Privacy-Preserving Probabilistic Forecasting for Temporal-spatial Correlated Wind Farms

2018/12/17 by Mengshuo Jia, Chen Shen, Jia, Mengshuo +5
Computer Science · Decision Sciences · Engineering · Mathematics · #Big Data Technologies and Applications #Computational Physics and Python Applications #Energy Load and Power Forecasting #cs.IT #eess.SP #math.IT

paper · pdf · doi:10.48550/arxiv.1901.01126

arxiv created 2018/12/17 · arxiv updated 2019/01/07

Abstract

Adopting Secure scalar product and Secure sum techniques, we propose a privacy-preserving method to build the joint and conditional probability distribution functions of multiple wind farms' output considering the temporal-spatial correlation. The proposed method can protect the raw data of wind farms (WFs) from disclosure, and are mathematically equivalent to the centralized method which needs to gather the raw data of all WFs.

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