Enhancing Short-Term Load Forecasting in Virtual Power Plants via Sparrow Search Algorithm Optimized Long Short-Term Memory Network

With the introduction of goals for carbon neutrality and peak carbon emissions, Virtual Power Plants(VPPS) are evolving towards intelligence, automation, scaling, and diversification of resources. Load forecasting technology, when applied in VPPS, helps to improve the operational efficiency of the power system, optimize resource allocation, enhance the stability and reliability of the power grid, and also provides scientific data support and decision making basis for the operation of VPPS. This article focuses on the multi-objective optimization scheduling of VPPS, with economic and low-carbon goals at its core. An innovative short-term load forecasting method is proposed to address the key issue of poor accuracy in electric load forecasting. A load forecasting model based on the Sparrow Search Algorithm (SSA) optimized Long Short-Term Memory (LSTM) network is introduced. This model effectively enhances the accuracy and training efficiency of load forecasting by optimizing the key parameters of the LSTM network. Comparative experimental results show that the improved model significantly outperforms traditional methods in terms of error metrics, and is able to more accurately capture the trends of complex load changes. This provides high-quality input data support for the optimization of virtual power plant scheduling.