Load situation awareness is one of the key technologies for demand-side resource management under the situation of more and more precise demands. Aiming at the situation assessment and dynamic regulation for typical loads

a load situation awareness method based on artificial intelligence (AI) Agent is proposed. The architecture for collaboration of AI Agent and cloud platform is constructed. Through the learning and cognizing of the load model at the user side

a spatial set of loads is further built

and temporal feature expanded random forest algorithm is put forward to realize the assessment of load operating state

which provides the decision-making basis for load regulation. Then

according to the results of assessment and the requirements of system optimization

differences between load curve and target curve in the cloud platform are evaluated

and optimal portfolio is selected based on the calculation results of situation indicators

which achieves precise load control. Finally

the results of simulation show that the proposed method can accurately analyze the load situation and achieve better load shaping effect.