Building heat supply energy consumption detection system and energy-saving grade evaluation method

Abstract

The invention relates to the field of building energy conservation and environmental protection, in particular to a building heat supply energy consumption detection system and an energy conservation grade evaluation method. The system is composed of a building heat meter or a household heat meter, a room temperature controller, an outdoor air temperature collector, an electric valve and a computing center, and data are collected and transmitted through NB wireless remote communication. Based on a unique temperature difference heat index calculation method, a building energy consumption heat index, a temperature difference load rate and converted design temperature difference heat consumption are calculated by integrating various factors, and then the energy-saving grade is determined by comparing with local non-energy-saving building design parameters. According to the invention, energy consumption can be dynamically monitored in the heating season, and instance verification is provided for building energy-saving design; help is provided for energy-saving operation of hot enterprises; and an accurate and effective scientific basis is provided for national evaluation of the building energy-saving grade.

Description

Technical Field

[0001] The present invention relates to the field of building energy conservation and environmental protection, and in particular to a building heating energy consumption detection system and an energy conservation grade evaluation method. Background Art

[0002] With the rapid advancement of global industrialization and urbanization, the issue of energy consumption has become increasingly prominent. As a major energy consumer, the importance of energy conservation in the construction sector is self-evident. In my country, the total energy consumption of buildings has remained high for a long time, accounting for a considerable proportion of the total social energy consumption. Among them, heating energy consumption is particularly prominent in the northern heating areas. In response to this severe situation, the country has actively introduced a series of building energy-saving design standards and specifications. Starting from the planning and design stage of the building, strict restrictions have been imposed on many aspects such as the building's orientation, body coefficient, and heat transfer coefficient of the enclosing structure. At the same time, clear indicators and requirements have been formulated for the thermal efficiency of boilers and the transmission efficiency of pipe networks in the heating system. During the construction stage, there are detailed regulations on the selection of energy-saving materials, the control of construction technology, and the inspection of the acceptance link to ensure that new buildings can meet energy-saving requirements.

[0003] However, building energy efficiency efforts don't stop at completion and handover. During the long operational lifespan of a building, its actual heating energy consumption lacks effective monitoring and accurate assessment methods. Traditional energy consumption assessments often rely on simple total heat consumption statistics, which ignore the impact of many key factors, such as indoor and outdoor temperature fluctuations and building usage, and thus fail to accurately reflect a building's true energy efficiency. For example, during the heating season, fluctuations in outdoor temperature directly lead to changes in building heat consumption. Judging energy efficiency solely based on total heat consumption can lead to erroneous conclusions. Furthermore, differences in building usage, occupancy density, and other factors can lead to significant variations in energy consumption. The lack of a scientifically sound evaluation method makes it difficult to effectively differentiate and implement targeted improvements to building energy efficiency. This situation not only hinders the further development and optimization of building energy-saving technologies, but also hinders heating companies from implementing refined energy management and operating cost control, making it difficult to meet the increasingly stringent national regulatory requirements for building energy efficiency. Therefore, an innovative, scientifically accurate building heating energy consumption monitoring system and energy-saving rating evaluation method are urgently needed to fill this technological gap and advance building energy efficiency to new heights. Summary of the Invention

[0004] The object of the present invention is to provide a building heating energy consumption detection system, comprising:

[0005] Building heat meters or household heat meters are used to measure the heat consumption data of buildings or households;

[0006] Room thermostat, installed in each heated room to monitor indoor temperature;

[0007] Outdoor temperature collectors are installed in each building to collect outdoor temperature data;

[0008] Electric valves are installed in each household's heating pipes to adjust the hydraulic and thermal balance of the heating system;

[0009] The computing center receives and processes data from each measuring device through NB wireless remote communication;

[0010] Among them, measuring devices such as building heat meters, household heat meters, room thermostats, and outdoor temperature collectors all use NB wireless remote communication, uploading the collected data to the computing center for processing at set intervals. The installation of each device must meet specific location and environmental requirements to ensure the accuracy and reliability of data collection.

[0011] Furthermore, the building heat meter is based on a heat metering algorithm, which measures the hot water flow rate and the supply and return water temperature difference in the heating pipe, and uses a heat calculation formula to accurately calculate the building's heat consumption data. The heat calculation formula is Q=cmΔT, where c is the specific heat capacity of water, m is the mass flow rate of water, and ΔT is the supply and return water temperature difference.

