The development and effective use of energy and per capita consumption are important indicators of production technology and living standards. The burning of fossil fuels has caused serious environmental pollution and global warming. The irrational transportation and utilization of energy has caused a lot of waste

It can be understood as a comprehensive energy production, supply and demand system formed by the organic coordination and optimization of energy planning, construction, distribution, transfer, storage, and consumption. Integrated energy systems are of great significance for improving energy efficiency, reducing carbon emissions, and ensuring clean energy supply.

If the energy supply process of the integrated energy system can be accurately analyzed and evaluated to improve energy efficiency and reduce carbon emissions, then the integrated energy system can achieve better energy conservation.

Comprehensive energy system energy supply efficiency evaluation model based on Monte Carlo measurement data envelopment analysis (MC) method (MC-DBM-DEA) for energy optimization and carbon emission reduction.

MC can estimate multi-dimensional means in complex spaces, correct the corresponding solutions through extensive performance and time debugging, meet accuracy requirements, and expand the power supply data of integrated energy systems. In terms of F test, the effectiveness of MC extension is verified

The above methods have certain advantages, but they cannot analyze multi-output production activities well. DEA does not require the determination of the specific form of the production function, which is a significant advantage in studies of environmental and energy efficiency

Traditional DEA Model,The traditional DEA model does not consider the possible,inputs and multiple output relaxations of each DMU.

Robustness refers to the resistance of a system or model to changes, uncertainties, or disturbances. In data science and machine learning, a robust model is one that maintains stable performance in the face of different types of data or input changes.

Most of the traditional DEA models are radial measurement models and angular measurement models. Radial models often ignore the issue of relaxation. At the same time, angle models usually only consider one direction angle (input direction or output direction). Tone first proposed a non-radial and non-angular SBM-DEA model based on slack variable measures. The SBM-DEA model directly introduces slack variables into the objective function, which can well incorporate environmental factors into the measurement of the SBM-DEA model.

Efficiency measurement results are not affected by DMU measurement input and output terms

The difference between the efficiency value and each input and output is monotonically decreasing

The large number theorem and the central limit theorem in probability theory are the theoretical basis of the MC method.

f is the reliability function

Monte Carlo methods can be used to estimate uncertainties and relationships between variables in complex systems

Select and analyze data objects from integrated energy systems

Determine inputs, desired outputs, and desired outputs of integrated energy systems

Unfolding energy supply data using MC methods

Verify the validity of extended data through hypothesis testing

SBM-DEA analysis based on extended data

Obtain power supply efficiency and slack variables of integrated energy systems

Energy supply optimization and efficiency analysis.

Integrated energy system refers to the use of advanced physical information technology and innovative management models in a certain field to integrate multiple energy sources, etc.

Data preprocessing

Indicator selection

Expand the data based on the MC method to increase the scale of the data and ensure the accuracy of the experiment

30 sample data expanded to 100 sample data

Monte Carlo Simulation (MC): Extended Sample Data

SBM-DEA: Establishing a power supply efficiency evaluation model for an integrated energy system with input, desired output and undesired output

The proposed method can identify decision-making units (DMUs) with good and bad performance with a high degree of discrimination, thereby obtaining better efficiency groupings.

This model has positive implications for energy efficiency management of integrated energy systems. When the number of samples increases, the energy supply efficiency of the integrated energy system will be concentrated around 0.9

By rationally allocating the slack variables of inputs, desired outputs, and undesired outputs, the ineffective supply of integrated energy systems can be realized effectively, thereby improving energy supply efficiency and environmental benefits.

Future work will consider the impact of clean energy sources (such as photovoltaics and wind energy) on integrated energy system energy efficiency and energy storage costs to enable a more comprehensive assessment of integrated energy system energy supply efficiency