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The energy consumption of mobile networks poses a critical challenge.Mitigating this concern necessitates the deployment and optimization of networkenergy-saving solutions, such as carrier shutdown, to dynamically managenetwork resources. Traditional optimization approaches encounter complexity dueto factors like the large number of cells, stochastic traffic, channelvariations, and intricate trade-offs. This paper introduces the simulatedreality of communication networks (SRCON) framework, a novel, data-drivenmodeling paradigm that harnesses live network data and employs a blend ofmachine learning (ML)- and expert-based models. These mix of models accuratelycharacterizes the functioning of network components, and predicts networkenergy efficiency and user equipment (UE) quality of service for any energycarrier shutdown configuration in a specific network. Distinguishing itselffrom existing methods, SRCON eliminates the reliance on expensive expertknowledge, drive testing, or incomplete maps for predicting networkperformance. This paper details the pipeline employed by SRCON to decompose thelarge network energy efficiency modeling problem into ML and expert-basedsubmodels. It demonstrates how, by embracing stochasticity, and carefullycrafting the relationship between such submodels, the overall computationalcomplexity can be reduced and prediction accuracy enhanced. Results derivedfrom real network data underscore the paradigm shift introduced by SRCON,showcasing significant gains over a state-of-the art method used by a operatorfor network energy efficiency modeling. The reliability of this local,data-driven modeling of the network proves to be a key asset for networkenergy-saving optimization.

Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach