CN113033864A - Energy consumption prediction method for newly built enterprise based on energy big data - Google Patents
Energy consumption prediction method for newly built enterprise based on energy big data Download PDFInfo
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Abstract
The invention provides a newly-built enterprise energy consumption prediction method based on energy big data, which comprises a newly-built enterprise energy consumption prediction system, wherein the newly-built enterprise energy consumption prediction system comprises an enterprise energy consumption database module, a prediction enterprise input module, an energy consumption data matching module and an energy consumption prediction production module. Establishing an enterprise energy utilization database, researching and surveying the energy utilization enterprises to be tested, predicting the energy utilization requirements of the energy utilization enterprises to be tested, and summarizing the processes to predict the energy utilization of the newly-built enterprises by various load statistics of the energy utilization enterprises to be tested. According to the method, the energy consumption enterprise to be tested is comprehensively analyzed, deep enterprise cognition is performed, an enterprise characteristic model is further established, the energy consumption requirement of the industrial enterprise is matched with the characteristic model and is associated, the prediction basis and the information source of the industrial energy consumption requirement are greatly expanded, and the energy consumption requirement of the enterprise is accurately mastered; the method has the advantages that the enterprise energy big data are established and used, the energy big data technology is applied to the prediction of the industrial energy demand, and the prediction accuracy of the industrial enterprise energy demand is greatly improved.
Description
Technical Field
The invention relates to the field of energy consumption prediction of new enterprises, in particular to a new enterprise energy consumption prediction method based on energy big data.
Background
The influence factors of the industrial energy load are numerous, the influence factors are related to the industrial type and the capacity scale and also related to the production process and the production characteristics, and the accuracy of predicting the industrial energy load of electric power, gas, industrial steam and the like of a newly-built enterprise is always a difficult point of comprehensive energy planning and electric power/gas/heat special energy planning. The traditional prediction method is based on industrial types (two-class and three-class industries), industrial land area and energy consumption indexes to predict load, which often causes large deviation and even multiple deviation.
The traditional prediction method does not consider factors such as the industry type, the capacity scale, the production process, the energy consumption characteristics and the production characteristics of industrial enterprises, and simultaneously does not fully utilize advanced technologies such as energy big data, so that the industrial energy consumption prediction has larger deviation. The scale construction of energy supply facilities is generally large, the utilization rate of the energy facilities is seriously low, most of the energy facilities are between 20 and 50 percent, and the energy supply facilities are wasted, the investment economy is poor and even the investment fails.
The method has the advantages that the client cognition on the industrial enterprise in the traditional mode is insufficient, the industrial type, the process characteristics, the energy consumption characteristics, the production characteristics and the like of the enterprise cannot be fully mastered, and the industrial energy consumption prediction is separated from the enterprise reality; the traditional industrial energy demand prediction deviation is large, so that the resource waste such as large construction scale of energy facilities at an energy supply end, low utilization rate of the energy facilities, high investment and the like is caused.
Disclosure of Invention
Aiming at the problem of large energy consumption prediction deviation of the existing enterprise energy consumption prediction method, the invention provides a new enterprise energy consumption prediction method based on energy big data.
The invention adopts the following technical scheme:
a newly-built enterprise energy consumption prediction method based on energy big data comprises a newly-built enterprise energy consumption prediction system, wherein the newly-built enterprise energy consumption prediction system comprises an enterprise energy consumption database module, a prediction enterprise input module, an energy consumption data matching module and an energy consumption prediction production module;
the energy consumption prediction method comprises the following steps:
step 1: establishing an enterprise energy utilization database;
the method adopts a field investigation method to systematically collect comprehensive information of enterprise energy consumption of different types and scales all over the country, and establishes an enterprise energy consumption database;
step 2: enterprise research and investigation of energy consumption to be tested;
the method comprises the steps of carrying out on-site investigation on energy consumption enterprise information to be detected aiming at the energy consumption enterprise to be detected, and establishing an enterprise characteristic model according to the energy consumption enterprise information to be detected, wherein the enterprise characteristic model comprises the location of the enterprise, the type of the enterprise, the scale of the enterprise, energy consumption characteristics and production characteristics;
inputting the enterprise characteristic model into an energy consumption data matching module through a prediction enterprise input module;
and step 3: forecasting the energy consumption requirement of the energy consumption enterprise to be tested;
matching the enterprise characteristic model with information in an enterprise energy utilization database module by an energy utilization data matching module, and predicting energy utilization data of an energy utilization enterprise to be tested;
and 4, step 4: various loads of the energy utilization enterprises to be tested are counted and summarized;
and generating an energy utilization data table of the energy utilization enterprises to be tested by the energy utilization predicting production module according to the energy utilization data of the energy utilization enterprises to be tested predicted by the energy utilization data matching module.
