Energy-saving behavior disassembly auxiliary management method based on output value energy consumption predictive analysis method

Abstract

The invention discloses an energy-saving behavior disassembly auxiliary management method based on an output value energy consumption prediction analysis method. The method comprises the following steps: S1, historical energy consumption analysis; s2, predicting productivity energy consumption; s3, the energy-saving indexes are disassembled; and S4, energy-saving behavior auxiliary monitoring is carried out. According to the energy-saving behavior disassembly auxiliary management system based on the output value energy consumption prediction analysis method, analysis can be carried out based on historical production line equipment-level energy consumption data, task disassembly is carried out on a periodic energy-saving target in a more refined and scientific manner according to the energy consumption condition, the energy consumption difference of the same type and the energy consumption ratio of different types, and the energy-saving behavior disassembly auxiliary management system has the advantages that the energy-saving behavior disassembly efficiency is improved. The management granularity is determined to the daily energy consumption condition of each device, so that managers can intervene in energy-saving management in a lower-cost and more efficient manner, the energy-saving management threshold is greatly reduced, the energy-saving management efficiency is improved, and a large amount of unnecessary energy waste is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular relates to an energy-saving behavior disassembly and auxiliary management method based on an output value energy consumption prediction and analysis method. Background Art

[0002] As the scale of power grids grows larger and fossil energy becomes increasingly depleted, clean energy such as wind, light, and gas are gradually added to traditional power grids, forming a comprehensive energy system that integrates multiple energy sources such as wind, light, electricity, and gas.

[0003] With the advancement of Industry 4.0 transformation, more and more industrial enterprises have begun to pay attention to industrial digital management, and energy saving is a very important management goal. At present, most energy saving is still based on the summary statistics of IoT data, assisted by human analysis and judgment, and energy saving is carried out through offline management. On the one hand, this method requires the access of personnel with professional experience, and on the other hand, energy saving management can only be followed up and adjusted periodically, which makes management inflexible and ineffective. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned technical problems existing in the prior art, and to provide an energy-saving behavior disassembly and auxiliary management method based on the output value energy consumption forecasting and analysis method. Based on the matching relationship between historical energy consumption and output value, and future production scheduling, the output value energy consumption forecasting algorithm is used to predict the energy consumption of each link of each production line in the future. After manually setting the total energy-saving target and cycle, the historical energy consumption proportion of each production line link is disassembled, and the upper limit of the daily energy consumption index of each product line is given, which can realize refined target control and monitoring.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] The energy-saving behavior disassembly auxiliary management method based on the output value energy consumption prediction analysis method is characterized by comprising the following steps:

[0007] S1. Historical energy consumption analysis: Collect monthly energy consumption data of facilities in different links of different production lines, label relevant functional production lines and facilities with the same functions, and analyze the average unit output value energy consumption of production lines with the same function and the average unit output value difference of production lines based on historical data.

[0008] S2. Capacity energy consumption forecast: Based on the monthly production schedule and output value energy consumption forecast method, predict the energy consumption of each production line and equipment in the future.

[0009] S3. Energy-saving index decomposition: artificially set the total energy-saving target and cycle, and then decompose the historical energy consumption proportion of each production line and each equipment to obtain the daily energy consumption forecast of each production line and each equipment.

[0010] S4. Auxiliary monitoring of energy-saving behavior: The daily energy consumption forecast of each device on each production line is used as the upper limit indicator for that day to achieve target management and control monitoring.

[0011] Furthermore, (1) production lines with the same functions are grouped together.

[0012] (2) The monthly standard output value of the same type of production line is X, and the specific value of each production line is: X1, X2, X3...Xn; the energy consumption is E, and the specific value of each production line is: E1, E2, E3...En, where n is a natural number.

[0013] (3) Calculate the average energy consumption per unit output value of similar production lines: Qt = (E1 / X1+E2 / X2+…En / Xn) / n; at the same time, calculate the average energy consumption per unit output value difference of a single production line: △E = En / Xn-Qt, where n is a natural number.

[0014] (4) Management personnel set energy-saving targets Zt within a certain period based on actual production conditions.

