and difficulty in fully reflecting the overall efficiency of the comprehensive energy system in current parks
this paper proposes a park comprehensive energy system efficiency evaluation model that combines improved grey wolf optimization algorithm and support vector machine. This method constructs a comprehensive evaluation index system covering three dimensions of society
economy
and environment
and uses an improved grey wolf optimization algorithm to optimize support vector machine parameters
thereby improving the convergence speed and prediction accuracy of the model
and achieving accurate and efficient evaluation of the comprehensive benefits of the park's integrated energy system. The empirical results show that the proposed improved hybrid model is significantly better than traditional support vector machines and the original hybrid model in terms of loss degree
training efficiency
and evaluation accuracy. It can provide scientific basis and decision support for the planning
optimization
and sustainable development of the energy system in the park.