Building electric energy management energy-saving evaluation system and evaluation method

Abstract

The invention discloses a building electric energy management energy-saving evaluation method, which comprises the following steps of: collecting electric energy consumption of a building by taking a month as a unit to obtain electric energy monthly actual energy consumption of the building; establishing an energy budget scoring mechanism, an industry comparison scoring mechanism and an energy abnormity alarm processing scoring mechanism for the monthly actual energy consumption of the electric energy; and according to the energy budget score, the industry comparison score and the energy abnormity alarm processing score, calculating a building electric energy management energy-saving evaluation total score, and according to the obtained building electric energy management energy-saving evaluation total score, determining the electric energy management energy-saving level of the building. The invention aims to optimize the electric energy use efficiency of the building and realize a quantifiable and accurate energy-saving effect; and a comprehensive electric energy consumption evaluation result is provided by combining an industrial standard and a preset energy-saving target, so that a building manager is assisted to effectively master an electric energy budget and reduce waste.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent building energy management, and in particular to a building electric energy management energy-saving evaluation system and evaluation method. Background Art

[0002] In the field of intelligent building energy management technology, building energy-saving management systems have been widely used to optimize building energy consumption and improve operational efficiency. Existing intelligent management systems often rely on hardware devices such as smart electricity, water, and gas meters to collect real-time energy consumption data. These systems also utilize IoT devices, including smart lighting, smart air conditioning systems, and smart fans, for remote control, aiming to reduce energy waste. In terms of software, existing energy management systems primarily focus on real-time monitoring, data analysis, and alarm management. These systems adjust device switching strategies through predefined rules and logical algorithms to achieve energy savings.

[0003] Therefore, the technical problems of existing building energy management systems are: lack of systematic evaluation standards for energy-saving effect evaluation and optimization decision-making; existing general systems focus more on energy consumption monitoring and energy-saving regulation of single equipment, and lack systematic methods for comprehensive energy-saving scoring in different dimensions, such as energy budget achievement rate, industry comparison, and timeliness of alarm processing. Especially in the case of multiple devices and multiple energy consumption data sources, the existing system is unable to conduct comprehensive quantitative evaluation in terms of budget tracking, real-time industry comparison and alarm processing, resulting in managers being unable to accurately understand the effectiveness of their energy-saving measures.

[0004] Therefore, achieving effective energy conservation is a key issue in building electrical energy management. Traditional building electrical energy conservation measures often rely on the energy-saving functions of hardware devices or manual operation, lacking an intelligent and systematic evaluation mechanism. As a result, current energy conservation management systems struggle to accurately reflect energy consumption in real time, effectively track energy budget achievement rates, industry comparison data, the timeliness of energy alarms, and the effectiveness of their handling. Furthermore, they are unable to comprehensively and scientifically evaluate building energy conservation performance. In other words, existing electrical energy management systems still lack the scientific nature of scoring and decision-making, especially when it comes to quantifying and evaluating energy conservation results. Summary of the Invention

[0005] The application provides a building energy management energy-saving evaluation system and an evaluation method, aiming to optimize the energy use efficiency of a building through the cooperation of hardware and software, realize quantifiable and accurate energy-saving effect, provide comprehensive energy consumption evaluation results by combining industry standards and preset energy-saving targets through automatically collected and processed energy data, help building managers effectively control energy budget and reduce waste, and finally realize the energy-saving goal of the building, and the application realizes the above-mentioned purposes through the following technical solutions.

[0006] A building energy management energy-saving evaluation method, comprising the following steps:

[0007] Step S1: collecting the energy consumption of the building in a monthly unit to obtain the monthly actual energy consumption of the building;

[0008] Step S2: establishing an energy budget score mechanism for the monthly actual energy consumption;

[0009] Step S3: setting an industry comparison score mechanism for the monthly actual energy consumption;

[0010] Step S4: establishing an energy abnormality alarm processing score mechanism for the monthly actual energy consumption;

[0011] Step S5: calculating the total score of the building energy management energy-saving evaluation according to the energy budget score, the industry comparison score and the energy abnormality alarm processing score, and determining the energy management energy-saving level of the building according to the obtained total score of the building energy management energy-saving evaluation.