[0012] Furthermore, the device installation requirements are as follows:

[0013] All heated rooms with exterior walls and radiators in the building must be equipped with room thermostats. They should not be installed near exterior walls, radiators, or fan coil outlets. They should be installed in the center of the indoor space, away from heat and cold sources, and approximately 1.5 meters above the ground.

[0014] Each building is equipped with an outdoor temperature collector, and the temperature probe is installed in the regular blinds to ensure air circulation, rain protection, snow protection and sunlight protection;

[0015] Each building's heating pipe should be equipped with a building heat meter or each user should be equipped with a household heat meter. The heat meter should be installed on the straight section of the heating pipe, with the upstream straight section being no less than 10 pipe diameters long and the downstream straight section being no less than 5 pipe diameters long. The heat meter sensor should be in full contact with the hot water in the pipe.

[0016] The building energy consumption management department has a main control computing center, which uses NB remote communication to receive thermal data collected by various measuring devices in the building. The computing center is set up in a computer room that meets constant temperature, constant humidity, dustproof and anti-static requirements, and is equipped with a complete network security protection system.

[0017] Furthermore, the system workflow is as follows: all measurement parameters are transmitted once every 10 minutes and data processing is performed. When the collection cycle arrives, the building heat meter and household heat meter measure the heat data, the room thermostat and the outdoor temperature collector collect the indoor temperature and outdoor temperature data respectively, and send the data to the computing center through the NB wireless remote communication module. The computing center verifies the integrity and accuracy of the data, eliminates abnormal data, and calculates the key energy consumption parameters according to the preset energy consumption calculation method. The calculation results are compared and analyzed with the local non-energy-saving building heating design parameters to determine the building energy-saving level. The energy consumption data, calculation results and energy-saving level information are sorted and stored, and an energy consumption report is generated.

[0018] On the other hand, the present invention also provides a method for calculating the temperature difference heat index (energy consumption coefficient), including the following three calculation methods:

[0019] Calculation method of temperature difference thermal index 1:

[0020] Calculation method 2 for temperature difference thermal index:

[0021] Method 3 for calculating temperature difference heat index: Due to different indoor areas, the indoor temperature weights are also different. In formulas (1) and (2), q c The calculation may have a few thousandths of an error, so Algorithm 3 can be used to perform accurate calculations for each household, that is,

[0022]

[0023] in:

[0024] q c It is the temperature difference heat index (energy consumption coefficient), the unit is W / m 2 hΔ℃;

[0025] Q is the heat consumption, which is the heat consumption of the building's heat meter, or the total heat consumption of each household's heat meter, and the unit is W;

[0026] A is the total heating area, unit is m 2 ;

[0027] Tp 为 The actual average indoor temperature in °C;

[0028] T w is the actual average outdoor temperature in °C;

[0029] q is the area heat index, the unit is W / m 2 h;

[0030] A1、A2、A3、A n is the heating area of ​​each user, in m 2 ;

[0031] T1, T2, T3, T n It is the indoor temperature of each user, in °C.

[0032] It is further used to calculate the building energy consumption heat index, and the calculation formula is as follows:

[0033] q a Energy consumption index, unit is W / m 2 ;

[0034] q c It is the temperature difference heat index, the unit is W / m 2 hΔ℃;

[0035] T np is the actual average indoor temperature, in °C;

[0036] T wp is the actual average outdoor temperature in °C.

[0037] It is further used to calculate the actual temperature difference load rate, and the calculation formula is: in:

[0038] It is further used to calculate the converted design temperature difference heat consumption, and the calculation formula is: where Q c The unit is W, which is the heat consumed by the temperature difference.

[0039] The present invention also provides a building energy-saving grade evaluation method, which takes the local non-energy-saving building heating design parameters as a reference, and the country sets five building energy-saving grades based on the assessment of non-energy-saving buildings. By comparing the energy-saving building thermal index with the non-energy-saving building design thermal index, the formula Determine the energy conservation level, where:

[0040] a is the energy-saving grade coefficient;

[0041] q c It is the temperature difference thermal index of energy-saving buildings;

[0042] q cj It is the design temperature difference thermal index of non-energy-saving buildings;

[0043] Q c Design temperature difference heat consumption for energy-saving buildings;

[0044] q j Design thermal index for non-energy-saving buildings.

