Regional Load Forecasting Scheme for Security Outsourcing Computation

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.