[0012] Preferably, the implementation mode of step S1 comprises:

[0013] A plurality of intelligent collection devices for energy are arranged in the building, the energy consumption of the building is collected in real time, the collection frequency is set to a minute level, and the monthly actual energy consumption of the building is collected and arranged as a statistical node; the intelligent collection device is a smart meter with a minute-level collection accuracy.

[0014] Preferably, the implementation mode of establishing the energy budget score mechanism for the monthly actual energy consumption in step S2 comprises:

[0015] Step S21: comparing the monthly actual energy consumption of the building with the monthly budget energy consumption of the building, calculating the percentage Y of the monthly actual energy consumption of the building and the monthly budget energy consumption of the building, Y being the monthly actual energy consumption data / the monthly budget energy consumption target data;

[0016] Step S22: setting a plurality of score intervals based on Y to obtain the energy budget score, and the score intervals are specifically set as:

[0017] When 90% ≤ Y ≤ 100%, the energy budget score is 100 points;

[0018] When 80%≤Y<90%, the energy budget score is 90 points;

[0019] When Y < 80%, the energy budget score is 70 points;

[0020] When 100%<Y≤105%, the energy budget score is 85 points;

[0021] When Y>105%, the energy budget score is 60 points.

[0022] Preferably, the implementation of setting the industry comparison scoring mechanism for actual monthly electricity consumption in step S3 includes:

[0023] Step S31: Collect the monthly electricity consumption of no less than three groups of buildings of the same type, take the average value, and set the average value as the industry standard data;

[0024] Step S32: Compare the actual monthly energy consumption of the building with industry standard data;

[0025] Step S33: Setting comparison score rules to obtain industry comparison scores, specifically including:

[0026] Set the difference between the actual monthly energy consumption of the building and the industry standard data as A. When A is less than 0, the industry comparison score is set at 100 points; when 0 is less than A≤5%, the industry comparison score is reduced by 10 points to 90 points; when 5% is less than A≤10%, the industry comparison score is reduced by 00 points to 80 points; when 10% is less than A≤25%, the industry comparison score is reduced by 30 points to 70 points; when A is greater than 25%, the industry comparison score is reduced by 40 points to 60 points; in this way, the final industry comparison score is obtained.

[0027] Preferably, the implementation of establishing an energy anomaly alarm processing scoring mechanism for actual monthly electricity consumption in step S4 includes:

[0028] Step S41: During the process of collecting the actual monthly energy consumption of the building, the abnormalities of the actual monthly energy consumption are constantly monitored, and an alarm is triggered when the actual monthly energy consumption is abnormal;

[0029] Step S42: Set up an alarm mechanism for the anomaly, issue an alarm in time according to the triggered anomaly, and determine whether the processing time limit for the anomaly alarm is completed within 8 hours. If the processing is completed and handled in place, the energy anomaly alarm processing score is set to 100 points; if the processing is not completed, 10 points will be deducted for each anomaly alarm that exceeds the time limit, until the energy anomaly alarm processing score is deducted to 0 points.

[0030] Preferably, the way of calculating the building energy management energy saving evaluation total score in step S5 is:

[0031] The building energy management energy saving evaluation total score = 0.4 * energy budget score + 0.2 * industry comparison score + 0.4 * energy alarm processing score.

[0032] A building energy management energy saving evaluation system for executing a building energy management energy saving evaluation method, comprising a data-driven energy management module, a quantitative evaluation module and an energy management energy saving adjustment module, wherein the data-driven energy management module and the quantitative evaluation module are controlled by the energy management energy saving adjustment module.

