Short Term Nonlinear Load Forecasting Method for Distributed Photovoltaic Grid Connection Based on GSA Algorithm

In order to cope with the intermittency and uncertainty of photovoltaic power generation and improve the accuracy of short-term load forecasting, a distributed photovoltaic grid connected short-term nonlinear load forecasting method based on Genetic Simulated Annexing Algorithm (GSA) is proposed. The historical data of the distributed photovoltaic grid connected system is used as the analysis basis, and the concept of particle aggregation is introduced to establish a solution space. The global load data is searched in the space, and the original data is substituted into the solution space to achieve load data normalization processing. Design a distributed photovoltaic grid connected short-term nonlinear load forecasting method based on GSA, obtain the global optimal solution, and accurately predict short-term load changes. The experimental results show that this method has significantly improved prediction accuracy and can more accurately predict short-term nonlinear loads.