Preferably, the enterprise energy database module comprises an industry type information unit, the industry type information unit comprises energy utilization information of various industry types, and the energy utilization information of each industry type comprises energy utilization characteristic information, production characteristic information, enterprise scale information and enterprise location information of each industry type.
Preferably, the energy consumption data matching module matches the enterprise characteristic model with information in the enterprise energy consumption database module, and the specific process of predicting the energy consumption data of the energy consumption enterprise to be tested is as follows:
step 3.1: screening enterprises in the enterprise energy utilization database module through the industrial type information;
screening all enterprises in the enterprise energy consumption database module which are consistent with the industry type of the enterprise to be tested according to the matching of the industry type in the enterprise characteristic model and the industry type information unit in the enterprise energy consumption database module;
step 3.2: further screening the enterprises screened out in the step 3.1 by using the energy characteristic information;
according to the matching of the energy utilization characteristics in the enterprise characteristic model and the energy utilization characteristic information of the enterprises screened in the step 3.1, further screening the enterprises which accord with the energy utilization characteristics of the enterprises to be tested;
step 3.3: further screening the enterprises screened out in the step 3.2 through the production characteristic information;
according to the matching of the production characteristics in the enterprise characteristic model and the production characteristic information of the enterprise screened in the step 3.2, further screening out the enterprise which is consistent with the energy utilization characteristics of the enterprise to be tested;
step 3.4: further screening the enterprises screened out in the step 3.3 through enterprise scale information;
according to the matching of the enterprise scale in the enterprise characteristic model and the enterprise scale information of the enterprise screened in the step 3.3, further screening out the enterprise which is consistent with the enterprise scale of the enterprise to be tested;
step 3.5: further screening the enterprises screened out in the step 3.4 through the information of the locations of the enterprises;
according to the matching of the enterprise location in the enterprise characteristic model and the enterprise location information of the enterprise screened in the step 3.4, further screening out the enterprise corresponding to the enterprise location of the enterprise to be tested;
step 3.6: and predicting the energy utilization data of the to-be-tested energy utilization enterprises according to the energy utilization data of the enterprises screened in the step 3.5.
Preferably, the industry type information unit includes energy use information of petrochemical enterprises, energy use information of fine chemical enterprises, energy use information of food processing enterprises, energy use information of biomedical enterprises, and energy use information of equipment manufacturing enterprises;
the energy utilization characteristic information comprises continuous energy utilization, intermittent energy utilization and energy utilization quality;
the production characteristic information comprises continuous production, intermittent production and annual production time;
the enterprise scale information comprises enterprise floor area, factory building area and enterprise capacity;
the information of the location of the enterprise comprises province, city and county of the enterprise.
The invention has the beneficial effects that:
according to the newly-built enterprise energy consumption prediction method based on the energy big data, deep enterprise cognition is performed by comprehensively analyzing the energy consumption enterprise to be detected, an enterprise characteristic model is further established, the industrial enterprise energy consumption demand is matched with the characteristic model and a correlation is established, the industrial energy consumption demand prediction basis and the information source are greatly expanded, and the enterprise energy consumption appeal is accurately mastered; the method has the advantages that enterprise energy big data are established and used, an energy big data technology is applied to industrial energy demand prediction, a target enterprise energy consumption characteristic model is combined, recent matching is achieved, the accuracy of industrial enterprise energy consumption demand prediction is greatly improved, industrial energy consumption prediction deviation is effectively reduced, further, the scale of energy facilities is reasonably built, the utilization rate of the energy facilities is improved, and investment waste is avoided.
Detailed Description
The following further illustrates embodiments of the invention by way of specific examples:
a new enterprise energy consumption prediction method based on energy big data comprises a new enterprise energy consumption prediction system, wherein the new enterprise energy consumption prediction system comprises an enterprise energy consumption database module, a prediction enterprise input module, an energy consumption data matching module and an energy consumption prediction production module.
The energy consumption prediction method comprises the following steps:
step 1: establishing an enterprise energy utilization database;
the method of on-site research is adopted to systematically collect comprehensive information of enterprise energy consumption of different types and scales across the country, and an enterprise energy consumption database is established.
The establishment and the improvement of the enterprise energy consumption database are a continuous and long-term process, the enterprise data of different regions, different industrial types, different enterprise scales, different energy consumption characteristics and production characteristics need to be continuously improved, and the enterprise energy consumption prediction work can be accurately guided after a considerable amount of data support is provided.
The enterprise energy consumption database module comprises an industry type information unit, the industry type information unit comprises energy consumption information of various industry types, and the energy consumption information of each industry type comprises energy consumption characteristic information, production characteristic information, enterprise scale information and enterprise location information of the respective industry type.
The industry type information unit comprises the energy utilization information of enterprises such as petrochemical industry, fine chemical industry, food processing, biomedicine, equipment manufacturing, textile clothing, wood processing, furniture manufacturing, paper making, printing and dyeing, rubber and plastic, automobile manufacturing and the like.
The energy consumption characteristics information includes energy consumption type (such as steam, hot water, gas, electric power, air separation gas and the like), energy consumption load (maximum load, minimum load, average load and the like), energy consumption quality (pressure, temperature, purity and the like), load curve (such as typical daily load curve, monthly load curve and the like), accumulated load (annual energy consumption total), energy consumption process (such as energy consumption section, dosage process and the like), energy consumption (such as heating, combustion, drying, sterilization and the like) and the like.
The production characteristic information includes continuous production (e.g., 24 hours), intermittent production (e.g., 8 hours), order type production (production characteristics fluctuate dynamically depending on the number of orders), stable production (capacity stable), fluctuating production (capacity fluctuating), annual production time, overhaul and production stop time, and the like.
The enterprise scale information includes enterprise floor space, factory building space (factory building floor space, building space, etc.), enterprise energy production (such as annual product yield (ten thousand tons, ten thousand square meters, etc.), annual output value (hundred million yuan), etc.), annual energy consumption total amount (such as annual consumption standard coal amount, annual power consumption total amount, etc.), etc.
The information of the location of the enterprise comprises the province, the city (district) and the county of the enterprise, the climate condition, the temperature and the humidity of the location and the like.
Step 2: enterprise research and investigation of energy consumption to be tested;
the accuracy of the energy consumption load prediction is directly determined by the information of the energy consumption enterprises to be measured and the collection degree of the information of the energy consumption enterprises to be measured, so the investigation and investigation work at the stage should be as detailed as possible.
And establishing an enterprise characteristic model according to the information of the energy consumption enterprise to be tested, wherein the enterprise characteristic model comprises the location of the enterprise, the type of the enterprise, the scale of the enterprise, the energy consumption characteristics and the production characteristics.
The content contained in the enterprise characteristic model is corresponding to the information in the enterprise energy database for subsequent matching.
And inputting the enterprise characteristic model into the energy consumption data matching module through the prediction enterprise input module.
And step 3: forecasting the energy consumption requirement of the energy consumption enterprise to be tested;
and the energy consumption data matching module matches the enterprise characteristic model with the information in the enterprise energy consumption database module, and predicts the energy consumption data of the energy consumption enterprise to be tested.
The method specifically comprises the following steps:
step 3.1: screening enterprises in the enterprise energy utilization database module through the industrial type information;
screening all enterprises in the enterprise energy consumption database module which are consistent with the industry type of the enterprise to be tested according to the matching of the industry type in the enterprise characteristic model and the industry type information unit in the enterprise energy consumption database module;
for example, the to-be-tested energy consumption enterprise is a petrochemical enterprise, and all petrochemical enterprises are screened out from the enterprise energy consumption database module.
Step 3.2: further screening the enterprises screened out in the step 3.1 by using the energy characteristic information;
according to the matching of the energy utilization characteristics in the enterprise characteristic model and the energy utilization characteristic information of the enterprises screened in the step 3.1, further screening the enterprises which accord with the energy utilization characteristics of the enterprises to be tested;
for example, if the energy consumption characteristics of the energy consumption enterprises to be measured are 2.0MPa and 250 ℃ steam and continuous energy consumption (i.e. the typical daily load curve is a straight line), the petrochemical enterprises with continuous energy consumption matched with the energy consumption characteristics are further screened out from the screened petrochemical enterprises.
Step 3.3: further screening the enterprises screened out in the step 3.2 through the production characteristic information;
according to the matching of the production characteristics in the enterprise characteristic model and the production characteristic information of the enterprise screened in the step 3.2, further screening out the enterprise which is consistent with the energy utilization characteristics of the enterprise to be tested;
for example, if the production characteristics of the energy-consuming enterprises to be measured is 24 hours of continuous production, the petrochemical enterprises which are continuous production and use energy are further screened out from the screened-out petrochemical enterprises which use energy continuously.
Step 3.4: further screening the enterprises screened out in the step 3.3 through enterprise scale information;
according to the matching of the enterprise scale in the enterprise characteristic model and the enterprise scale information of the enterprise screened in the step 3.3, further screening out the enterprise which is consistent with the enterprise scale of the enterprise to be tested;
for example, if the enterprise scale of the energy-consuming enterprise to be measured is 200 ten thousand tons of oil refining capacity per year, the petrochemical enterprise of continuous production, in which the enterprise scale is 200 ten thousand tons of oil refining capacity per year, is further selected from the screened petrochemical enterprises of continuous energy consumption for continuous production.
Step 3.5: further screening the enterprises screened out in the step 3.4 through the information of the locations of the enterprises;
according to the matching of the enterprise location in the enterprise characteristic model and the enterprise location information of the enterprise screened in the step 3.4, further screening out the enterprise corresponding to the enterprise location of the enterprise to be tested;
for example, if the location of the enterprise to be tested is city a, the annual average temperature of city a is 12.7 ℃, and the annual average relative humidity is 73%, then the enterprise selected in step 3.4 is further selected to be city a or an enterprise similar to city a in terms of weather conditions and the like.
Step 3.6: and predicting the energy utilization data of the to-be-tested energy utilization enterprises according to the enterprise energy utilization data screened in the step 3.5.
For example, the screened enterprise energy data, the energy-to-be-measured enterprise information, and the energy data are shown in tables 1 and 2:
TABLE 1 Enterprise energy data (represented by steam)
TABLE 2 energy data of the enterprise to be tested (steam as representative)
Through the table 1, the energy consumption load of the enterprise to be measured is predicted to be 15 tons/hour in the table 2, and the annual steam consumption is 11.25 ten thousand tons.
And 4, step 4: various loads of the energy utilization enterprises to be tested are counted and summarized;
and generating an energy utilization data table of the energy utilization enterprises to be tested by the energy utilization predicting production module according to the energy utilization data of the energy utilization enterprises to be tested predicted by the energy utilization data matching module.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A newly-built enterprise energy consumption prediction method based on energy big data is characterized by comprising a newly-built enterprise energy consumption prediction system, wherein the newly-built enterprise energy consumption prediction system comprises an enterprise energy consumption database module, a prediction enterprise input module, an energy consumption data matching module and an energy consumption prediction production module;
the energy consumption prediction method comprises the following steps:
step 1: establishing an enterprise energy utilization database;
the method adopts a field investigation method to systematically collect comprehensive information of enterprise energy consumption of different types and scales all over the country, and establishes an enterprise energy consumption database;
step 2: enterprise research and investigation of energy consumption to be tested;
the method comprises the steps of carrying out on-site investigation on energy consumption enterprise information to be detected aiming at the energy consumption enterprise to be detected, and establishing an enterprise characteristic model according to the energy consumption enterprise information to be detected, wherein the enterprise characteristic model comprises the location of the enterprise, the type of the enterprise, the scale of the enterprise, energy consumption characteristics and production characteristics;
inputting the enterprise characteristic model into an energy consumption data matching module through a prediction enterprise input module;
and step 3: forecasting the energy consumption requirement of the energy consumption enterprise to be tested;
matching the enterprise characteristic model with information in an enterprise energy utilization database module by an energy utilization data matching module, and predicting energy utilization data of an energy utilization enterprise to be tested;
and 4, step 4: various loads of the energy utilization enterprises to be tested are counted and summarized;
and generating an energy utilization data table of the energy utilization enterprises to be tested by the energy utilization predicting production module according to the energy utilization data of the energy utilization enterprises to be tested predicted by the energy utilization data matching module.
2. The method for predicting energy consumption of new enterprises based on energy big data as claimed in claim 1, wherein the enterprise energy consumption database module comprises an industry type information unit, the industry type information unit comprises energy consumption information of various industry types, and the energy consumption information of each industry type comprises energy consumption characteristic information, production characteristic information, enterprise scale information and enterprise location information of the respective industry type.
3. The energy consumption prediction method for the newly built enterprise based on the energy big data as claimed in claim 2, wherein the energy consumption data matching module is used for matching the enterprise characteristic model with the information in the enterprise energy consumption database module, and the specific process of predicting the energy consumption data of the energy consumption enterprise to be tested is as follows:
step 3.1: screening enterprises in the enterprise energy utilization database module through the industrial type information;
screening all enterprises in the enterprise energy utilization database module which are consistent with the industrial types of the enterprises to be tested according to the matching of the industrial types in the enterprise characteristic model and the industrial type information unit in the enterprise energy utilization database module;
step 3.2: further screening the enterprises screened out in the step 3.1 by using the energy characteristic information;
according to the matching of the energy utilization characteristics in the enterprise characteristic model and the energy utilization characteristic information of the enterprise screened in the step 3.1, further screening out the enterprise which is in accordance with the energy utilization characteristics of the enterprise to be tested;
step 3.3: further screening the enterprises screened out in the step 3.2 through the production characteristic information;
according to the matching of the production characteristics in the enterprise characteristic model and the production characteristic information of the enterprise screened in the step 3.2, further screening out the enterprise which is consistent with the energy utilization characteristics of the enterprise to be tested;
step 3.4: further screening the enterprises screened out in the step 3.3 through enterprise scale information;
according to the matching of the enterprise scale in the enterprise characteristic model and the enterprise scale information of the enterprise screened in the step 3.3, further screening out the enterprise which is consistent with the enterprise scale of the enterprise to be tested;
step 3.5: further screening the enterprises screened out in the step 3.4 through the information of the locations of the enterprises;
according to the matching of the enterprise location in the enterprise characteristic model and the enterprise location information of the enterprise screened in the step 3.4, further screening out the enterprise corresponding to the enterprise location of the enterprise to be tested;
step 3.6: and predicting the energy utilization data of the to-be-tested energy utilization enterprises according to the energy utilization data of the enterprises screened in the step 3.5.
4. The energy consumption prediction method for the newly built enterprise based on the energy big data as claimed in claim 2,
the industry type information unit comprises energy information of petrochemical enterprises, energy information of fine chemical enterprises, energy information of food processing enterprises, energy information of biomedical enterprises and energy information of equipment manufacturing enterprises;
the energy utilization characteristic information comprises continuous energy utilization, intermittent energy utilization and energy utilization quality;
the production characteristic information comprises continuous production, intermittent production and annual production time;
the enterprise scale information comprises enterprise floor area, factory building area and enterprise capacity;
the information of the location of the enterprise comprises province, city and county of the enterprise.
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