CN113112245A

CN113112245A - Intelligent management method, system and storage medium for multi-level energy consumption quota of public institution

Info

Publication number: CN113112245A
Application number: CN202110481192.2A
Authority: CN
Inventors: 闫军威; 陈城; 何敏; 刘玲燕; 许志鑫; 曾超逸
Original assignee: Guangzhou I Mec Intelligent Technology Co ltd
Current assignee: Guangzhou I Mec Intelligent Technology Co ltd
Priority date: 2021-04-30
Filing date: 2021-04-30
Publication date: 2021-07-13

Abstract

The invention discloses a method, a system and a storage medium for intelligent management of public institution multi-level energy consumption quota, which are characterized in that public institution energy consumption index data are obtained, the public institution energy consumption index data and public institution energy consumption quota indexes of the types of local areas belong to the public institution are subjected to benchmarking analysis to obtain benchmarking results, the benchmarking results are subjected to multi-level classification summarizing analysis to obtain classification analysis results, and energy consumption quota decision can be performed on the classification analysis results, so that the intelligent management of the public institution energy consumption quota whole process is realized, the development of public institution energy consumption quota work is effectively promoted, the utilization efficiency of energy resources is improved, and the contribution is made for realizing carbon peak reaching and carbon neutralization.

Description

Intelligent management method, system and storage medium for multi-level energy consumption quota of public institution

Technical Field

The invention relates to the field of data analysis and processing, in particular to a method, a system and a storage medium for intelligent management of multi-level energy consumption quota of a public institution.

Background

At present, the state administration will implement the energy-saving target management mode of combining the double control of the total energy consumption and the intensity with the quota in the range of the whole country, promote the public institutions in the whole country to comprehensively improve the utilization efficiency of energy resources, and further increase the difficulty of energy-saving management. Because the energy consumption rating index related to different public institution types has large index difference, different administrative region level indexes are arranged differently, and in the energy consumption rating execution process, the problems of difficult energy consumption rating supervision, difficult energy consumption rating guidance, difficult energy consumption rating tracking and the like exist in each level of public institutions, energy-saving administrative departments and industry administrative departments of the state level-provincial level-prefectural level-energy consumption unit, and the like, so that the development of the energy consumption rating work is difficult to be effectively promoted.

The above problems are in need of improvement.

Disclosure of Invention

In view of the above problems, an object of the present invention is to provide a method, a system and a storage medium for intelligent management of multi-level energy consumption quota for a public institution, so as to implement intelligent management of the whole process of energy consumption quota for the public institution, effectively promote the development of energy consumption quota work for the public institution, improve the utilization efficiency of energy resources, and make due contribution to carbon peak reaching and carbon neutralization.

The invention provides a public institution multi-level energy consumption quota intelligent management method, which comprises the following steps:

acquiring energy consumption index data of a public institution;

performing benchmarking analysis according to the public institution energy consumption index data and the public institution energy consumption quota index of the type of the local area to obtain a benchmarking result;

carrying out multi-level classification and summary analysis according to the benchmarking result to obtain a classification analysis result;

and carrying out energy consumption quota decision according to the classification analysis result, and outputting a decision result.

In this scheme, the acquiring of the energy consumption index data of the public institution specifically includes:

acquiring public institution energy consumption related data;

and calculating energy consumption indexes according to the acquired relevant data of the public institution energy consumption to obtain the energy consumption of the public institution unit building area, the power consumption of the unit building area, the heating energy consumption of the unit heating area, the non-heating energy consumption of the unit building area, the per-capita comprehensive energy consumption, the per-capita non-heating energy consumption, the per-capita power consumption, the per-capita water consumption and the energy use efficiency data of the data center machine room.

In this scheme, the benchmarking analysis is performed according to the data of the energy consumption indexes of the public institutions and the energy consumption quota indexes of the public institutions of the type to which the local area belongs to obtain benchmarking results, which specifically include:

acquiring a constraint value, a reference value and a guide value of an energy consumption quota index of a public institution of the type to which the local area belongs;

carrying out weight allocation on the constraint value, the reference value and the guide value according to a preset period;

and performing benchmarking analysis of each preset period on the energy consumption index data according to the apportioned constraint value, the reference value and the guide value to obtain a benchmarking analysis result of each preset period.