[0045] The present invention also provides a comprehensive application method for building heating energy consumption detection and energy-saving grade evaluation, which uses the building heating energy consumption detection system to collect data, uses the temperature difference heat index calculation method to calculate relevant energy consumption indicators, determines the building energy-saving grade according to the building energy-saving grade evaluation method, and formulates the building heating energy-saving optimization strategy based on the evaluation results. The energy-saving optimization strategy includes but is not limited to adjusting the operating parameters of the heating system, optimizing the thermal insulation performance of the building envelope structure, and promoting the application of energy-saving equipment.

[0046] Beneficial effects:

[0047] The detection system of the present invention uses advanced measuring devices and NB wireless remote communication technology to achieve real-time and accurate collection and transmission of multiple parameters in the building heating process. Whether it is the heat consumption of buildings or households, indoor and outdoor temperature changes, or the hydraulic and thermal balance status of the heating system, they can all be accurately monitored, providing a rich and accurate data basis for in-depth understanding of the building's energy consumption status, and effectively solving the problem of single and inaccurate data in traditional detection methods. The innovative temperature difference heat index calculation method and its related derivative calculations fully consider multiple factors such as indoor and outdoor temperature differences, the area and temperature weights of different areas of the building, and can more scientifically reflect the energy consumption characteristics of the building under different working conditions. By accurately calculating key parameters such as the building energy consumption heat index, temperature difference load rate and converted design temperature difference heat consumption, not only can the current energy consumption level be accurately assessed, but also the energy consumption trend of the entire heating season can be reliably predicted. The energy-saving grade evaluation system based on the local non-energy-saving building heating design parameters has clear reference standards and scientific evaluation formulas. By comparing the thermal indicators of energy-saving buildings with those of non-energy-saving buildings to determine the energy-saving grade, the building's energy-saving degree can be intuitively reflected, making the assessment of the building's energy-saving effect more objective, fair, and reasonable. This helps building managers and relevant departments clearly understand the building's energy-saving status, provides strong support for the formulation of targeted energy-saving measures and policies, and promotes the orderly development and in-depth promotion of building energy-saving work. The comprehensive and accurate energy consumption data, scientific calculation results, and reasonable energy-saving grade evaluation provided by the present invention can provide key decision-making basis for building energy-saving management departments, thermal power companies, etc. when formulating energy-saving strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 System principle flow chart; DETAILED DESCRIPTION

[0049] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0050] Example 1

[0051] This example uses a residential complex in a northern city as the research object. This complex consists of 10 residential buildings, each a six-story brick-concrete structure with a total floor area of ​​approximately 30,000 square meters. Central heating provides winter heating services to residents. Heat meters were installed at the heating inlet pipes of each building. Installation was performed on straight pipe sections, ensuring sufficient upstream straight pipe length and that the downstream straight pipe section met requirements to ensure stable water flow. The heat meter sensor penetrated deep into the pipe, ensuring full contact with the hot water. After installation, rigorous calibration and leak testing were performed to prevent leaks and measurement errors. Room thermostats were installed in each heated room in each building. Based on the room layout and usage, they were installed on a wall away from exterior walls, radiators, and fan coil unit outlets, typically in the center of the room, approximately 1.5 meters above the floor. During installation, ensure that the thermostat sensor accurately senses the indoor air temperature and avoids interference from localized heat or cold sources. For example, in bedrooms, they avoided bedside areas and windows; in living rooms, they avoided areas like air conditioner units and balcony doors. A total of approximately 1,800 room thermostats were installed, covering all heated rooms in the complex.

