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Application of Ensemble Kalman Filter in Forecasting the Electricity Grid Carbon Factor

Eng Tseng Lau 1, Qingping Yang 1, Alistair Forbes 2, and Valerie Livina 2
1. College of Engineering, Design, and Physical Sciences, Brunel University London, Uxbridge, UB8 3PH, UK
2. Mathematics and Modelling Group, National Physical Laboratory, Hampton Road, Teddington, Middlesex, TW11 0LW, UK

Abstract—Several publications have discussed the application of Ensemble Kalman Filter (EnKF) in history matching problems. The EnKF provides updated approximations based on the conditioned constraints to the historical data. In this paper we show how the EnKF is capable of forecasting/recovering the unpredictable trends of Electricity Grid Carbon Factor (EGCF). We adopt the EGCF scenario in the UK based on the available energy data provided by the Balancing Mechanism Reporting System (BMRS). We apply EnKF for forecasting the incomplete datasets in the UK EGCF in 2014. We present the ability of EnKF to recover the EGCF.

Index Terms—history matching, recovery, ensemble Kalman filter

Cite: Eng Tseng Lau, Qingping Yang, Alistair Forbes, and Valerie Livina, "Application of Ensemble Kalman Filter in Forecasting the Electricity Grid Carbon Factor," International Journal of Electrical Energy, Vol. 3, No. 4, pp. 209-212, December 2015. doi: 10.18178/ijoee.3.4.209-212

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