In this scheme, the weight sharing is performed on the constraint value, the reference value and the guidance value according to a preset period, specifically:

determining the energy consumption ratio of each reporting period in a statistical year;

calculating the energy consumption quota index constraint value a distributed in each reporting period in a statistical year_iReference value b_iLeading value c_i(ii) a Constraint value a_i＝a*θ_iReference value b_i＝b*θ_iLeading value c_i＝c*θ_i；

Wherein, the average value is calculated according to a preset rule to obtain theta_i. For example, the energy consumption ratio of four seasons is calculated according to the average ratio of the seasons of the previous j years, and is respectivelyθ₁，θ₂，θ₃，θ₄And theta₁+θ₂+θ₃+θ₄＝1。

performing annual energy consumption index prediction calculation of the public institution according to a preset prediction rule;

and carrying out prediction benchmarking on annual energy consumption index prediction data according to the constraint value, the reference value and the guide value to obtain a prediction benchmarking result.

In this scheme, the multi-level classification and summary analysis is performed according to the bidding result to obtain a classification analysis result, which specifically includes:

and carrying out multi-level classification and collection on the benchmarking result data of the public institution according to a preset classification rule to obtain a classification result.

And performing ranking analysis, same-ratio analysis and/or ring-ratio analysis on the energy consumption quota target classification result of each level according to a preset statistical rule to obtain an analysis result.

In this scheme, the performing energy consumption quota decision according to the classification analysis result specifically includes:

calculating the public institution energy-saving potential of each level;

decomposing energy-saving reduction targets in different quota level ranges of each hierarchy according to the assigned energy-saving targets and the energy-saving potentials;

and performing gradient decision according to the energy-saving potential and the decomposed energy-saving reduction target.

In the scheme, the gradient decision is that for the public institutions exceeding the constraint value, the larger the energy consumption index distance constraint value is, the larger the distributed energy-saving reduction rate is; the smaller the distance constraint value, the smaller the assigned energy saving reduction rate.

The second aspect of the invention discloses an intelligent management system for multi-level energy consumption quota of public institution, which comprises: the intelligent management method comprises a memory and a processor, wherein the memory comprises a program of the intelligent management method for the multi-level energy consumption quota of the public institution, and the program of the intelligent management method for the multi-level energy consumption quota of the public institution realizes the following steps when being executed by the processor:

acquiring energy consumption index data of a public institution;

acquiring public institution energy consumption related data;

In this scheme, the performing weight distribution on the constraint value, the reference value, and the guidance value of each hierarchy according to a preset period specifically includes:

Wherein, the average value is calculated according to a preset rule to obtain theta_i. For example, the energy consumption ratio of four quarters is calculated according to the average ratio of quarters of the previous j years, and is respectively theta₁，θ₂，θ₃，θ₄And theta₁+θ₂+θ₃+θ₄＝1。

In the scheme, benchmarking analysis is carried out according to the data of the energy consumption indexes of the public institutions and the energy consumption quota indexes of the public institutions of the type of the local area to obtain benchmarking results, and the benchmarking results specifically comprise the following steps:

calculating the public institution energy-saving potential of each level;

In a third aspect, the present invention discloses a computer-readable storage medium, where the computer-readable storage medium includes a program of an intelligent management method for public institution multi-level energy consumption quota, and when the program of the intelligent management method for public institution multi-level energy consumption quota is executed by a processor, the steps of the intelligent management method for public institution multi-level energy consumption quota as described in any one of the above are implemented.

According to the intelligent management method, system and storage medium for the multi-level energy consumption quota of the public institution, the prediction result is obtained through calculation and analysis of the multi-level energy consumption related data of the public institution, the energy consumption quota decision can be carried out on the prediction result, the intelligent management of the whole process of the energy consumption quota of the public institution is realized, the development of the energy consumption quota work of the public institution is effectively promoted, the utilization efficiency of energy resources is improved, and due contribution is made for realizing carbon peak reaching and carbon neutralization.

Drawings

FIG. 1 illustrates a flow chart of a method for intelligent management of institutional multi-tier energy consumption ratings of the present invention;

fig. 2 shows a block diagram of an intelligent management system for multi-level energy consumption rating of an institution according to the invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.

Fig. 1 shows a flow chart of an intelligent management method for multi-level energy consumption quota of an institution according to the invention.

As shown in fig. 1, the invention discloses an intelligent management method for multi-level energy consumption quota of public institution, comprising the following steps:

s101, acquiring energy consumption index data of a public institution;

s102, performing benchmarking analysis according to the public institution energy consumption index data and the public institution energy consumption quota index of the type of the local area to obtain a benchmarking result;

s103, carrying out multi-level classification and summary analysis according to the benchmarking result to obtain a classification analysis result;

and S104, carrying out energy consumption quota decision according to the classification analysis result, and outputting a decision result.

