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Smart city construction is closely related to building energy consumption data analysis, and energy consumption prediction is helpful to guide urban power dispatching strategy. In general, the artificial neural network models used for energy consumption prediction are often based on the optimization of a single network, which has some shortcomings such as poor generalization ability and unstable fitting accuracy. Therefore, in order to improve the performance of BP neural network in building air conditioning energy consumption prediction, a per-column optimization prediction model is constructed in this paper. In order to avoid falling into the local minimum, genetic algorithm and particle swarm optimization algorithm are introduced to optimize the weight and threshold of the basic BP neural network to avoid the randomness of the parameters. Then, in order to improve the reliability and prediction accuracy of the model, the optimal prediction value is identified on the basis of the BP model prediction value based on the optimization algorithm. In addition, this paper makes an experimental study on the real air-conditioning energy consumption of a building in Laixi City, and the experimental results show the superiority of the model. The prediction accuracy is improved by about 79%, and the reliability of the algorithm is also improved. In the long run, the model can provide advance prediction for urban power dispatching and contribute to the construction of smart cities.