Energy-saving amount calculation method for energy system energy-saving reconstruction

Abstract

The application provides an energy-saving quantity calculation method for energy-saving reconstruction of a use energy system, comprising the following steps: collecting daily energy consumption data and daily influence factor data of the use energy system during operation in the reconstructed use energy system, and establishing an energy consumption regression model based on the collected data; selecting at least one day as a benchmark test day in each month after the reconstruction is completed, and operating according to the operation mode before the reconstruction on the benchmark test day to obtain benchmark energy consumption data and benchmark influence factor data; substituting the benchmark influence factor data into the energy consumption regression model to obtain corresponding virtual energy consumption data after the reconstruction; calculating the energy-saving rate corresponding to the benchmark test day based on the benchmark energy consumption data and the virtual energy consumption data; obtaining the actual cumulative total energy consumption of the system during the month according to the operation after the reconstruction, and calculating the total energy-saving quantity in the month according to the energy-saving rate. The method does not need to rely on historical energy consumption data before the reconstruction, and realizes accurate calculation of the energy-saving quantity.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving technology for energy-consuming systems, and specifically relates to a method for calculating energy-saving retrofits of energy-consuming systems. Background Technology

[0002] Currently, energy saving calculations for building energy-saving renovation projects, both domestically and internationally, are typically based on standards and regulations such as the "Technical Specification for Energy-Saving Renovation of Public Buildings" (JGJ176), the "General Technical Rules for Energy Saving Measurement and Verification" (GB / T 28750), and the International Programme for Measurement and Certification of Energy Efficiency (IPMVP). These methods usually use historical energy consumption data before the renovation and its influencing factors (such as weather, operating load, equipment capacity, and operating time) as a benchmark, determining energy savings by comparing energy consumption measured before and after the project implementation.

[0003] According to the "General Technical Rules for Energy Saving Measurement and Verification" (GB / T 28750), existing technologies typically establish a correlation model between calibrated energy consumption (energy consumption of a building under its current condition without energy-saving measures) and its influencing factors based on historical energy consumption data before the renovation, using methods such as regression analysis. That is, a calculation model between energy consumption and influencing factors is first established using historical energy consumption data before the renovation. Then, the influencing factors under the current operating conditions after the renovation are substituted into this model to obtain the energy consumption of the building under its current operating conditions without energy-saving renovation. Comparing this energy consumption with the actual energy consumption after the renovation yields the project's energy savings.

[0004] However, in practical engineering applications, many existing buildings lack pre-renovation energy consumption data and operational records, making the aforementioned traditional energy-saving calculation methods difficult to implement. To address this issue, existing technologies often use the post-renovation operating conditions as the standard operating conditions. The influencing factors after the renovation are substituted into a regression model fitted from the pre-renovation data to calculate the building's energy consumption under the current operating conditions without energy-saving measures—that is, the adjusted baseline energy consumption. By comparing this adjusted baseline energy consumption with the actual energy consumption after the renovation, the energy savings can be determined.

[0005] While the above methods can calculate energy savings to some extent, they are highly dependent on the completeness and accuracy of pre-renovation data, and the model's applicability and generalization ability are limited. When pre-renovation data is missing, operating conditions fluctuate significantly, or the characteristics of the building system change significantly, the accuracy of the baseline energy consumption calculation is insufficient, the reliability of the energy saving results is low, and it is difficult to meet the requirements for high-precision and high-robustness energy saving measurement and verification. Summary of the Invention

[0006] This invention provides a method for calculating energy savings in energy-saving retrofits of energy-consuming systems. This method does not rely on historical energy consumption data before the retrofit and can accurately calculate the amount of energy saved.

[0007] The technical solution of the present invention is as follows:

[0008] A method for calculating energy savings in energy-saving retrofits of energy-consuming systems includes the following steps:

[0009] S1: In the modified energy system, daily energy consumption data and daily influencing factor data are continuously collected during the operation of the energy system. Based on the collected daily energy consumption data and daily influencing factor data, an energy consumption regression model of the energy system is established. The energy consumption regression model is used to characterize the functional relationship between daily energy consumption and daily influencing factors.