According to the embodiment of the present invention, the acquiring of the energy consumption index data of the public institution specifically includes:

acquiring public institution energy consumption related data;

The acquiring of the public institution energy consumption data refers to acquiring energy consumption related data information of the terminal energy consumption unit. The energy use unit refers to a national institution, institution or group that uses financial funds in whole or in part. The energy consumption related data acquisition comprises the acquisition of basic information data of energy consumption units and the acquisition of various energy consumption information data. The energy consumption unit basic information data acquisition comprises energy consumption unit names, public institution types, public institution subdivision types, building areas, energy consumption people numbers and the like. The acquisition of various energy consumption information data of the energy consumption unit comprises the real-time acquisition of various energy consumption quantities of the terminal energy consumption unit, such as electricity, natural gas, gasoline, diesel oil, heating power and the like, and the energy consumption quantities in each period can also be acquired from energy purchase bills in a national tax system.

It should be noted that, the calculation of the energy consumption index of the public institution refers to that the energy consumption unit of the terminal converts the energy consumption into standard coal uniformly through the acquired energy consumption and energy consumption unit information, and calculates various energy consumption quota indexes, which generally include but are not limited to indexes related to building area: energy consumption per unit building area, power consumption per unit building area, heating energy consumption per unit heating area, non-heating energy consumption per unit building area, and indexes related to energy consumption number: the energy consumption is synthesized per capita, the energy consumption is not supplied by the per capita, the power consumption is consumed per capita, the water consumption is consumed per capita, and the energy consumption related to the special energy consumption part is as follows: the energy utilization efficiency of the data center machine room and the like.

The calculation formula of the indexes related to the building area is as follows:

the energy consumption of the unit building area of the public institution is the total building energy consumption of the public institution/the total building area of the public institution within a preset range

The power consumption of the unit building area of the public institution is the total power consumption of the public institution/the total building area of the public institution within a preset range

Heating energy consumption of the public institution in unit heating area is equal to total heating energy consumption of the public institution/total heating area of the public institution within a preset range

The unit building area non-heating energy consumption is equal to the public institution non-heating energy consumption/the total non-heating area of the public institution within the preset range

Indexes related to energy consumption number:

the comprehensive energy consumption per person is the total energy consumption of the public institutions/the total energy consumption number of the public institutions within a preset range;

the average non-heating energy consumption of people is the total non-heating energy consumption of the public institutions/the total energy consumption number of the public institutions within a preset range;

the average power consumption per person is the total power consumption of the public institutions/the total energy consumption number of the public institutions in a preset range;

the average water consumption per person is the total water resource consumption of the public institution/the total energy consumption of the public institution within a preset range

Energy consumption related to special energy utilization part:

the energy utilization efficiency of the data center machine room is equal to total energy consumption of the public institution data center machine room/I T total equipment electricity consumption within a preset range.

According to the embodiment of the invention, the benchmarking analysis is performed according to the public institution energy consumption index data and the public institution energy consumption quota index of the type to which the local area belongs to obtain a benchmarking result, which specifically comprises the following steps:

The weight distribution of the constraint value, the reference value and the guide value of each level according to the preset period specifically comprises the following steps:

It should be noted that, the obtaining of the constraint value, the reference value and the guide value of the energy consumption rating index of the type of the local area affiliated public institution means obtaining the energy consumption rating index value of the type of the affiliated public institution from the energy consumption rating standard of the public institution issued by the local area, wherein the energy consumption rating standard of the public institution may be a state institution energy consumption rating standard, an energy consumption rating standard of a college and university, a hospital energy consumption rating standard, or an energy consumption rating standard covering partial types of the public institution; the energy consumption rating index can be a constraint value, a reference value and a guide value, and can also be an index value of a related rating level such as an advanced value and a pioneer value.

It is worth mentioning that according to the reporting period granularity, the energy consumption rating index constraint value, the reference value and the guide value of the type public institution to which the local area belongs are allocated according to the reporting period of the public institution to form the energy consumption rating index constraint value, the reference value and the guide value of the reporting period, so that the public institution is ensured to timely and accurately analyze benchmarks.

Example (c): the energy consumption rating index grade value of public institution energy consumption rating standard released in local area is a statistical period of year, and the reporting period is a quarter, so that the energy consumption rating grade value is divided into quarters before the rating analysis is carried out, and an energy consumption rating index dividing method is provided due to the difference of energy consumption intensity of quarters.