[0015] According to the production schedule, the energy-saving targets are broken down by the average unit output value energy consumption of different functional production lines in the total energy consumption. If there are production lines with the same functions involved, the targets are allocated and broken down according to Qt and △E, so that production lines with high energy consumption are allocated more energy-saving targets.

[0016] Further, 1) assuming that the production lines with the same functions are A1 and A2, and the third production line with a function different from that of production line A is B, QAt = (EA1 / XA1+EA2 / XA2) / 2; QBt = EB / XB; △EA1 = EA1 / XA1-QAt.

[0017] When the production schedule for the next month requires that production lines A1, A2, and B are all put into full production, the energy-saving target for production line A1 during the cycle can be broken down into: ZA1t=Zt*QAt / (QBt+2QAt)*(1+△EA1 / QAt*β); β is the energy-saving rectification completion control parameter, which is used to adjust the achievable proportion of rectification of inflated energy consumption of different production lines: when it is believed that the inflated part can be restored to the average value through management, β is 1, and when it is believed that this part cannot be rectified, β is 0; other production lines are the same as production line A1.

[0018] 2) The total output of production line A1 during the cycle is SA1t, and the daily output is SA1tn. The daily energy saving target of production line A1 is ZA1tn=ZA1t(SA1tn / SA1t).

[0019] 3) Finally, the granularity is broken down into the energy-saving indicators of the corresponding equipment in the production line and the corresponding working day.

[0020] Furthermore, the production capacity energy consumption forecast is specifically as follows: based on the monthly production schedule and output value energy consumption forecast method Et=Qt0*Mt, where Et is the predicted energy demand, Qt0 is the energy consumption per unit output value in the base period month, and Mt is the predicted annual production output value, and the energy consumption forecast for each production line or even the equipment level under each production line is obtained.

[0021] Furthermore, auxiliary monitoring of energy-saving behavior is as follows: the daily energy consumption forecast of each device on each production line is used as the upper limit indicator for that day. A real-time monitoring interactive page can be developed to monitor the real-time energy consumption and upper limit indicators of each device today, and reminders will be issued when the indicator is approaching or exceeded, allowing managers to intervene in energy-saving behavior management at low cost and high efficiency.

[0022] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0023] The energy-saving behavior decomposition auxiliary management system based on the output value energy consumption forecast analysis method of the present invention can be analyzed based on the historical production line equipment-level energy consumption data, and according to its energy consumption situation, the difference in energy consumption of the same type, and the proportion of energy consumption of different types, the periodic energy-saving goals can be more refined and scientifically decomposed into tasks, and the management granularity can be accurately determined to the daily energy consumption of each device, allowing managers to intervene in energy-saving management in a lower cost and more efficient manner, greatly lowering the threshold for energy-saving management while improving the efficiency of energy-saving management and reducing a large amount of unnecessary energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below in conjunction with the accompanying drawings:

[0025] Figure 1 The present invention is a flow chart of the energy-saving behavior disassembly auxiliary management method based on the output value energy consumption prediction analysis method. DETAILED DESCRIPTION

[0026] like Figure 1 As shown, the present invention mainly includes four steps: 1. Historical energy consumption analysis; 2. Output value energy consumption prediction; 3. Energy-saving index decomposition; 4. Auxiliary monitoring of energy-saving behavior.

[0027] Step 1: Historical energy consumption analysis:

[0028] By utilizing the industrial Internet of Things transformation, monthly energy consumption data of facilities in different links of different production lines are collected, and relevant functional production lines and facilities with the same functions are labeled accordingly. Based on historical data, the average energy consumption data of a single output value of the same functional production line and the average energy consumption data of the same functional facilities can be analyzed.

[0029] The monthly standard output value of the same production line is X, and each production line is specifically: X1, X2, X3...Xn, and the energy consumption is E, and each production line is specifically: the energy consumption is E1, E2, E3...En, where n is a natural number. Calculate the average unit output value energy consumption of the same production line: Qt = (E1 / X1+E2 / X2+...En / Xn) / n; at the same time, calculate the average unit output value energy consumption difference of a single production line: △E = En / Xn-Qt, where n is a natural number.