[0010] S2: In each natural month after the renovation is completed, at least one day is selected as the benchmark test day. On the benchmark test day, the energy system is controlled to operate in the operating mode before the renovation, and the benchmark energy consumption data and the corresponding benchmark influencing factor data on the benchmark test day are measured and obtained.

[0011] S3: Substitute the benchmark influencing factor data into the energy consumption regression model to calculate the virtual energy consumption data corresponding to the energy system operating in the modified mode under the benchmark influencing factor data.

[0012] S4: Based on the baseline energy consumption data and the virtual energy consumption data, calculate the energy saving rate corresponding to the baseline test day, and use the energy saving rate as the monthly energy saving rate of the natural month;

[0013] S5: Obtain the actual cumulative total energy consumption of the energy system operating in the modified mode during the natural month, and calculate the total energy saving during the natural month based on the energy saving rate.

[0014] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, the daily influencing factors and the benchmark influencing factors respectively include outdoor meteorological parameters and / or system operating parameters. The outdoor meteorological parameters include at least one of outdoor temperature, outdoor humidity or solar radiation illuminance, and the system operating parameters include at least one of equipment operating time, equipment load rate, equipment capacity or heating and cooling load.

[0015] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, before establishing the energy consumption regression model in step S1, a data preprocessing step is also included:

[0016] Clean the continuously collected daily energy consumption data and daily influencing factors, and remove bad data caused by equipment failure or abnormal data transmission;

[0017] The cleaned data is normalized or standardized to unify the dimensions of different influencing factors.

[0018] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, the energy consumption regression model is a multiple regression model.

[0019] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, step S2, when selecting the benchmark test day, also includes a working condition matching step:

[0020] Obtain the baseline influencing factor data for the benchmark test date, and compare the baseline influencing factor data with the distribution of influencing factor data for the same period in history before the renovation;

[0021] If the baseline influencing factor data exceeds the standard deviation range of the historical data distribution for the same period before the modification, the baseline test day is determined to be an abnormal operating condition day, and a new baseline test day is selected for measurement.

[0022] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, the formula for calculating the energy-saving rate in step S4 is as follows:

[0023] Energy saving rate = (baseline energy consumption data - virtual energy consumption data) / baseline energy consumption data × 100%.

[0024] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, the method for calculating the total monthly energy saving in step S4 is as follows:

[0025] Total energy saved = Actual cumulative total energy consumption in the current month × [Energy saving rate / (1 - Energy saving rate)].

[0026] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, the energy-consuming system includes one or more combinations of air conditioning system, ventilation system or domestic hot water system.

[0027] Furthermore, in the energy-saving calculation method for the energy-saving renovation of the energy-consuming system, if the types of energy used by the energy-consuming system before and after the renovation are different, the calculation method also includes a unified energy conversion step:

[0028] Based on the preset energy conversion standard, the benchmark energy consumption data, virtual energy consumption data, and actual cumulative total energy consumption are uniformly converted into standard coal units or uniform cost units before calculating the energy saving rate and total energy saving amount.

[0029] Furthermore, the energy-saving calculation method for the energy-saving renovation of the energy-consuming system includes a total energy saving correction step after calculating the total energy saving for the month.

[0030] Obtain the random interference factors that affect the energy consumption of the energy system in the current month. The random interference factors include unplanned equipment maintenance time or special holiday operation time.

[0031] The actual cumulative total energy consumption for the month is corrected based on the random interference factors to eliminate the impact of abnormal operating periods on the calculation of total energy savings.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention discloses a method for calculating energy savings in energy-saving retrofits of energy-consuming systems. By leveraging the regression relationship between the daily energy consumption and daily influencing factors of the retrofitted system, combined with measured data (including influencing factors and energy consumption under the pre-retrofit model) on a monthly baseline day, the virtual energy consumption of the retrofitted system under the baseline test day's operating conditions is calculated. Then, by comparing the energy consumption before and after the retrofit on the baseline day, the monthly energy saving rate is obtained. Finally, by combining this with the actual total energy consumption after the retrofit for the month, the monthly energy savings are calculated. This method achieves accurate quantification of energy savings without requiring historical data from before the retrofit, through back-calculation using monthly baseline measurements and a retrofitted energy consumption regression model. It has significant advantages such as wide applicability, high calculation accuracy, short settlement cycle, and strong operability, and can be widely applied to various energy-saving retrofit projects. Attached Figure Description

[0034] Figure 1 This is a flowchart of a method for calculating energy savings in an energy-saving retrofit of an energy-consuming system according to the present invention;

[0035] Figure 2 This is a flowchart illustrating the calculation method for energy saving in energy-saving retrofitting of an energy-consuming system according to the present invention.