The energy consumption quota index apportionment method comprises the following steps:

step 1: determining the energy consumption ratio of each reporting period in a statistical year:

energy consumption index constraint value a, reference value b and guide value c of annual unit building area of national institution 'public institution energy consumption quota Standard' of a certain province, wherein a is more than b and more than c, the reporting period is that energy consumption data are reported once every quarter, the energy consumption occupation ratios of four quarters are calculated according to the average occupation ratio of the quarters of the previous j years, and are respectively theta₁，θ₂，θ₃，θ₄Wherein theta₁+θ₂+θ₃+θ₄＝1，θ_i＝E_{Season i}/∑E_{Season i}Wherein i is quarterly 1,2,3,4, E_{Season i}Is the average of the i th quarter of the previous j years.

Step 2: calculating the energy consumption quota index constraint value a distributed in each reporting period in a statistical year_iReference value b_iLeading value c_i

Constraint value a_i＝a*θ_iReference value b_i＝b*θ_iLeading value c_i＝c*θ_i

And 3, step 3: and carrying out benchmarking analysis on the energy consumption index and the energy consumption quota index value of the reporting period to determine which energy consumption quota level the energy consumption index of the reporting period is in.

The energy consumption index of the unit building area calculated in the ith reporting period is e, and the e and the energy consumption rating grade value (the constraint value a) are compared_iReference value b_iLeading value c_i) And carrying out calibration to obtain a calibration amount range.

Four energy consumption rating level ranges: greater than the constraint value, greater than the reference value, less than the constraint value, greater than the guide value, less than the reference value, and less than the guide value.

It should be noted that the predicted energy consumption index includes an energy consumption index per unit building area and an energy consumption index per capita, total building energy consumption, non-heating energy consumption prediction, power consumption prediction, water consumption prediction, and the like; the step also comprises the steps of calculating the energy consumption index of the unit building area and the per-capita energy consumption index, wherein the energy consumption index related to the building area and the energy consumption index related to the number of the energy consumption people comprise the energy consumption of the unit building area, the power consumption of the unit building area, the heating energy consumption of the unit heating area, the non-heating energy consumption of the unit building area, the per-capita comprehensive energy consumption, the per-capita non-heating energy consumption, the per-capita power consumption and the per-capita water consumption. Wherein, the energy consumption index of unit building area is the total annual energy consumption Q forecast/building area; the index of the energy consumption per person is the total energy consumption Q prediction/energy consumption number of the whole year.

The public institution energy consumption index prediction means that the total energy consumption of the whole statistical year is predicted according to the energy consumption data of the elapsed time of the statistical year and the energy consumption data of the previous year or the energy consumption data change trend or the energy consumption average value of the recent years, the energy consumption unit energy consumption index is calculated according to the predicted total energy consumption of the whole statistical year, the energy consumption unit energy consumption index is aligned with the public institution energy consumption rating index of the same type of unit corresponding to the public institution energy consumption rating standard issued in a local area, the energy consumption rating level of the energy consumption unit in the future year is judged, and the condition that the early warning public institution cannot reach the target rating level range is tracked in time.

The specific prediction method is as follows:

the method includes the steps that firstly, energy consumption data of the terminal energy consumption unit of elapsed time in the current year are obtained, and the energy consumption data can be 1 month or more, 1 quarter or more. The energy consumption data may be a comprehensive energy consumption, a building energy consumption, a heating energy consumption, a non-heating energy consumption or a water consumption.

②

Calculating predicted energy consumption index

Energy consumption index of unit building area is total annual energy consumption Q_PredictionArea of building

The index of the comprehensive energy consumption per capita is total annual energy consumption Q_PredictionEnergy consumption.

Predicting benchmarking

And obtaining the type of the public institution of the terminal energy consumption unit and the energy consumption quota index value (constraint value, reference value and guide value) of the public institution of the corresponding type of the local area, and carrying out benchmarking on the calculated predicted energy consumption index unit building area energy consumption and per capita comprehensive energy consumption and the model of the public institution energy consumption quota index value (constraint value, reference value and guide value) of the public institution of the type of the local area to obtain a benchmarking quota horizontal range.

For example: the method comprises the steps that a certain college belongs to the education category of education institutions higher in the local public institution energy consumption rate standard, energy consumption rate index values (constraint values, reference values and guide values) of the local education institution higher education category are obtained, the energy consumption index predicted by the college is subjected to benchmarking with the energy consumption rate index values (constraint values, reference values and guide values) of the education institution higher education category corresponding to the rate standard, and the range of four rate levels that the energy consumption index predicted by the college is larger than the constraint value, larger than the reference value, smaller than the constraint value, larger than the guide value, smaller than the reference value and smaller than the guide value is obtained.