[0030] Specifically: For example, there are two production lines with the same functions, specifically production line A1 and production line A2. Production line A1 has equipment a11 and a12, and production line A2 has equipment a21 and a22. The monthly standard output value of production line A1 is 1, the energy consumption is E1, and the equipment energy consumption is Ea11 and Ea12. The monthly standard output value of production line A2 is 2, the energy consumption is E2, and the equipment energy consumption is Ea21 and Ea22. The average unit output value energy consumption of the production line can be obtained as: QAt = (E1 / 1 + E2 / 2) / 2, and then the average unit output value energy consumption difference of production line A1 is obtained as: △EA1 = E1 / 1-QAt, and it can be judged whether it is falsely high. Equipment-level analysis can be obtained based on the energy consumption proportion of different equipment under the production line.

[0031] Step 2: Output energy consumption forecast:

[0032] After completing the historical energy consumption analysis, according to the next monthly production schedule and output value energy consumption forecast method Et = Qt0 * Mt (energy saving is not considered here), Et is the predicted energy demand, Qt0 is the energy consumption per unit output value (output) of the base period, and Mt is the predicted annual production output value. The energy consumption forecast for each production line or even the equipment level under each production line can be obtained.

[0033] Step 3: Energy saving index analysis:

[0034] Managers set the overall energy-saving target Zt within a certain period based on actual production conditions, and the system performs this according to the proportion of different production lines in the total energy consumption.

[0035] (1) The system breaks down energy-saving targets according to the average production energy consumption of different production lines in the production schedule and the proportion of total energy consumption.

[0036] (2) If the same production lines are involved, the disassembly targets are allocated based on Qt and △E, so that the production lines with artificially high energy consumption are allocated more energy-saving targets:

[0037] On the basis of step one, assume that there is a third production line B with functions different from production lines A1 and A2. The monthly output value of production line B is 1 and the energy consumption is E3. The average unit output value energy consumption of production line B is QBt=E3 / 1. When the production schedule for the next month requires production lines A1, A2, and B to be fully operational, the expected energy-saving target is Zt. The energy-saving target for production line A1 during the cycle can be decomposed into: ZA1t=Zt*QAt / (QBt+2QAt)*(1+△EA1 / QAt*β); where β is the control parameter for the completion of energy-saving rectification, which is used to adjust the proportion of rectification of inflated energy consumption of different production lines: when it is believed that the inflated part can be restored to the average value through management, β is 1; when it is believed that the part cannot be rectified, β is 0; production line A2 is the same as production line A1.

[0038] (3) Based on the daily production schedule, the energy consumption forecast of the corresponding production line on the corresponding working day is obtained. Assuming that the total production volume of production line A1 during the cycle is SA1t and the daily production volume is SA1tn, the daily energy saving target of production line A1 is ZA1tn=ZA1t(SA1tn / SA1t).

[0039] (4) Finally, the granularity is broken down into energy-saving indicators for the corresponding equipment in the production line and corresponding working days.

[0040] Step 4: Auxiliary monitoring of energy-saving behavior:

[0041] The daily energy consumption forecast of each device on each production line is used as the upper limit indicator for that day. A real-time monitoring interactive page can be developed to monitor the real-time energy consumption and upper limit indicators of each device today. When the indicator is approached or exceeded, a reminder can be issued, allowing managers to intervene in energy-saving behavior management at a low cost and high efficiency.

[0042] The energy-saving behavior decomposition auxiliary management system based on the output value energy consumption forecast analysis method of the present invention can be analyzed based on the historical production line equipment-level energy consumption data, and according to its energy consumption situation, the difference in energy consumption of the same type, and the proportion of energy consumption of different types, the periodic energy-saving goals can be more refined and scientifically decomposed into tasks, and the management granularity can be accurately determined to the daily energy consumption of each device, allowing managers to intervene in energy-saving management in a lower cost and more efficient manner, greatly lowering the threshold for energy-saving management while improving the efficiency of energy-saving management and reducing a large amount of unnecessary energy waste.

[0043] The above are only specific embodiments of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent replacements or modifications made based on the present invention to solve basically the same technical problems and achieve basically the same technical effects are all included in the protection scope of the present invention.