[0036] Figure 3 This is a curve comparing the measured and calculated daily power consumption values ​​of Embodiment 1 of the present invention;

[0037] Figure 4 This is a curve comparing the measured and calculated daily power consumption values ​​of Embodiment 2 of the present invention. Detailed Implementation

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0039] like Figure 1 As shown in the figure, this embodiment provides a method for calculating the energy saving of an energy-saving system retrofit, which includes the following steps: S1-S5.

[0040] S1: In the modified energy system, daily energy consumption data and daily influencing factor data are continuously collected during the system's operation. Based on the collected daily energy consumption data and daily influencing factor data, an energy consumption regression model is established for the energy system. This energy consumption regression model is used to characterize the functional relationship between daily energy consumption and daily influencing factors. The energy consumption regression model is preferably a multiple regression model, specifically a multiple linear regression model, which can simultaneously consider the effects of multiple influencing factors on energy consumption, thus improving the model's fitting accuracy.

[0041] Furthermore, before establishing the energy consumption regression model, a data preprocessing step is also included: cleaning the continuously collected daily energy consumption data and daily influencing factors, and removing bad data caused by equipment failure or abnormal data transmission; normalizing or standardizing the cleaned data to unify the dimensions of different influencing factors, avoid model fitting deviation due to differences in quantity, and improve the calculation accuracy of the energy consumption regression model.

[0042] S2: In each natural month after the renovation is completed, select at least one day as the benchmark test day. On the benchmark test day, control the energy system to operate in the operating mode before the renovation, and measure and obtain the benchmark energy consumption data and the corresponding benchmark influencing factor data on the benchmark test day.

[0043] Furthermore, the selection of the benchmark test day also includes a working condition matching step: obtaining benchmark influencing factor data for the benchmark test day and comparing the benchmark influencing factor data with the distribution of influencing factor data for the same period in history before the modification; if the benchmark influencing factor data exceeds the standard deviation range of the distribution of data for the same period in history before the modification, the benchmark test day is determined to be an abnormal working condition day, and a new benchmark test day is selected for measurement. This step can ensure the consistency between the benchmark working condition and the normal working condition before the modification, reducing the impact of working condition differences on energy saving calculation.

[0044] S3: Substitute the baseline influencing factor data into the energy consumption regression model to calculate the virtual energy consumption data corresponding to the energy system operating in the modified mode under the baseline influencing factor data. The purpose of the virtual energy consumption data is to simulate the energy consumption level of the modified operating mode under the baseline operating conditions, providing a unified operating condition benchmark for comparison with the baseline energy consumption data.

[0045] S4: Based on the baseline energy consumption data and the virtual energy consumption data, calculate the energy saving rate corresponding to the baseline test day, and use the energy saving rate as the monthly energy saving rate for the natural month. The formula for calculating the energy saving rate is: Energy saving rate = (Baseline energy consumption data - Virtual energy consumption data) / Baseline energy consumption data × 100%.

[0046] S5: Obtain the actual cumulative total energy consumption of the energy system operating under the modified operation mode within the natural month, and calculate the total energy saving within the natural month based on the energy saving rate. The calculation method for the total energy saving in the current month is: Total energy saving = Actual cumulative total energy consumption in the current month × [Energy saving rate / (1 - Energy saving rate)].