[0033] The data-driven energy management module is used for accurately collecting the energy consumption data of the building and statistically calculating the monthly actual energy consumption of the building in a month unit; the data quantitative evaluation module is used for calculating the energy budget score mechanism, the industry comparison score mechanism and the energy abnormal alarm processing score mechanism; and the energy management energy saving adjustment module is used for calculating the building energy management energy saving evaluation total score and determining the energy management energy saving level of the building according to the calculated building energy management energy saving evaluation total score.

[0034] Preferably, the data-driven energy management module comprises a data acquisition module group and an Internet of Things device group, the data acquisition module group comprises a plurality of smart electricity meters, and the Internet of Things device group comprises smart lighting lamps, smart air conditioners and smart fans; the energy management energy saving adjustment module dynamically and real-timely adjusts the running state of the Internet of Things devices through remote control, so that the Internet of Things devices are controlled within the energy saving range.

[0035] Preferably, the quantitative evaluation module comprises an energy budget module, an industry comparison module and an energy alarm processing module; the energy budget module is used for calculating the energy budget score mechanism; the industry comparison module is used for calculating the industry comparison score mechanism; and the energy abnormal alarm processing module is used for calculating the energy abnormal alarm processing score mechanism.

[0036] Preferably, the energy management energy saving adjustment module further has a real-time monitoring function, which is used for providing intuitive chart feedback for the building management personnel according to the running strategy of the Internet of Things devices and automatically generating energy saving improvement suggestions according to the building energy management energy saving evaluation total score, so as to guide the building manager to more efficiently execute the energy saving measures.

[0037] The present application has the following advantages and beneficial effects compared with the prior art:

[0038] 1. The evaluation system and method of the present invention effectively track the gap between energy budget and actual energy consumption through data collection of hardware devices and intelligent analysis of software systems, and provide real-time prompts of industry comparison results and alarm processing status. Through a quantitative scoring mechanism, it achieves a scientific and comprehensive evaluation of a building's energy-saving performance, effectively guides the implementation of energy-saving measures, and improves energy management efficiency.

[0039] 2. This assessment system has real-time monitoring capabilities, can dynamically adjust equipment operation strategies, and provide intuitive graphical feedback to building managers. In addition, it automatically generates energy-saving improvement suggestions based on the assessment results, helping users make more scientific decisions on energy management.

[0040] 3. This invention effectively helps reduce electricity waste in buildings and helps optimize the rational allocation of energy resources. If it is promoted and applied in areas with concentrated buildings, it will significantly reduce carbon emissions and promote green and environmentally friendly development.

[0041] 4. This invention improves the automation level of building electricity energy-saving management, realizes minute-level precise data collection and intelligent adjustment capabilities, and has the advantages of being more flexible, timely and accurate compared to traditional energy management methods based on manual and fixed strategies, ensuring that building energy use is in the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 4 is an execution flow chart of the method of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear and explicit, the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] Example 1:

[0046] like Figure 1 As shown, a building power management energy saving evaluation method includes the following steps:

[0047] Step S1: Collect the building's electricity consumption on a monthly basis to obtain the building's actual monthly electricity consumption; specifically:

[0048] Multiple intelligent energy collection devices are installed in buildings to collect energy consumption in real time, with a frequency of minute-by-minute. Monthly statistics are compiled to store and organize actual monthly energy consumption. These intelligent collection devices are smart meters with minute-by-minute accuracy. Compared to traditional building energy management, which relies solely on annual or quarterly energy consumption data and struggles with real-time tracking and adjustment, this invention utilizes smart meters and remotely controllable IoT devices to collect energy consumption data accurate to the minute, enabling real-time monitoring and scientific management of energy use.

[0049] Step S2: Establish an energy budget scoring mechanism for actual monthly electricity consumption, specifically:

[0050] Step S21: Compare the actual monthly energy consumption of the building with the monthly budgeted energy consumption of the building, and calculate the percentage Y of the actual monthly energy consumption of the building to the monthly budgeted energy consumption of the building, where Y is the actual monthly energy consumption data / the monthly budgeted energy consumption target data;

[0051] Step S22: Set multiple scoring intervals based on Y to obtain energy budget scores. The scoring intervals are specifically set as follows:

[0052] When 90% ≤ Y ≤ 100%, the energy budget score is 100 points;

[0053] When 80%≤Y<90%, the energy budget score is 90 points;

[0054] When Y < 80%, the energy budget score is 70 points;

[0055] When 100%<Y≤105%, the energy budget score is 85 points;

[0056] When Y>105%, the energy budget score is 60 points.