[0052] Outdoor temperature sensors are installed on the shady side of each building, with temperature probes placed within dedicated blinds. The blinds are installed approximately 2.2 meters above the ground to ensure smooth air circulation and effectively protect against rain, snow, and direct sunlight. Customized light barriers are installed above the blinds on buildings adjacent to glass curtain walls. These barriers are made of aluminum alloy and coated with a heat-insulating coating. They are precisely adjusted based on the angle of sunlight reflection to protect the temperature probes from the effects of reflected heat. Electric valves are installed on each household's heating pipes. These valves are linked to the room thermostats to automatically adjust their opening according to indoor temperature fluctuations, enabling precise control of heating flow to each household. During installation, the valves were ensured to have a good seal and operate flexibly and reliably, undergoing multiple commissioning sessions to ensure stable operation under various operating conditions. A master control computing center was established within the community's property management center. The computing center's computer room is built to standards for constant temperature, humidity, dust protection, and anti-static protection. It is equipped with high-performance servers, network switches, and data storage devices, along with a specially developed energy consumption monitoring and analysis software system. The servers utilize redundant power supplies and hot backup technology to ensure stable and reliable data processing and storage. Through NB wireless remote communication modules, the computing center establishes a stable communication link with the measuring devices in each building, enabling real-time data reception and processing. During the 2023-2024 heating season, the system began data collection. The data collection cycle was set to 10 minutes, with each measuring device automatically collecting data at set intervals and uploading it to the computing center via NB wireless remote communication. Building heat meters monitor each building's heat consumption in real time. During a typical collection cycle, for example, from 10:00 AM to 10:10 AM on January 10th, a building's heat meter measured 50,000 joules of heat consumption. Household heat meters record detailed heat usage for each household, such as a household's heat consumption of 3,000 joules during the same period. This heat data is promptly transmitted to the computing center, providing baseline data for subsequent energy consumption analysis. Room thermostats continuously monitor indoor temperatures. During the same period, the indoor temperature in a room was 22°C. The outdoor temperature collector simultaneously collects the outdoor temperature, which was -5°C at the time. These temperature data are uploaded to the computing center together with the heat data for subsequent energy consumption calculations and other operations. In the middle of the heating season, the computing center uses the temperature difference heat index calculation method 1 to calculate based on the collected data such as the total heat consumption of a certain building over a period of time (such as a week), as well as the actual indoor average temperature, actual outdoor average temperature and total heating area during the period. After a series of data processing and calculations, the temperature difference heat index value of the building is obtained. For a certain household, its area, indoor temperature at a certain moment and other data are collected and summarized, and combined with relevant data from other households. The temperature difference heat index calculation method 3 is used for accurate household calculations. Through the computing center's summary of the data of each household and the complex calculation process, the temperature difference heat index value of the household is obtained.This household-by-household calculation method provides a more accurate understanding of each household's energy consumption characteristics, providing a basis for household-by-household billing and personalized energy-saving recommendations. Given the actual average indoor and outdoor temperatures of a building at a given moment, as well as the previously calculated temperature difference heat index (using the overall calculation result as an example), the corresponding formula is used to calculate the building's energy consumption heat index. By substituting the relevant data into the formula, the specific value of the building's energy consumption heat index is calculated.

[0053] The design indoor and outdoor temperatures of the non-energy-efficient buildings in the region are known. Based on the actual average indoor and outdoor temperatures, the actual temperature difference load rate calculation formula is used. After data collation and calculation, the actual temperature difference load rate value is obtained.

[0054] Based on the calculated energy consumption index and temperature difference load rate, the calculation formula for the converted design temperature difference heat consumption is used. The relevant data is accurately substituted into the formula, and after a series of calculation operations, the converted design temperature difference heat consumption value is obtained.

[0055] After calculating and analyzing the building's energy consumption and obtaining relevant energy consumption indicators, we compared and evaluated them with the heating design parameters of local non-energy-saving buildings. First, we determined the design thermal index of local non-energy-saving buildings, and then calculated the design temperature difference thermal index of non-energy-saving buildings.

[0056] Then calculate the energy-saving grade coefficient according to the energy-saving grade evaluation formula. Substitute the relevant energy consumption index values ​​of the building into the formula and calculate the specific value of the energy-saving grade coefficient. Compare it with the national building energy-saving grade standards:

[0057] a1 Level 1 energy saving: energy saving rate required to be above 65%

[0058] a2 Level 2 energy saving: energy saving rate required to be more than 50%

[0059] a3 Level 3 energy saving: energy saving rate required to be above 35%

[0060] a4 Level 4 energy saving: energy saving rate required to be more than 25%

[0061] a5 Level 5 energy saving: energy saving rate is required to be above 15%.

[0062] The building's energy-saving grade is A4, which indicates that the building has achieved certain results in energy conservation, but there is still room for further improvement.

[0063] Based on the results of energy consumption testing and analysis, the heating company optimized the residential complex's heating system. The boiler's combustion parameters were reset, improving thermal efficiency and ensuring more complete combustion, reducing fuel consumption. For example, the air supply temperature and air volume were optimized to ensure complete combustion within the furnace, significantly increasing thermal efficiency.

[0064] At the same time, hydraulic balancing was conducted on the heating network. By adjusting the balancing valves at each building entrance, hot water was distributed more evenly throughout the network, reducing overheating of nearby users and overcooling of distant users, thereby improving the thermal stability of the entire heating system. During the commissioning process, the opening of the balancing valves was precisely adjusted based on data from the building's heat meters and household heat meters, ensuring that each user received the appropriate amount of heat, thereby reducing overall heating energy consumption.