[0047] like Figure 2 As shown, the specific calculation process is as follows:

[0048] Step 1: Establish a regression model for energy consumption after the upgrade. In the upgraded energy system, collect daily energy consumption data and corresponding daily influencing factor data (e.g., weather, operating load, equipment capacity, operating time, etc.), and establish a multiple linear regression model:

[0049]

[0050] In the formula, This refers to the daily energy consumption data after the energy system upgrade; This is daily data on influencing factors.

[0051] Step 2: Select a baseline test day and collect baseline data. Select one day each month as the baseline test day, disable energy-saving measures, operate the system according to the pre-modification mode, and measure the baseline energy consumption data for that day. and benchmark influencing factor data .

[0052] Step 3: Calculate virtual energy consumption data. This involves using the baseline influencing factor data from the benchmark test date. Substituting the data into the modified energy consumption regression model, the virtual energy consumption data would be calculated if the system were to operate in the modified manner. :

[0053] .

[0054] Step 4: Calculate the monthly energy saving rate. This is based on the baseline energy consumption data from the baseline date. and virtual energy consumption data Calculate the energy saving rate R (i.e., the monthly energy saving rate) corresponding to the benchmark test date:

[0055]

[0056] Step 5: Calculate the total energy savings for the month. This is based on the actual cumulative total energy consumption of the energy-consuming system operating under the modified mode for the month (excluding the baseline test date). Calculate the total energy saved in the current month. :

[0057] .

[0058] If the energy system does not implement energy-saving measures in the current month, calculate the energy consumption before the renovation. :

[0059] ,

[0060] Calculate the total energy saved in the current month :

[0061] .

[0062] The above method calculates the virtual energy consumption of the modified energy system under the benchmark test day conditions by combining the regression relationship between the daily energy consumption and daily influencing factors of the modified energy system with the measured data of the monthly benchmark day (energy consumption of the pre-modification mode and its influencing factors). Then, by comparing the energy consumption before and after the modification on the benchmark day, the monthly energy saving rate is obtained. Finally, by combining the actual total energy consumption after the modification in the month, the total energy saving for the month is calculated. This method eliminates the dependence on historical energy consumption data before the modification, improves the accuracy, stability and versatility of energy saving calculation, and can be widely applied to various energy-saving modification projects.

[0063] In a preferred embodiment, the influencing factors include outdoor meteorological parameters and / or system operating parameters. The outdoor meteorological parameters include at least one of outdoor temperature, outdoor humidity, or solar radiation intensity. The system operating parameters include at least one of equipment operating time, equipment load rate, equipment capacity, or heating and cooling load. By comprehensively collecting various influencing factors, the accuracy of the energy consumption regression model is ensured, further improving the accuracy of energy saving calculations.

[0064] In a preferred embodiment, the energy-consuming system includes one or more combinations of an air conditioning system, a ventilation system, or a domestic hot water system. This calculation method is applicable to various common energy-consuming systems such as air conditioning, ventilation, and domestic hot water, and is highly versatile and widely applicable.

[0065] As a preferred implementation, if the types of energy used by the energy systems before and after the modification are different, the calculation method also includes a unified energy conversion step:

[0066] Based on a preset energy conversion standard, the baseline energy consumption data, virtual energy consumption data, and actual cumulative total energy consumption are uniformly converted to standard coal units or a unified cost unit before calculating the energy saving rate and total energy saving. The preset energy conversion standard is the "General Rules for Comprehensive Energy Consumption Calculation" (GB / T 2589). This step avoids calculation deviations caused by different energy types and ensures the comparability of calculation results.

[0067] As a preferred implementation, after calculating the total energy savings for the month, a total energy savings correction step is also included:

[0068] Obtain the random interference factors that affect the energy consumption of the energy system in the current month. The random interference factors include unplanned equipment maintenance time or special holiday operation time.

[0069] The actual cumulative total energy consumption for the month is corrected based on the random interference factors to eliminate the impact of abnormal operating periods on the calculation of total energy savings.

[0070] This step further improves the accuracy of energy saving calculations.

[0071] Example 1: Energy-saving renovation of a refrigeration room in an office building

[0072] This embodiment takes the overall energy-saving renovation of a refrigeration room in an office building as an example to illustrate the specific implementation process of the method of the present invention in detail.

