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

## TL;DR

This paper introduces a privacy-preserving probabilistic forecasting method for wind farms that maintains data confidentiality while accurately modeling temporal and spatial correlations.

## Contribution

It presents a novel secure computation approach using scalar product and sum techniques to estimate joint and conditional distributions without exposing raw data.

## Key findings

- Protects wind farm data confidentiality
- Achieves equivalent accuracy to centralized methods
- Enables secure probabilistic forecasting

## 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.