[0057] Step S3: Setting up an industry comparison scoring mechanism for actual monthly electricity consumption, specifically:

[0058] Step S31: Collect the monthly electricity consumption of no less than three groups of similar buildings, take the average value, and set the average value as the industry standard data;

[0059] Step S32: Compare the actual monthly energy consumption of the building with industry standard data;

[0060] Step S33: Setting comparison score rules to obtain industry comparison scores, specifically including:

[0061] Set the difference between the actual monthly energy consumption of the building and the industry standard data as A. When A is less than 0, the industry comparison score is set at 100 points; when 0 is less than A≤5%, the industry comparison score is reduced by 10 points to 90 points; when 5% is less than A≤10%, the industry comparison score is reduced by 00 points to 80 points; when 10% is less than A≤25%, the industry comparison score is reduced by 30 points to 70 points; when A is greater than 25%, the industry comparison score is reduced by 40 points to 60 points; in this way, the final industry comparison score is obtained.

[0062] Step S4: Establish an energy anomaly alarm processing scoring mechanism for the actual monthly energy consumption, specifically:

[0063] Step S41: During the process of collecting the actual monthly energy consumption of the building, the abnormalities of the actual monthly energy consumption are constantly monitored, and an alarm is triggered when the actual monthly energy consumption is abnormal;

[0064] Step S42: Set up an alarm mechanism for the anomaly, issue an alarm in time according to the triggered anomaly, and determine whether the processing time limit for the anomaly alarm is completed within 8 hours. If the processing is completed and handled in place, the energy anomaly alarm processing score is set to 100 points; if the processing is not completed, 10 points will be deducted for each anomaly alarm that exceeds the time limit, until the energy anomaly alarm processing score is deducted to 0 points.

[0065] Step S5: Calculate the total building power management energy-saving assessment score based on the energy budget score, the industry comparison score, and the energy anomaly alarm processing score, and determine the building's power management energy-saving level based on the obtained total building power management energy-saving assessment score.

[0066] The method used to calculate the total score of the building power management energy saving assessment is as follows:

[0067] The total score for the building power management energy-saving assessment is calculated as 0.4 x energy budget score + 0.2 x industry comparison score + 0.4 x energy alarm handling score. This scoring system provides a simple, quantitative method for objectively evaluating a building's energy-saving performance, facilitating tracking and improving energy-saving strategies.

[0068] The present invention facilitates dynamic adjustment of strategies and optimization of electric energy configuration by providing monthly energy scores. Its real-time processing capability for critical electric energy alarms can immediately notify managers when electric energy anomalies occur, and statistical processing rates and efficiencies are calculated, so that energy management is not limited to problem discovery but also covers timely responses.

[0069] Example 2:

[0070] A building power management energy-saving assessment system is used to execute a building power management energy-saving assessment method, including a data-driven energy management module, a quantitative assessment module and a power management energy-saving adjustment module. The data-driven energy management module and the quantitative assessment module are both controlled by the power management energy-saving adjustment module.

[0071] Among them, the data-driven energy management module is used to accurately collect the building's electricity consumption data and to count the building's actual monthly electricity consumption on a monthly basis; the data quantitative evaluation module is used to calculate the execution energy budget scoring mechanism, the industry comparison scoring mechanism, and the energy abnormality alarm processing scoring mechanism; the electricity management energy-saving adjustment module is used to calculate the total score of the building's electricity management energy-saving assessment, and determine the building's electricity management energy-saving level based on the calculated total score of the building's electricity management energy-saving assessment.