[0073] The office building is located in a region with hot summers and cold winters, with a building area of ​​approximately 106,000 square meters. The original refrigeration room used fixed-frequency centrifugal chillers, which were replaced with variable-frequency centrifugal chillers after the renovation, and a group control strategy based on load forecasting was added.

[0074] Step S1: Establish a regression model for energy consumption after the modification.

[0075] After the renovation was completed, operational data was collected continuously for 31 days (excluding the baseline date), including daily power consumption of the chiller room, daily cumulative cooling load of the air conditioning system, and daily average outdoor dry-bulb temperature. Based on the collected daily energy consumption data and daily influencing factor data, a multiple linear regression model was established as follows:

[0076] E n =3451-28.3t+0.239q

[0077] In the formula, E n The power consumption of the chiller room on that day (kW·h); The average outdoor dry-bulb temperature (°C) for the day. The cumulative cooling load for the day is (kW·h).

[0078] Statistical analysis showed that the model had a coefficient of determination (R²) of 0.99, indicating a highly quantifiable linear relationship between the energy consumption of the modified system and the influencing factors.

[0079] Step S2: Base Date Testing

[0080] The 15th of the month was selected as the baseline test date. On the baseline test date, the chiller room was operated in the pre-modification mode (i.e., variable frequency control and group control strategy were turned off), and the following measurements were obtained:

[0081] Baseline energy consumption data Q base The actual power consumption on that day was 21,620 kWh.

[0082] Baseline influencing factor data: Daily average outdoor dry-bulb temperature (t) base =24.2℃, daily cumulative cooling load q base =61532kWh.

[0083] Step S3: Calculate virtual energy consumption data

[0084] The benchmark influencing factor data (t) base =24.2℃, q base Substituting (61532 kWh) into the energy consumption regression model established in step S1, the virtual energy consumption data Q corresponding to the operation of the chiller room according to the modified operating mode under the aforementioned baseline influencing factor data is calculated. calc :

[0085] Q calc =3451-28.3×24.2+0.239×61532=17472.3kWh.

[0086] Step S4: Calculate the energy saving rate

[0087] Based on the baseline energy consumption data and the virtual energy consumption data, the instantaneous energy saving rate corresponding to the baseline test day is calculated, and this energy saving rate is used as the monthly energy saving rate for the natural month.

[0088] η=(21620−17472.3) / 21620×100%=19.18%

[0089] Step S5: Calculate the total energy saved for the month

[0090] The system operates in the upgraded mode for the remaining 30 days of the month (excluding the base date of the 15th). Obtain the actual cumulative total energy consumption E of the system operating in the upgraded mode during this calendar month. actual =419355 kWh. The total energy saved for the month was calculated based on the stated energy saving rate.

[0091] E=E actual ×(η / 1−η)=419355×[0.1918 / (1−0.1918)]=99520kWh

[0092] That is, the office building's refrigeration room saved approximately 99,500 kWh of electricity that month through energy-saving renovations.

[0093] Model validation:

[0094] Substitute the daily influencing factor data for the current month into the above regression model to calculate the daily electricity consumption, and compare it with the actual measured value for the day (excluding the base day). Figure 3 As shown. Figure 3The discontinuities in the data represent baseline test day data and are not used for model validation. The comparison results show that the calculated daily power consumption for the month is very close to the measured value, with the error generally within 3%, further validating the model's high accuracy.

[0095] Example 2: Energy-saving renovation of a hotel's domestic hot water system (conversion from an oil-fired boiler to an air source heat pump)

[0096] This embodiment uses the energy-saving renovation of a hotel's domestic hot water system as an example to demonstrate the application of the present invention in a multi-energy type switching scenario.

[0097] The hotel originally used two oil-fired boilers to produce hot water for domestic use and the swimming pool. Due to aging equipment, the boilers' operating efficiency was below 80%. Now, an air-source heat pump has been used to replace the oil-fired boilers as the hot water source, changing the energy type from oil to electricity.

