With the increasing contradiction between energy supply and rapid economic development, building energy conservation has become a key link in sustainable development strategy. It is an important prerequisites for optimal control of building energy conservation that fast and accurate method research for predicting building electricity consumption. In this study, genetic algorithms and ant colony clustering algorithms are combined to improve the network node of IHCMAC (Improvement Hyperball CMAC) neural network based on clustering. As a building power load forecasting model, GIHCMAC (Genetic Algorithm Ant Colony Clustering Algorithm based on IHCMAC) is used to predict the electrical load of an office building in Weifang. The research results show that the prediction model has the smallest number of iterations and high accuracy. Its iteration number, training error, and generalization error are 9, 0.0045, and 0.0014 respectively. Compared with IHCMAC, KHCMAC (K-means Hyperball CMAC) and IKHCMAC (Improvement K-means Hyperball CMAC) model, GIHCMAC has faster convergence speed, higher accuracy, and better generalization.