huang,Keteng Jiang,Shuxin Luo,Haibo Li & Zongxiang Lu. (2024). Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory. Sustainability(15),6483-6483. Search in Google Scholar

[24] Xikang Yan,Qinyu Luo,Zeyu Chen,Yunhan Yan,Tian Qiu & Peng Cheng. (2024). Carbon Emission Accounting Model for Comprehensive Medical Facilities Based on Population Flow. Buildings(3). Search in Google Scholar

[25] Qinglei Guo,Xiukui Pan,Weiqing Zhou,Peng Li & Qi Li. (2024). Credible calculation method for regional corrected carbon emission factors taking into account the nearby consumption of renewable energy. Journal of Physics: Conference Series(1),012004-012004. Search in Google Scholar

[26] Chen Kun,Wang Xinhua,Alghazzawi Daniyal M. & Wang Yanfeng. (2021). Visualized calculation of regional power grid power data based on multiple linear regression equation. Applied Mathematics and Nonlinear Sciences(1),93-102. Search in Google Scholar