[0065] For some buildings with relatively poor energy-saving performance, proposals for building envelope modifications were submitted to the community property management department. They recommended retrofitting the building's exterior walls with insulation, using new exterior wall insulation materials, such as polystyrene insulation systems, to increase the wall's thermal insulation performance and reduce heat loss. Estimates indicate that implementing exterior wall insulation can significantly reduce the wall's heat transfer coefficient, significantly reducing the building's winter heating energy consumption.

[0066] Furthermore, it is recommended that windows be replaced with energy-saving plastic-steel windows or thermally insulated aluminum windows to improve their airtightness and thermal insulation. These new windows will meet the highest national airtightness standards, effectively reducing heat loss from cold air infiltration. These structural modifications will further enhance the building's energy efficiency, lowering residents' heating costs while also reducing energy consumption and environmental pollution.

[0067] After implementing the energy-saving strategies, the community conducted follow-up monitoring and effectiveness evaluation of energy consumption during the following heating season. A comparative study revealed that overall heating energy consumption decreased by approximately 15% compared to the previous heating season. For example, under the same outdoor temperature conditions, heat consumption across all buildings decreased significantly. Before implementing the energy-saving strategies, one building consumed approximately 80,000 joules per day at an outdoor temperature of -10°C. After implementing the energy-saving strategies, this consumption decreased to approximately 68,000 joules at the same outdoor temperature. This demonstrates that the heating system optimization and building envelope renovation recommendations have achieved significant energy savings. A satisfaction survey was also conducted among community residents. The results showed a significant improvement in residents' satisfaction with indoor temperature comfort. Before the energy-saving strategies were implemented, approximately 20% of residents reported experiencing unstable indoor temperatures in winter, with either overheating or overcooling. After implementing the energy-saving strategies, this percentage dropped to less than 5%. Residents generally reported a more uniform and comfortable indoor temperature and a reduction in heating costs, significantly increasing their recognition and support for building energy efficiency efforts. In summary, the building heating energy consumption detection system and energy-saving grade evaluation method of the present invention can effectively monitor the building heating energy consumption, accurately evaluate the building energy-saving grade, and provide a scientific basis for the formulation and implementation of energy-saving strategies in practical applications. It has significant economic and social benefits and provides strong technical support for the development of the field of building energy conservation.

[0068] Example 2

[0069] This project selected a commercial complex in a southern city as the implementation target. With a total floor area of ​​approximately 50,000 square meters, the building houses a shopping mall, offices, and some dining and entertainment areas. Its heating system utilizes distributed gas boilers to meet the diverse heating needs of different functional areas. High-precision heat meters were installed on the outlet pipes of each distributed gas boiler and on the heating mains leading to each major functional area (such as each floor of the shopping mall and each section of the office building). The boiler outlet heat meters monitor the boiler's total heating output, while the heat meters on each regional main facilitate heat distribution within each area. During installation, strict heat meter installation specifications were adhered to, ensuring stable water flow within the pipes and no impurities interfering with measurement. The heat meter sensors were fully in contact with the hot water, and multiple calibration tests were performed to ensure measurement accuracy within ±1%. Temperature sensors were installed in strategic locations throughout the building, including heated rooms, public areas, and the outdoor environment. The indoor temperature sensors are intelligent, high-precision sensors installed approximately 2 meters below the ceiling, away from temperature fluctuations such as air conditioning vents, doors, and windows, to accurately reflect the actual average indoor temperature. Outdoor temperature sensors are installed in a dedicated meteorological observation enclosure on the shady side of the building. The enclosure features excellent ventilation, protection from rain, snow, and solar radiation, ensuring the accuracy and reliability of collected outdoor temperature data. Approximately 1,500 indoor temperature sensors and five outdoor temperature sensors are installed, enabling comprehensive monitoring of both internal and external temperatures within the building. Flow control valves are installed at branch points in each district heating pipeline. These valves, linked to temperature sensors and heat meters, automatically adjust the flow of hot water based on regional temperature demand. These flow control valves offer fast response and precise regulation. After installation, they undergo rigorous testing for sealing and flow characteristics to ensure accurate and stable flow control at varying openings, with a flow regulation error within ±3%. A data acquisition and transmission terminal is installed in the building's equipment management room. This terminal is connected to each heat meter, temperature sensor, and flow control valve via a wired connection. It collects real-time data and transmits it to a remote computing center via a 5G wireless communication module. The data acquisition and transmission terminal features data caching and preliminary data processing. In the event of a communication interruption, it can temporarily store data for a period of time and automatically upload it upon restoration, ensuring data integrity.