According to the embodiment of the present invention, the multi-level classification and summary analysis is performed according to the benchmarking result to obtain a classification analysis result, which specifically comprises:

It should be noted that the classified collection of bidding results by the multi-level public institution refers to classified collection of bidding results at four levels, namely, district level, prefecture level, province level, and country level.

The benchmarking result classifying and summarizing refers to classifying and summarizing benchmarking analysis results and classifying and summarizing predicted benchmarking results in each preset period. The benchmarking result comprises classification and collection of different rating level ranges corresponding to different energy consumption indexes, classification and collection of different rating level ranges corresponding to different public institution types, and classification and collection of different rating level ranges corresponding to different areas.

The classified collection of the different rated level ranges corresponding to the different energy consumption indexes refers to the classified collection of the energy consumption indexes related to the building area and the energy consumption indexes related to the energy consumption number, including but not limited to unit building area energy consumption, unit building area power consumption, unit heating area heating energy consumption, unit building area non-heating energy consumption, per-capita comprehensive energy consumption, per-capita non-heating energy consumption, per-capita power consumption and per-capita water consumption, which correspond to the different rated level ranges (greater than a constraint value, greater than a reference value, less than a constraint value, greater than a guide value, less than a reference value and less than a guide value).

The different public institution types comprise public institution primary classification and secondary classification, and even smaller fine classification, the public institution primary classification comprises a party administration organ, an institution with energy use characteristics similar to the party administration organ, an education institution, a health and medical institution, a venue institution and other institutions, and the secondary classification can be divided according to the administration level or the building area size; education institutions can be divided according to higher education, medium education, elementary education, preschool education, other education and the like, and the higher education can be subdivided according to synthesis, worker arrangement, financial and the like; the health and medical institutions can be divided according to comprehensive hospitals, special hospitals, basic medical treatment and other medical institutions and the like, and can also be divided according to the third level, the second level and the first level; venues can be divided into science and technology venues, cultural venues, sports venues, etc. The classified collection of the different quota level ranges corresponding to the different institution types refers to that all institution types divided in the institution energy consumption quota standard issued locally are classified and collected respectively corresponding to the different quota level ranges (larger than a constraint value, smaller than a reference value, larger than a guide value, smaller than the reference value, and smaller than the guide value).

The classified collection of the different quota level ranges corresponding to the different regions refers to that the regions are refined according to actual conditions or all the public institutions of the regions divided in the public institution energy consumption quota standard issued by referring to the local regions are classified and collected respectively corresponding to the different quota level ranges (larger than a constraint value, larger than a reference value, smaller than the constraint value, larger than a guide value, smaller than the reference value and smaller than the guide value). For example: the classified collection of the local cities refers to the classified collection of the public institutions in the d regions according to corresponding different quota horizontal ranges (larger than a constraint value, smaller than a reference value, larger than a guide value, smaller than a reference value and smaller than a guide value).

The classified collection refers to classified collection of all dimensions according to energy consumption indexes, types of public institutions, divided areas and rated level ranges.

The classified analysis of the public institution energy consumption quota benchmarks comprises four levels of benchmarks, namely, regional level, prefecture level, provincial level and national level.

The district-county level energy consumption quota benchmarking classification analysis indicates that the public institution in the district performs classification ranking analysis and statistical analysis according to different energy consumption quota level ranges (larger than a constraint value, larger than a reference value, smaller than a reference value, and smaller than a guide value) of different energy consumption indexes, each type of public institution in the district performs classification ranking analysis and statistical analysis according to different energy consumption quota level ranges (larger than a constraint value, larger than a reference value, smaller than a constraint value, larger than a guide value, and smaller than a guide value) of different energy consumption indexes, each subdivision type of public institution in the district performs classification ranking analysis and statistical analysis according to different energy consumption quota level ranges (larger than a constraint value, larger than a reference value, smaller than a reference value, and smaller than a guide value) of different energy consumption indexes, and the analysis granularity can perform monthly analysis, statistical analysis, and statistical analysis, Quarterly analysis, annual analysis, and energy consumption quota versus class analysis for any specified time period.

