Research on Load Forecasting Based on Quantum Genetic Algorithm in Small Living Environment

This study combines the small ecological environment algorithm with quantum genetic algorithm to predict the electricity load of solar buildings, aiming to improve the accuracy of electricity load prediction by combining the small ecological environment algorithm with quantum genetic algorithm algorithm. And evaluate its performance using two statistical indicators: coefficient of determination ($\mathrm{R}^{2}$) and root mean square error (RMSE). The research results indicate that the small ecological environment algorithm and quantum genetic algorithm have higher prediction accuracy, and small ecological environment optimization significantly improves the prediction accuracy of quantum genetic algorithm, $R^{2}=0.98$, RMSE $=0.12$. In addition, the small ecological quantum genetic algorithm showed excellent convergence, achieving a TIC value of 0.01395 after 121 iterations. This model is significantly better than traditional methods in predicting electricity load.