Secure privacy computer mechanism for power load forecasting

With the continuous advancement of the new power system, the application of big data technology in electric load forecasting has become increasingly crucial and holds significant importance for the optimization of power system planning and operation. In recent years, the frequent occurrence of extreme weather events has highlighted the advantages of distributed load forecasting in rapid response and timely adjustment of power planning. However, the security challenges faced by distributed nodes, including insufficient prediction accuracy and load privacy data leakage, have become urgent problems that need to be solved. In view of these challenges, this paper proposes a distributed security and privacy load forecasting framework, which encompasses multiple participants such as electricity consumers, load forecasters, and power plants. By integrating encryption technology and Data Processing Unit (DPU), the secure storage and efficient calculation of data are realized. This paper designs a secure computing architecture based on DPU, which makes full use of the non-tampering and traceability of DPU to ensure the high security of power load forecasting results. In addition, this paper also develops a lightweight algorithm to achieve efficient calculation of power load forecasting while effectively protecting users’ privacy data. Experiments show that the proposed algorithm has significant advantages in data protection and computational efficiency, which verifies its effectiveness and superiority in practical applications.