A prediction model for electric load based on an immune support vector machine (SVM) algorithm is proposed to address the issues of high randomness and poor stability in the electric load of residential areas. Considering various factors that affect the electric load of residents, the historical electric consumption and relevant climate data of residential areas are used as the processing objects. The principal component analysis (PCA) algorithm is utilized to preprocess the historical data of the power grid, and the immune algorithm is combined to preprocess the data by forming data clusters and defining labels for training the prediction model. To improve the accuracy of the model, the biological immune optimization algorithm is used to optimize the parameters of the SVM model. In the load prediction process, the prediction error is used as the basis for feedback tuning of the prediction model. The prediction performance of the immune SVM algorithm load prediction model is compared with that of the commonly used back propagation (BP) neural network and SVM algorithm model. The short-term and medium-term prediction accuracies of the immune SVM algorithm load prediction model are both above 98%, demonstrating good accuracy and robustness.