The provincial-city-level energy consumption quota benchmarking is characterized in that the provincial-city-level energy consumption quota benchmarking is used for carrying out classified ranking analysis and statistical analysis according to different energy consumption quota level ranges (larger than a constraint value, smaller than a constraint value, larger than a guide value, smaller than a reference value and smaller than a guide value) of cities in a district according to different energy consumption indexes, each type of public institution in the district carries out classified ranking analysis and statistical analysis according to different energy consumption quota level ranges (larger than the constraint value, smaller than the reference value, smaller than the constraint value, larger than the reference value and smaller than the guide value) of different energy consumption indexes, each subdivision type in the district carries out classified analysis and statistical analysis according to different energy consumption quota level ranges (larger than the constraint value, smaller than the guide value and smaller than the guide value) of different energy consumption indexes, each type in the district carries out classified analysis and statistical, greater than the reference value and less than the constraint value, greater than the guide value and less than the guide value), performing classified ranking analysis and statistical analysis by each subdivision type public institution in each district according to different energy consumption indexes and different energy consumption quota level ranges (greater than the constraint value, greater than the reference value and less than the constraint value, greater than the guide value and less than the guide value), and performing monthly analysis, quarterly analysis, annual analysis and energy consumption quota classification analysis in any specified time period according to the reporting period.

The national level energy consumption quota analysis indicates that the classification ranking analysis and statistical analysis are carried out on each province and city of the jurisdiction according to different energy consumption indexes and different energy consumption quota level ranges (larger than a constraint value, larger than a reference value, smaller than a reference value, larger than a guide value, and smaller than a guide value) in the jurisdiction, each type of public institution of the jurisdiction carries out the classification ranking analysis and statistical analysis according to different energy consumption quota level ranges (larger than the constraint value, larger than the reference value, smaller than the constraint value, larger than the guide value, and smaller than the guide value) in the jurisdiction, each subdivision type of the jurisdiction carries out the classification analysis and statistical analysis according to different energy consumption indexes and different energy consumption quota level ranges (larger than the constraint value, larger than the reference value, smaller than the guide value, and smaller than the guide value) in the jurisdiction, each type of the jurisdiction carries out the classification analysis and statistical analysis according to different energy consumption quota, greater than the reference value and less than the constraint value, greater than the guide value and less than the guide value), performing classified ranking analysis and statistical analysis by each subdivision type public institution of provinces and cities in the jurisdiction according to different energy consumption indexes and different energy consumption quota level ranges (greater than the constraint value, greater than the reference value and less than the constraint value, greater than the guide value and less than the guide value), and performing monthly analysis, quarterly analysis, annual analysis and energy consumption quota target classification analysis in any specified time period according to the reporting period.

According to the embodiment of the present invention, the performing the energy consumption rating decision according to the classification analysis result specifically includes:

calculating the public institution energy-saving potential of each level;

decomposing energy-saving reduction targets of different quota level intervals of each level according to the assigned energy-saving targets and the energy-saving potentials;

The sub-gradient decision is that for public institutions exceeding a constraint value, the larger the energy consumption index distance constraint value is, the larger the distributed energy-saving reduction rate is; the smaller the distance constraint value, the smaller the assigned energy saving reduction rate.

The energy consumption quota decision module is used for guiding and deciding the energy-saving working direction by combining the energy-saving reduction target and the energy consumption quota.

The multi-level energy consumption quota decision module of the public institution comprises four levels of energy consumption quota decision of district level, prefecture level, province level and country level.

The energy consumption quota decision mainly comprises the following steps:

step 1, evaluating the energy-saving potential of public institutions in administrative districts of all levels.

Obtaining the energy-saving potential evaluation of the public institution in the jurisdiction according to the benchmarking result, comprising the following steps:

energy saving amount when the district public institution reaches QD value ∑ energy consumption index of unit building area of the k-th class subdivision public institution in the district-unit building area energy consumption QD value of the same type institution) × building area of target institution

The QD value is a constraint value, a guide value or a reference value; k represents a division type of a public institution, and includes a state institution, a political institution, each type of educational institution, each type of medical institution, each type of venue, and others.

R1 is the ratio of the energy saving amount to the total energy consumption of the district public institution when the district public institution actually exceeds the constraint value and the constraint value is planned to be reached

Energy savings when the jurisdictional utility reaches the constraint value/total energy consumption of the jurisdictional utility.

R2 is the ratio of the energy saving amount to the total energy consumption of the district public institution when the district public institution is actually in the range of the constraint value and the reference value and the plan reaches the reference value

(energy saving when the jurisdictional institution reaches the reference value-

Energy saving when the jurisdiction institution reaches a constraint value

Total energy consumption of the jurisdictional institution.