Claims

1. An energy-saving behavior disassembly and auxiliary management method based on output value energy consumption prediction and analysis method, characterized in that: The steps include: S1. Historical energy consumption analysis: Collect monthly energy consumption data of different production lines and facilities in different links, label relevant functional production lines and facilities with the same function, and analyze the average unit output value energy consumption of the same functional production lines and the average unit output value difference of the production lines based on historical data; S2. Production capacity energy consumption forecast: Based on the monthly production schedule and output value energy consumption forecast method, forecast the energy consumption of each production line and equipment in the future; S3. Energy-saving index decomposition: artificially set the total energy-saving target and cycle, and then decompose the historical energy consumption proportion of each production line and each equipment to obtain the daily energy consumption forecast of each production line and each equipment; S4. Auxiliary monitoring of energy-saving behavior: The daily energy consumption forecast of each device on each production line is used as the upper limit indicator for that day to achieve target management and control monitoring.

2. The energy-saving behavior disassembly and auxiliary management method based on output value energy consumption forecasting and analysis method according to claim 1 is characterized by: (1) Group production lines with the same functions into one category; (2) The monthly standard output value of the same production line is X, and each production line is: X1, X2, X3...Xn, and the energy consumption is E, and each production line is: E1, E2, E3...En, where n is a natural number; (3) Calculate the average unit output value energy consumption of similar production lines: Qt = (E1 / X1+E2 / X2+…En / Xn) / n; at the same time, calculate the average unit output value energy consumption difference of a single production line: △E = En / Xn-Qt, where n is a natural number; (4) Management personnel set energy-saving targets Zt within a certain period based on actual production conditions; According to the production schedule, the energy-saving target is broken down based on the average unit output value energy consumption of different functional production lines in the total energy consumption; If there are production lines with the same functions involved, the disassembly targets will be allocated based on Qt and △E, so that production lines with high energy consumption can be allocated more energy-saving targets.

3. The energy-saving behavior disassembly and auxiliary management method based on output value energy consumption prediction and analysis method according to claim 2 is characterized by: 1) Assuming that the production lines with the same function are A1 and A2, and the third production line with a different function from production line A is B, QAt=(EA1 / XA1+EA2 / XA2) / 2; QBt=EB / XB; △EA1=EA1 / XA1-QAt; When the production schedule for the next month requires production lines A1, A2, and B to be fully operational, the energy-saving target for production line A1 during the cycle can be divided into: ZA1t = Zt*QAt / (QBt+2QAt)*(1+△EA1 / QAt*β); β is the energy-saving and high-energy rectification completion control parameter, which is used to adjust the achievable proportion of high-energy rectification of different production lines: when it is believed that the high-energy part can be restored to the average value through management, β is 1; when it is believed that the part cannot be rectified, β is 0; other production lines are the same as production line A1; 2) The total output of production line A1 during the cycle is SA1t, and the daily output is SA1tn. The daily energy saving target of production line A1 is ZA1tn = ZA1t (SA1tn / SA1t); 3) Finally, the granularity is broken down into the energy-saving indicators of the corresponding equipment in the production line and the corresponding working day.

4. The energy-saving behavior disassembly and auxiliary management method based on output value energy consumption prediction and analysis method according to claim 1 is characterized by: The specific forecast of production capacity energy consumption is as follows: According to the monthly production plan and output value energy consumption forecasting method Et=Qt0*Mt, where Et is the predicted energy demand, Qt0 is the energy consumption per unit output value in the base period month, and Mt is the predicted annual production value, the energy consumption forecast for each production line and even the equipment level under each production line is obtained.

5. The energy-saving behavior disassembly and auxiliary management method based on output value energy consumption forecasting and analysis method according to claim 1 is characterized in that: Energy-saving behavior auxiliary monitoring is: The daily energy consumption forecast of each device on each production line is used as the upper limit indicator for that day. A real-time monitoring interactive page can be developed to monitor the real-time energy consumption and upper limit indicators of each device today. When the indicator is approached or exceeded, reminders will be issued, allowing managers to intervene in energy-saving behavior management at a low cost and high efficiency.

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