[0070] The computing center for this project was established in a specialized data center room located away from the building. Equipped with a professional air-conditioning system, the room maintains a temperature of 20°C-25°C and a humidity of 40%-60%. It features comprehensive safety measures, including fire protection, waterproofing, and lightning protection, as well as a robust network security system to prevent data leaks and malicious attacks. The computing center is equipped with a high-performance server cluster and a distributed computing architecture, capable of rapidly processing massive amounts of heating energy consumption data. The server storage system utilizes a redundant design to ensure the security and reliability of data storage, with a data storage period of over five years. Furthermore, the computing center has installed a specially developed intelligent analysis software platform for building heating energy consumption, integrating a variety of advanced data analysis algorithms and visualization tools to analyze, process, and display collected data in real time.

[0071] During the 2024 heating season, the system operated stably and collected data. The data collection frequency was set to every five minutes, with each measuring device and terminal device automatically collecting and transmitting data at the specified interval. For example, between 3:00 PM and 3:05 PM on February 15th, a heat meter on a main pipe on one floor of the mall recorded 12,000 kcal of heat consumption, while a heat meter in a zone in the office building recorded 8,000 kcal. Simultaneously, heat meters at the outlets of each distributed gas boiler recorded corresponding heating output data. This data was rapidly uploaded to the computing center via the data collection and transmission terminal, providing accurate baseline data for subsequent energy consumption analysis. At that time, the indoor temperature sensor in a store within the mall registered 20°C, the temperature in an office building registered 22°C, and the outdoor temperature sensor registered 8°C. This temperature data, along with the heat data, was synchronously transmitted to the computing center for comprehensive energy consumption analysis and energy conservation assessment.

[0072] Traditional building energy consumption calculations often treat each area as having equal weight, ignoring the impact of factors such as functional differences in different areas, changes in population density, and differences in heat usage time on energy consumption. The present invention proposes a regional energy consumption contribution algorithm based on dynamic weights. The algorithm first sets an initial weight coefficient for each area based on the functional attributes of each area of ​​the building (such as the business hours of the shopping mall, peak hours of personnel flow, office hours of office buildings, etc.), area size, and historical energy consumption data. Then, during operation, the weight coefficient of each area is dynamically adjusted based on the real-time collected personnel flow data (estimated by the intelligent security system or Wi-Fi hotspot connection number in the building), the deviation between the indoor temperature setting value and the actual value, and time factors. For example, when the shopping mall has a large flow of personnel on weekends or holidays, long business hours, and a high indoor temperature setting, its weight coefficient increases accordingly; the weight coefficient of the office building approaches zero during non-office hours at night. The calculation formula is as follows:

[0073] E rci =α i×E ri ×f(t i , P i ΔT i )

[0074] Among them, E rci is the real-time energy consumption contribution of region i, α i is the initial weight coefficient of region i, E ri is the measured energy consumption of region i, f(t i , P i , ΔT i ) is a dynamic adjustment function, t i is the time factor (such as working days, holidays, business hours, etc.), P i is the real-time personnel flow in area i, ΔT i is the deviation between the setpoint and actual indoor temperature in zone i. This algorithm allows for a more accurate determination of the actual contribution of each zone within a building to total energy consumption, providing a strong basis for the development of targeted energy-saving measures.

[0075] Taking into account that the outdoor ambient temperature has a significant impact on the heating energy consumption of the building, and traditional energy consumption calculations are mostly based on real-time temperature data, lacking forward-looking considerations for future temperature changes. The present invention designs an energy consumption pre-assessment algorithm based on ambient temperature prediction. The algorithm first collects historical temperature data, seasonal variation patterns, and recent weather forecast information from the local meteorological department, and uses time series analysis and machine learning algorithms (such as long short-term memory networks LSTM) to predict the outdoor ambient temperature for a period of time in the future (such as the next 24 hours, 48 ​​hours). Then, based on the predicted outdoor temperature curve and the thermal characteristics of the building (such as the heat transfer coefficient of the enclosing structure, the heating capacity curve of the heating system, etc.), combined with the current indoor temperature setting value and the real-time operating status of the building (such as the heat consumption of each area, the opening of the flow control valve, etc.), the heating energy consumption demand of the building in the future period is estimated.

