Energy Grid Control Using Homomorphic Load Prediction
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Solution Overview
Problem
Energy service providers face challenges in predicting electric load on energy grids due to complex and computationally intensive calculations, which can compromise data privacy when raw data is shared with external systems for model adaptation.
Innovation Solution
A method using homomorphic encryption to encrypt user data, allowing external systems to perform prediction calculations on encrypted data without decryption, enabling decentralized storage and computation, thus maintaining data privacy and facilitating complex predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prediction calculations are performed externally to reduce local computational burden, then productivity is improved, but data privacy is compromised due to exposure of raw user data
Solution Approach 1:
Homomorphic encryption acts as an intermediary mechanism that allows external systems to perform predictions on encrypted data without exposing raw user information. The encryption scheme enables computations on ciphertexts while maintaining data confidentiality, thus resolving the contradiction between external processing efficiency and data privacy protection
Solution Approach 2:
The patent transforms the state of data from plaintext to encrypted form, changing the parameter of data representation. This allows the same data to be processed externally while its sensitive characteristics are hidden through encryption, enabling productivity improvement without privacy compromise
2Object-affected harmful factors
If complex prediction models are calculated locally to maintain data privacy, then data privacy is improved, but device complexity increases
Solution Approach 1:
The patent extracts the computationally intensive prediction calculations from the local user device and relocates them to external processing systems. By doing so, the complex model calculations are performed elsewhere while data privacy is maintained through homomorphic encryption, thus reducing local device complexity without sacrificing privacy protection
3Adaptability or versatility
If raw user data is shared with external systems for model adaptation, then adaptability is improved, but data privacy is worsened
Solution Approach 1:
Homomorphic encryption serves as an intermediary that enables model adaptation through external processing while maintaining data confidentiality. The encryption allows external systems to access and process user data patterns for model improvement without actually seeing the raw sensitive information, thus achieving adaptability without privacy loss
Data Source
AI summary
A method for controlling an energy grid. In this method, first pieces of information about the past behavior of at least one user at the energy grid are ascertained by a processing unit assigned to the user, homomorphically encrypted and transferred homomorphically encrypted to a first data memory. An external processing unit reads the homomorphically encrypted first pieces of information, calculates as a function thereof second, homomorphically encrypted pieces of information about a predicted, future behavior of the user and stores the second, homomorphically encrypted pieces of information on a second data memory. The second, homomorphically encrypted pieces of information are read out from the second data memory and decrypted by the processing unit assigned to the user. A control of the energy grid takes place as a function of the decrypted second pieces of information.