[0072] The data-driven energy management module comprises a data acquisition module group and an IoT device group. The data acquisition module group includes multiple smart meters, and the IoT device group includes smart lighting, smart air conditioners, and smart fans. The power management energy-saving adjustment module dynamically adjusts the operating status of IoT devices in real time through remote control, keeping them within energy-saving limits. By dynamically adjusting the start and stop strategies of IoT devices such as air conditioners, lighting, and fans, it achieves scientific power consumption control, optimizes energy efficiency with energy conservation as the goal, and makes decisions based on real-time data collection, ensuring that devices operate in optimal energy-saving conditions.

[0073] The aforementioned quantitative assessment modules include an energy budget module, an industry comparison module, and an energy alarm processing module. Specifically, the energy budget module is used to calculate and implement the energy budget scoring mechanism; the industry comparison module is used to calculate and implement the industry comparison scoring mechanism; and the energy anomaly alarm processing module is used to calculate and implement the energy anomaly alarm processing scoring mechanism. The energy management and energy conservation adjustment module also features real-time monitoring capabilities, providing building managers with intuitive graphical feedback based on the operating strategies of IoT devices. It also automatically generates energy-saving improvement suggestions based on the building's overall power management energy conservation assessment score, guiding building managers to more effectively implement energy conservation measures.

[0074] This invention aims to optimize a building's electrical energy efficiency through the synergy of hardware and software, achieving quantifiable and precise energy savings. By automatically collecting and processing fine-grained electrical energy consumption data and integrating it with industry standards and preset energy conservation targets, the system provides comprehensive energy consumption scores, helping building managers effectively manage energy budgets and reduce waste, ultimately achieving building energy conservation goals. Energy efficiency can also be effectively improved through energy conservation assessments, and based on the system's assessment and adjustment strategies, energy savings of at least 10%-15% can be achieved.

Claims

1. A building power management energy saving evaluation method, characterized in that: The following steps are involved: Step S1: Collect the building's electricity consumption on a monthly basis to obtain the building's actual monthly electricity consumption; Step S2: establishing an energy budget scoring mechanism based on actual monthly electricity consumption; Step S3: Setting an industry comparison scoring mechanism for actual monthly electricity consumption; Step S4: Establishing an energy anomaly alarm processing scoring mechanism for actual monthly electricity consumption; Step S5: Calculate the total building power management energy-saving assessment score based on the energy budget score, the industry comparison score, and the energy anomaly alarm processing score, and determine the building's power management energy-saving level based on the obtained total building power management energy-saving assessment score.

2. A building power management energy saving evaluation method according to claim 1, characterized in that: The implementation of step S1 includes: Multiple intelligent energy collection devices are installed in the building to collect the building's electricity consumption in real time. The collection frequency is set to minutes, and the actual monthly electricity consumption of the building is collected and sorted out on a monthly basis. The intelligent collection device is a smart meter with minute-level collection accuracy.

3. A building power management energy saving evaluation method according to claim 1, characterized in that: The implementation of establishing the energy budget scoring mechanism for the actual monthly energy consumption of electricity in step S2 includes: Step S21: Compare the actual monthly energy consumption of the building with the monthly budgeted energy consumption of the building, and calculate the percentage Y of the actual monthly energy consumption of the building to the monthly budgeted energy consumption of the building, where Y is the actual monthly energy consumption data / the monthly budgeted energy consumption target data; Step S22: Set multiple scoring intervals based on Y to obtain energy budget scores. The scoring intervals are specifically set as follows: When 90% ≤ Y ≤ 100%, the energy budget score is 100 points; When 80%≤Y<90%, the energy budget score is 90 points; When Y < 80%, the energy budget score is 70 points; When 100%<Y≤105%, the energy budget score is 85 points; When Y>105%, the energy budget score is 60 points.