R3 is the ratio of the energy saving amount when the district public institution is actually in the range of the reference value and the guide value and the planned guide value to the total energy consumption of the district public institution

(energy saving when the jurisdictional public institution reaches a lead value-energy saving when the jurisdictional public institution reaches a reference value)/total energy consumption of the jurisdictional public institution.

And 2, decomposing the energy-saving descending targets in different quota horizontal intervals according to the issued energy-saving target P. Calculating the occupation ratio beta of the energy saving amount to the total energy consumption ratio of the public institutions in the jurisdiction when the public institutions in the jurisdiction respectively exceed the constraint value and respectively reach the constraint value, the guide value and the reference value in the constraint value-reference value range and the reference value-guide value range_QD，QD＝1，2，3，β₁+β₂+β₃＝1，β_QD＝R_QD/∑R_QD

According to the difficulty difference that public institutions within the range of constraint value-reference value and within the range of reference value-guide value respectively reach the constraint value, the reference value and the guide value when the constraint value is exceeded, the difficulty coefficient gamma that the energy consumption index of one public institution reaches QD is given_QDEnsuring beta_1*γ₁+β_2*γ₂+β_3*γ₂Calculating the energy-saving reduction ratio in the range of decomposing different energy consumption quota as 1

P_QD＝P*β_QD*γ_QD

The energy saving reduction ratio in the range of different energy consumption ratings refers to the ratio of the required energy saving in the range of different energy consumption rating levels to the total energy consumption of all the public institutions in the range

Determining energy conservation degradation targets within different energy consumption rating levels

M_QD＝P_QDX Total energy consumption of all institutions in the range/Total energy consumption of institutions in the range of different quotients

QD is 1,2 and 3, which are respectively expressed as exceeding constraint values, in the 3 rd step in the constraint value-reference value range and in the reference value-guide value range, the constraint value exceeding constraint values of the jurisdiction public institutions can be uniformly distributed according to the decomposed energy-saving reduction targets in different quota ranges, all the public institution energy-saving reduction targets when the constraint values, the guide values and the reference values are respectively reached in the constraint value-reference value range and in the reference value-guide value range, or all the public institution energy-saving reduction targets when the constraint values, the guide values and the reference values are respectively reached in the constraint value-reference value range and in the reference value-guide value range can be distributed in a gradient manner, and all the public institution energy-saving reduction targets are respectively reached in the constraint value-reference value range and in the reference value-guide value range.

The step-by-step assignment energy-saving reduction target method means that,

the larger the energy consumption index of the public institution exceeding the constraint value is from the constraint value, the larger the distributed energy-saving reduction target is, and the smaller the distance constraint value is, the smaller the distributed energy-saving reduction target is.

Similarly, for the public institution in the range of the constraint value-reference value, the larger the energy consumption index is from the reference value, the larger the distributed energy saving reduction target is, and the smaller the distance reference value is, the smaller the distributed energy saving reduction target is.

The larger the energy consumption index of the public institution in the range of the reference value-guide value is from the guide value, the larger the assigned energy saving drop target is, and the smaller the distance guide value is, the smaller the assigned energy saving drop target is.

As shown in fig. 2, the present invention discloses an intelligent management system 2 for multi-level energy consumption quota of public institution, which comprises: a memory 21 and a processor 22, wherein the memory includes a program of the intelligent management method for the institution-based multi-level energy consumption rating, and when the program of the intelligent management method for the institution-based multi-level energy consumption rating is executed by the processor, the following steps are implemented:

acquiring energy consumption index data of a public institution;

acquiring public institution energy consumption related data;

Indexes related to energy consumption number:

Energy consumption related to special energy utilization part:

Step 2: calculating the distribution of each reporting period in a statistical yearEnergy consumption rating index constraint value a_iReference value b_iLeading value c_i

Four quotum level ranges: greater than the constraint value, greater than the reference value, less than the constraint value, greater than the guide value, less than the reference value, and less than the guide value.

The specific prediction method is as follows:

②

Calculating predicted energy consumption index

Predicting benchmarking

calculating the public institution energy-saving potential of each level;

The energy consumption quota decision mainly comprises the following steps:

(energy saving when the jurisdictional public institution reaches a reference value-energy saving when the jurisdictional public institution reaches a constraint value)/total energy consumption of the jurisdictional public institution.

