Privacy-Preserving Distributed Parameter Estimation for Probability Distribution of Wind Power Forecast Error
Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the conditional probability distribution. Traditional parameter estimation methods for probability distributions require the collection of historical data of all WFs. However, in the context of multi-regional interconnected grids, neither regional ISOs nor WFs can collect the raw data of WFs in other regions due to privacy or competition considerations. Therefore, based on the Gaussian mixture model, this paper first proposes a privacy-preserving distributed expectation-maximization algorithm to estimate the parameters of the joint probability distribution. This algorithm consists of two original methods: (1) a privacy-preserving distributed summation algorithm and (2) a privacy-preserving distributed inner product algorithm. Then, we derive each WF's conditional probability distribution of forecast error from the joint one. By the proposed algorithms, WFs only need local calculations and privacy-preserving neighboring communications to achieve the whole parameter estimation. These algorithms are verified using the wind integration data set published by the NREL.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision Makingparameter estimationPrivacy PreservingSimilar Papers 제목 키워드 기반
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms
Privacy-preserving data analysis becomes prevailing in recent years. It is a common sense in privacy literature that strict differential privacy can only be obtained by imposing additional randomness in the algorithm. In…
Common Sense ReasoningPrivacy PreservingregressionDeconvoluting Kernel Density Estimation and Regression for Locally Differentially Private Data
Local differential privacy has become the gold-standard of privacy literature for gathering or releasing sensitive individual data points in a privacy-preserving manner. However, locally differential data can twist the p…
Density EstimationPrivacy PreservingregressionA Privacy-Preserving and Trustable Multi-agent Learning Framework
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has been shown that, even when data is not di…
Privacy PreservingOptimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints
Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range of applications. This paper addresses th…
Federated LearningPrivacy PreservingPrivacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models
Various privacy-preserving frameworks that respect the individual's privacy in the analysis of data have been developed in recent years. However, available model classes such as simple statistics or generalized linear mo…
feature selectionPrivacy Preserving