[0098] Step S1: Establish a regression model for energy consumption after modification

[0099] After the renovation, operational data of the air source heat pump system was continuously collected for 30 days (excluding the baseline day), including daily power consumption, daily domestic hot water heat consumption, and daily average outdoor dry-bulb temperature. Based on the collected data, a multiple linear regression model was established as follows:

[0100] E n =350-21.8t+0.29q

[0101] In the formula, E n t represents the daily power consumption of the air source heat pump (kW·h); t represents the daily average outdoor dry-bulb temperature (°C); and q represents the daily domestic hot water consumption (kW·h).

[0102] Statistical analysis showed that the model had a coefficient of determination (R²) of 0.97, indicating a good linear relationship between the energy consumption of the modified air source heat pump system and the influencing factors.

[0103] Step S2: Base Date Testing

[0104] The 19th of the month was selected as the baseline test date. On this date, the air source heat pump was turned off (energy-saving measure), and the hot water system was operated in the pre-modification mode (i.e., the oil-fired boiler was activated). Measurements were taken to obtain:

[0105] Baseline energy consumption data Q base Daily fuel consumption = 0.55 tons;

[0106] Baseline influencing factor data: Daily average outdoor dry-bulb temperature (t) base =14.5℃, daily domestic hot water consumption q base =5721kWh.

[0107] Step S3: Calculate virtual energy consumption data

[0108] The benchmark influencing factor data (t) base =14.5℃, q base Substituting (=5721kWh) into the energy consumption regression model established in step S1, the virtual energy consumption data Q corresponding to operation in the modified mode (air source heat pump) under the aforementioned baseline influencing factor data is calculated. calc :

[0109] Q calc =350-21.8×14.5+0.29×5721=1693kWh.

[0110] Unified energy conversion

[0111] Because the energy types used by the energy systems before and after the renovation are different (fuel oil vs. electricity), a unified conversion is performed according to the "General Rules for Calculating Comprehensive Energy Consumption" GB / T 2589:

[0112] Fuel oil to standard coal equivalent coefficient: 1 kg fuel oil = 1.4571 kgce;

[0113] Electricity conversion to standard coal equivalent: 1 kWh = 0.1229 kgce.

[0114] Baseline energy consumption (fuel oil) converted to standard coal:

[0115] Q base_ce =550 × 1.4571 = 801.41 kgce

[0116] Virtual energy consumption (electricity) converted to standard coal:

[0117] Q calc_ce =1693×0.1229=208.07kgce.

[0118] Step S4: Calculate the energy saving rate

[0119] Based on the converted standard coal units, the instantaneous energy saving rate corresponding to the benchmark test day is calculated, and this energy saving rate is used as the monthly energy saving rate for the natural month.

[0120] η=(801.41−208.07) / 801.41×100%=74.04%

[0121] Step S5: Calculate the total energy saved for the month

[0122] Except for the base date (19th), the system operated in the modified mode (air source heat pump) for the remaining 29 days of the month. Obtain the actual cumulative total energy consumption (electricity) E for the month. actual=40632kWh. First, convert to standard coal: E actual_ce =40632×0.1229=4993.67kgce.

[0123] The total energy savings (standard coal equivalent) for the month are calculated based on the stated energy saving rate.

[0124] E ce =E actual_ce ×(η / 1−η)=4993.67×[0.7407 / (1−0.7407)] =14264.6kgce.

[0125] That is, the hotel's domestic hot water system saved approximately 14.26 tons of standard coal that month.

[0126] Model validation:

[0127] Substitute the daily influencing factor data for the month into the air source heat pump regression model to calculate the daily power consumption, and compare it with the actual measured value for that day (excluding the baseline day). Figure 4 As shown. Figure 4 The discontinuities in the data represent the baseline test day. The comparison results show that the calculated daily power consumption for the month is very close to the measured value, with the error generally within 3%, and an R² of 0.97, indicating that the model has good predictive accuracy.

