Peak load estimation of renewable energy generation based on imitator dynamic algorithm

Abstract

In the field of renewable energy generation forecasting, it is crucial to accurately estimate the peak load. However, due to the complex nonlinear characteristics of the data, the traditional long short-term memory network performs poorly in processing these data. This study introduces the imitator dynamic algorithm, which is able to generate samples close to the real situation by learning the change pattern of the data. Extensive experimental tests show that with the number of iterations increasing to 200, the prediction accuracy of the model reaches 62.35%, which is significantly better than that of the long short-term memory network, although it is decreased compared with the initial iteration. The imitator dynamic algorithm accurately learns the unknown data distribution according to two metrics of probability density and cumulative distribution within 5% error, demonstrating good generalization ability and robustness. These research results are of great significance for predicting the actual generation capacity of renewable energy. It not only enables grid operators to accurately predict and schedule power generation, but also supports sustainable energy development by improving grid stability and promoting the use of renewable energy.