Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting

arXiv:2403.01438v1 Announce Type: new
Abstract: Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-based load forecasting. Conventional ML allows training a single global model using data from multiple smart meters requiring data transfer to a central server, raising concerns for network requirements, privacy, and security. We propose a split learning-based framework for load forecasting to alleviate this issue. We split a deep neural network model …

abstract arxiv availability collaborative cs.lg data demand energy energy management forecasting framework global grid infrastructure management multiple planning privacy sensor smart training transfer type