[0076] For example, by analyzing historical temperature data and recent meteorological trends, it is predicted that outdoor temperatures will first drop and then rise over the next 24 hours, reaching a minimum of 5°C. Based on the building's thermal characteristics and current operating status, the algorithm calculates the changes in heat demand in each area at different outdoor temperatures, and thus estimates the building's total heating energy consumption range over the next 24 hours. This algorithm helps the heating system adjust its operating strategy in advance, such as optimizing boiler combustion parameters and adjusting the charging and discharging time of thermal storage devices, to achieve energy-saving operation and precise heating.

[0077] Efficiency evaluation of building heating systems is crucial for energy conservation and optimization, but traditional evaluation methods often rely solely on single heat measurement data or simple equipment operating parameters, making it difficult to fully and accurately reflect the system's true efficiency. This paper proposes a heating system efficiency evaluation algorithm that uses multi-source data fusion. This algorithm integrates multiple data sources, including heat data collected by heat meters, temperature data from temperature sensors, flow data from flow control valves, and gas consumption data from gas meters. First, based on the law of conservation of heat and the principles of heat transfer, the energy conversion and transfer efficiency of the heating system is calculated at each stage (such as heat generated by boiler combustion, heat loss from pipeline transportation, and heat dissipation at the terminal). Then, using data fusion technology, the data from different sources is weighted and integrated to eliminate data errors and uncertainties. For example, by comparing the theoretical heat calculated from gas consumption with the actual heat delivered to the building, combined with heat losses calculated from pipeline temperature differences, the overall efficiency of the heating system is comprehensively evaluated. Furthermore, the algorithm also considers the dynamic operating characteristics of the heating system, such as the number of boiler starts and stops and the changes in operating efficiency under different loads, thereby establishing a dynamic evaluation model for heating system efficiency. Through this algorithm, efficiency bottlenecks in the heating system can be discovered in a timely manner, such as excessive heat loss caused by poor pipe insulation and low boiler efficiency under low load, providing a clear direction for system optimization and transformation.

[0078] In building heating, different users have vastly different heating habits and behavior patterns. Traditional, unified heating strategies struggle to meet their individual needs and can easily lead to energy waste. This paper develops a personalized energy-saving recommendation algorithm based on user behavior pattern recognition. This algorithm collects historical heating data from users in various areas of a building (such as historical indoor temperature settings, heating time patterns, and door and window opening and closing status). Using cluster analysis and pattern recognition techniques, it categorizes users into distinct behavior pattern groups. For example, users can be categorized as "early to late heating users" (e.g., office workers), "intermittent heating users" (e.g., some stores in shopping malls), and "long-term, stable heating users" (e.g., hotel rooms). Then, personalized energy-saving recommendations are developed for each behavior pattern group based on their heating characteristics and energy-saving potential. For example, for "early to late heating users," it's recommended to start the heating system half an hour before work and appropriately lower the indoor temperature setpoint after get off work. For "intermittent heating users," it's recommended to appropriately lower the heating temperature or shut off the heating valve during non-business hours and preheat the room before business hours. These personalized energy-saving recommendations are pushed to users through smart terminals within the building (such as mobile phone apps, indoor display screens, etc.), guiding users to optimize their heating behavior and achieve energy saving and consumption reduction.

[0079] Failures in heating equipment not only affect heating efficiency but can also lead to energy waste and even safety incidents. Traditional equipment fault detection relies on sensor alarms within the equipment itself, which can be time-consuming. This paper proposes an equipment fault early warning algorithm based on energy consumption data trend analysis. This algorithm performs long-term monitoring and trend analysis of energy consumption data from various equipment in the heating system (such as gas boilers, circulating water pumps, and heat meters). By building a model of the equipment's energy consumption data under normal operating conditions (e.g., using linear regression or neural networks to fit the relationship between equipment energy consumption and operating parameters), the algorithm compares the deviation between the current energy consumption data and the normal model in real time. When the deviation exceeds a set threshold, the system uses information such as the equipment's operating time and recent maintenance history to comprehensively determine whether the equipment has a potential fault. For example, if a gas boiler's energy consumption data continues to rise over a period of time and exceeds a certain percentage of the normal model, combined with the boiler's long operating time and lack of recent maintenance, the system will issue a fault warning, prompting personnel to inspect and perform maintenance on the boiler, such as cleaning the burner and checking the heat exchange tubes for scaling. This fault warning algorithm based on energy consumption data trend analysis can detect potential equipment problems in advance, improve the reliability and safety of equipment operation, and reduce energy losses caused by equipment failures.