[0128] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A method for calculating energy savings in energy-saving retrofits of energy-consuming systems, characterized in that, Includes the following steps: S1: In the modified energy system, daily energy consumption data and daily influencing factor data are continuously collected during the operation of the energy system. Based on the collected daily energy consumption data and daily influencing factor data, an energy consumption regression model of the energy system is established. The energy consumption regression model is used to characterize the functional relationship between daily energy consumption and daily influencing factors. S2: In each natural month after the renovation is completed, at least one day is selected as the benchmark test day. On the benchmark test day, the energy system is controlled to operate in the operating mode before the renovation, and the benchmark energy consumption data and the corresponding benchmark influencing factor data on the benchmark test day are measured and obtained. S3: Substitute the benchmark influencing factor data into the energy consumption regression model to calculate the virtual energy consumption data corresponding to the energy system operating in the modified mode under the benchmark influencing factor data. S4: Based on the baseline energy consumption data and the virtual energy consumption data, calculate the energy saving rate corresponding to the baseline test day, and use the energy saving rate as the monthly energy saving rate of the natural month; S5: Obtain the actual cumulative total energy consumption of the energy system operating in the modified mode during the natural month, and calculate the total energy saving during the natural month based on the energy saving rate.

2. The energy saving calculation method for energy-saving retrofitting of energy-consuming systems as described in claim 1, characterized in that, The daily influencing factors and the baseline influencing factors respectively include outdoor meteorological parameters and / or system operating parameters. The outdoor meteorological parameters include at least one of outdoor temperature, outdoor humidity or solar radiation illuminance. The system operating parameters include at least one of equipment operating time, equipment load rate, equipment capacity or heating and cooling load.

3. The energy saving calculation method for energy-saving retrofitting of energy-consuming systems as described in claim 1, characterized in that, Before establishing the energy consumption regression model in step S1, a data preprocessing step is also included: Clean the continuously collected daily energy consumption data and daily influencing factors, and remove bad data caused by equipment failure or abnormal data transmission; The cleaned data is normalized or standardized to unify the dimensions of different influencing factors.

4. The energy saving calculation method for energy-saving retrofitting of energy-consuming systems as described in claim 1, characterized in that, The energy consumption regression model is a multiple regression model.

5. The energy saving calculation method for energy-saving retrofitting of energy-consuming systems as described in claim 1, characterized in that, In step S2, when selecting the benchmark test date, a working condition matching step is also included: Obtain the baseline influencing factor data for the benchmark test date, and compare the baseline influencing factor data with the distribution of influencing factor data for the same period in history before the renovation; If the baseline influencing factor data exceeds the standard deviation range of the historical data distribution for the same period before the modification, the baseline test day is determined to be an abnormal operating day, and a new baseline test day is selected for measurement.

6. The energy saving calculation method for energy-saving retrofitting of an energy-consuming system as described in claim 1, characterized in that, The formula for calculating the energy saving rate in step S4 is as follows: Energy saving rate = (baseline energy consumption data - virtual energy consumption data) / baseline energy consumption data × 100%.

7. The energy saving calculation method for energy-saving retrofitting of energy-consuming systems as described in claim 1, characterized in that, The method for calculating the total energy saving amount for the current month in step S4 is as follows: Total energy saved = Actual cumulative total energy consumption in the current month × [Energy saving rate / (1 - Energy saving rate)].

8. The method for calculating energy savings in energy-saving retrofitting of an energy-consuming system as described in claim 1, characterized in that, The energy-consuming system includes one or more combinations of air conditioning system, ventilation system or domestic hot water system.

9. The method for calculating energy savings in energy-saving retrofitting of an energy-consuming system as described in claim 1, characterized in that, If the energy types used by the energy systems before and after the renovation are different, the calculation method also includes a unified energy conversion step: Based on the preset energy conversion standard, the benchmark energy consumption data, virtual energy consumption data, and actual cumulative total energy consumption are uniformly converted into standard coal units or uniform cost units before calculating the energy saving rate and total energy saving amount.

10. The method for calculating energy savings in energy-saving retrofitting of an energy-consuming system as described in claim 1, characterized in that, After calculating the total energy savings for the month, the process also includes a total energy savings correction step: Obtain the random interference factors that affect the energy consumption of the energy system in the current month. The random interference factors include unplanned equipment maintenance time or special holiday operation time. The actual cumulative total energy consumption for the month is corrected based on the random interference factors to eliminate the impact of abnormal operating periods on the calculation of total energy savings.

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