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The influence of differential privacy on short term electric load forecasting

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Figure

Fig. 1 Essential roles and information flows in the current metering process (solid black arrows) and in theproposed differentially private process for privacy-preserving forecasting (dotted blue arrows)
Fig. 2 Statistics of the 20 zones of the GEFCom 2012 data set
Fig. 3 MAPE (left) and MAE (right) of Hong’s benchmark model for different zones (ordered by average load)
Table 1 Noise (λ) and sensitivities (�f) lead to ϵ and interpretable re-identification confidence (ρ)for k-fold adaptive composition (k = 38, 070)
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