Aiming at the existing energy consumption prediction methods for office buildings, which fail to take into account the chaotic change characteristics of energy consumption data. A method of energy consumption prediction for office buildings based on chaotic time series is proposed.It can reconstruct the phase space of the time series of the research object, judge that it has chaotic characteristics, establish the combination model of chaos theory and support vector regression for training, and use Markov chain to eliminate the cumulative errors caused by parameter transfer of the combination model, and obtain the final prediction result.In order to verify the effectiveness of the algorithm, the energy consumption monitoring data of an office building in xi 'an was taken as an example for example analysis, and compared with other prediction methods such as nonlinear autoregressive neural network and support vector regression.The experimental results show that the prediction accuracy of the chaotic time series combination model modified by Markov is significantly improved, the prediction effect is better than other models and more consistent with the change law of energy consumption of office buildings, providing effective data support for energy conservation optimization.