According to the difficulty difference that public institutions within the range of constraint value-reference value and within the range of reference value-guide value respectively reach the constraint value, the reference value and the guide value when the constraint value is exceeded, the difficulty coefficient gamma that the energy consumption index of one public institution reaches QD is given_QDEnsuring beta₁*γ₁+β_2*γ₂+β₃*γ₂＝1

Calculating the energy-saving reduction ratio in the range of decomposing different energy consumption quota

P_QD＝P*β_QD*γ_QD

QD is expressed as exceeding a constraint value, within a constraint value-reference value range, and within a reference value-guide value range, respectively

And 3, according to the decomposed energy-saving descending targets in different quota ranges, uniformly distributing all public mechanism energy-saving descending targets of the jurisdiction when the public mechanisms in the jurisdiction respectively exceed the constraint value, in the constraint value-reference value range and in the reference value-guide value range respectively reach the constraint value, the guide value and the reference value, or distributing all public mechanism energy-saving descending targets of the jurisdiction respectively exceed the constraint value in a gradient manner and in the constraint value-reference value range and in the reference value-guide value range respectively reach the constraint value, the guide value and the reference value.

The step-by-step assignment energy-saving reduction target method means that,

According to the intelligent management method, system and storage medium for the multi-level energy consumption quota of the public institution, the prediction result is obtained through calculation and analysis of the multi-level energy consumption related data of the public institution, the energy consumption quota decision can be carried out on the prediction result, the intelligent management of the whole process of the energy consumption quota of the public institution is realized, the development of the energy consumption quota work of the public institution is effectively promoted, the utilization efficiency of energy resources is improved, and the due contribution is made for carbon dioxide emission reaching the peak value and realizing carbon neutralization.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical or in other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

Claims

1. An intelligent management method for multi-level energy consumption quota of a public institution is characterized by comprising the following steps:

acquiring energy consumption index data of a public institution;

2. The intelligent management method for the multi-level energy consumption quota of the public institution as claimed in claim 1, wherein the acquiring of the energy consumption index data of the public institution is specifically as follows:

acquiring public institution energy consumption related data;

3. The intelligent management method for the multi-level energy consumption quota of the public institution as claimed in claim 1, wherein the benchmarking analysis is performed according to the data of the energy consumption quota of the public institution and the energy consumption quota index of the public institution of the type to which the local area belongs to obtain benchmarking results, and specifically comprises:

4. The intelligent management method for the multi-level energy consumption quota of the public institution as claimed in claim 3, wherein the weight distribution of the constraint value, the reference value and the guidance value is performed according to a preset period, and specifically comprises:

calculating each in a statistical yearReporting the energy consumption quota index constraint value a distributed in the period_iReference value b_iLeading value c_i(ii) a Constraint value a_i＝a*θ_iReference value b_i＝b*θ_iLeading value c_i＝c*θ_i；

Wherein, the average value is calculated according to a preset rule to obtain theta_i。

5. The intelligent management method for the multi-level energy consumption quota of the public institution as claimed in claim 1, wherein the benchmarking is performed according to the data of the energy consumption quota of the public institution and the energy consumption quota index of the public institution of the type to which the local area belongs to obtain a benchmarking result, and specifically comprises:

6. The intelligent management method for the multi-level energy consumption quota of the public institution as claimed in claim 1, wherein the classification and collection analysis is performed on the benchmarking result in a multi-level classification and collection manner to obtain a classification and analysis result, specifically:

7. The intelligent management method for multi-level energy consumption quota of public institution as claimed in claim 1, wherein the decision for energy consumption quota according to the classification analysis result is specifically:

calculating the public institution energy-saving potential of each level;

8. The intelligent management method for the multi-level energy consumption quota of the public institution as claimed in claim 7, wherein the graded decision is that for the public institution exceeding the constraint value, the larger the energy consumption index is from the constraint value, the larger the assigned energy saving reduction rate is; the smaller the distance constraint value, the smaller the assigned energy saving reduction rate.

9. An intelligent management system for multi-level energy consumption quota for an institution, the system comprising: the intelligent management method comprises a memory and a processor, wherein the memory comprises a program of the intelligent management method for the multi-level energy consumption quota of the public institution, and the program of the intelligent management method for the multi-level energy consumption quota of the public institution realizes the following steps when being executed by the processor:

acquiring energy consumption index data of a public institution;

10. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a program of an intelligent management method of a multi-level energy consumption rating of a public institution, which when executed by a processor, implements the steps of a method of intelligent management of a multi-level energy consumption rating of a public institution as claimed in any one of claims 1 to 8.