The construction industry, as one of the industries of environmental pollution, especially needs to pay attention to energy saving and emission reduction and sustainable development. This paper combines the actual construction project profile, the energy saving and emission reduction electrical automation system design under the concept of intelligent building, and then use BP neural network to construct the energy emission reduction electrical automation system energy consumption prediction model, and in this way to guide the system scheduling and control work for the development of the system control program to provide a scientific decision-making basis. Starting from the actual development of the current intelligent building, selecting the power consumption, carbon emissions, system operation and management costs as the objective function, while also setting the constraints, and proposing the use of genetic algorithms to form the system scheduling control model. Finally, based on the system energy consumption prediction and control, the research program of this paper is empirically analyzed. The genetic algorithm calculates the optimal solution of the three objective functions as 4821KJ, 21.81t, 100,600 yuan, respectively, i.e., the genetic algorithm realizes the globally optimal energy-saving and emission reduction electrical automation system control. In this paper, the role of the control program, energy saving power 28710.5kW·h, converted into carbon dioxide emissions of 30.415t, comprehensively confirmed the system control of this paper has a good practical effect.