4. A building power management energy saving evaluation method according to claim 1, characterized in that: The implementation of setting the industry comparison scoring mechanism for actual monthly electricity consumption in step S3 includes: Step S31: Collect the monthly electricity consumption of no less than three groups of buildings of the same type, take the average value, and set the average value as the industry standard data; Step S32: Compare the actual monthly energy consumption of the building with industry standard data; Step S33: Setting comparison score rules to obtain industry comparison scores, specifically including: Set the difference between the actual monthly energy consumption of the building and the industry standard data as A. When A is less than 0, the industry comparison score is set at 100 points; when 0 is less than A≤5%, the industry comparison score is reduced by 10 points to 90 points; when 5% is less than A≤10%, the industry comparison score is reduced by 00 points to 80 points; when 10% is less than A≤25%, the industry comparison score is reduced by 30 points to 70 points; when A is greater than 25%, the industry comparison score is reduced by 40 points to 60 points; in this way, the final industry comparison score is obtained.

5. A building power management energy saving evaluation method according to claim 1, characterized in that: The implementation of establishing the energy abnormality alarm processing scoring mechanism for the actual monthly energy consumption of electric energy in step S4 includes: Step S41: During the process of collecting the actual monthly energy consumption of the building, the abnormalities of the actual monthly energy consumption are constantly monitored, and an alarm is triggered when the actual monthly energy consumption is abnormal; Step S42: Set up an alarm mechanism for the anomaly, issue an alarm in time according to the triggered anomaly, and determine whether the processing time limit for the anomaly alarm is completed within 8 hours. If the processing is completed and handled in place, the energy anomaly alarm processing score is set to 100 points; if the processing is not completed, 10 points will be deducted for each anomaly alarm that exceeds the time limit, until the energy anomaly alarm processing score is deducted to 0 points.

6. A building power management energy saving evaluation method according to claim 1, characterized in that: The method for calculating the total score of the building power management energy saving assessment in step S5 is: The total score of the building power management energy-saving assessment = 0.4*energy budget score + 0.2*industry comparison score + 0.4*energy alarm processing score.

7. A building power management energy-saving evaluation system, used to execute a building power management energy-saving evaluation method according to any one of claims 1 to 6, characterized in that: It includes a data-driven energy management module, a quantitative evaluation module and an electric energy management energy-saving adjustment module, wherein the data-driven energy management module and the quantitative evaluation module are both controlled by the electric energy management energy-saving adjustment module; The data-driven energy management module is used to accurately collect the building's electricity consumption data and to compile statistics on the building's actual monthly electricity consumption on a monthly basis; the data quantitative evaluation module is used to calculate the execution energy budget scoring mechanism, the industry comparison scoring mechanism, and the energy anomaly alarm processing scoring mechanism; the electricity management energy-saving adjustment module is used to calculate the total score of the building's electricity management energy-saving evaluation, and to determine the building's electricity management energy-saving level based on the calculated total score of the building's electricity management energy-saving evaluation.

8. A building power management energy-saving evaluation system according to claim 7, characterized in that: The data-driven energy management module includes a data acquisition module group and an Internet of Things device group. The data acquisition module group includes multiple smart meters, and the Internet of Things device group includes smart lighting, smart air conditioners, and smart fans. The power management energy-saving adjustment module dynamically adjusts the operating status of the Internet of Things devices in real time through remote control, so that the Internet of Things devices are controlled within the energy-saving range.

9. A building power management and energy-saving evaluation system according to claim 7, characterized in that: The quantitative assessment module includes an energy budget module, an industry comparison module and an energy alarm processing module; the energy budget module is used to calculate and execute the energy budget scoring mechanism; the industry comparison module is used to calculate and execute the industry comparison scoring mechanism; the energy anomaly alarm processing module is used to calculate and execute the energy anomaly alarm processing scoring mechanism.

10. A building power management and energy-saving evaluation system according to claim 8, characterized in that: The power management energy-saving adjustment module also has a real-time monitoring function, which is used to provide building managers with intuitive graphical feedback based on the operating strategy of the Internet of Things devices, and automatically generate energy-saving improvement suggestions based on the total score of the building power management energy-saving assessment, thereby guiding building managers to implement energy-saving measures more efficiently.

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