[0080] Based on the various energy consumption indicators and analysis results calculated by the above-mentioned innovative algorithm, combined with the average energy consumption level of local commercial buildings of the same type and relevant national energy-saving standards, the energy-saving level of this building is evaluated. For example, after detailed calculation and comparative analysis, if the unit area heating energy consumption of this building is 30% lower than the average level of local buildings of the same type, and it performs well in terms of heating system efficiency, regional energy consumption balance control, and user energy-saving participation, according to the established energy-saving level evaluation system, it can be determined that this building has achieved a higher energy-saving level (such as level 1 energy-saving). The evaluation results not only reflect the current energy-saving status of the building, but also provide clear directions and goals for further energy-saving optimization.

[0081] Based on the analysis results of the heating system efficiency evaluation algorithm based on multi-source data fusion, the heating system is optimized in a targeted manner. For areas of poor pipe insulation found, the pipe insulation layer is thickened and repaired to reduce heat loss. To address the problem of low boiler operating efficiency under low load, the boiler's combustion control system is optimized, and intelligent frequency conversion technology is used to automatically adjust the boiler's combustion power according to actual heating demand, improving the boiler's operating efficiency under different loads. At the same time, based on the energy consumption pre-assessment algorithm based on ambient temperature prediction, the heating system's operating strategy is adjusted in advance. For example, during daytime periods with higher temperatures, the heating temperature or heat flow rate is appropriately lowered; during nighttime periods with lower temperatures, the heat storage capacity of the heat storage device is increased in advance to ensure the building's heating stability during low-temperature periods.

[0082] Energy-saving plans are developed based on personalized energy-saving recommendation algorithms based on user behavior pattern recognition, and energy-saving publicity and guidance are provided to users through various channels. Energy-saving posters are posted in public areas of the building, and short energy-saving videos are played to introduce energy-saving tips based on different behavior patterns. Personalized energy-saving suggestions and heating reminders are pushed to users through mobile apps. For example, users who use heating early and late at night are reminded to turn off unnecessary electrical appliances when leaving get off work to reduce indoor heat loss, and users who use heating intermittently are encouraged to arrange their business hours reasonably to avoid unnecessary heating waste. At the same time, an energy-saving reward mechanism is established to provide certain rewards, such as electricity fee reductions and certificates of honor, to users or regions that actively respond to energy-saving recommendations and achieve energy-saving targets, thereby increasing user enthusiasm and initiative in participating in energy conservation.

[0083] Based on warnings from equipment failure warning algorithms based on energy consumption data trend analysis, timely maintenance and upkeep are performed on heating equipment. Gas boilers undergo regular maintenance, including burner cleaning, heat exchange tube descaling, and safety accessory inspections. Circulating water pumps undergo bearing lubrication, impeller inspection, and motor maintenance. Heat meters undergo precision calibration and sensor maintenance. For older, energy-intensive equipment, such as inefficient gas boilers or flow control valves, an upgrade plan is developed to gradually replace them with more efficient and energy-efficient equipment, further improving the overall performance and energy efficiency of the heating system.

[0084] After implementing the aforementioned energy-saving strategies for one heating season, a comprehensive evaluation of the building's heating energy consumption and energy-saving effects was conducted. Comparing energy consumption data before and after the energy-saving strategies' implementation revealed a reduction of approximately 20%. For example, under the same outdoor temperature and building usage conditions, daily heating energy consumption was approximately 30,000 kcal before the energy-saving strategies were implemented, but it decreased to approximately 24,000 kcal after implementation. Energy consumption in various areas was also effectively controlled, with energy consumption in the shopping mall area decreasing by 18% and in the office area by 22%, fully demonstrating the effectiveness of the energy-saving strategies. A user satisfaction survey revealed a significant increase in user satisfaction with heating performance and energy-saving measures. Before the energy-saving strategies were implemented, approximately 30% of users reported experiencing unstable indoor temperatures or insufficient heating. After the energy-saving strategies were implemented, this percentage dropped to less than 10%. Furthermore, users expressed positive feedback on the energy-saving publicity and guidance efforts, with approximately 80% stating that the personalized energy-saving recommendations they received had a positive impact on their heating behavior and expressed their willingness to continue cooperating with the building's energy-saving efforts, further promoting the sustainable development of the building's energy-saving efforts.

[0085] In summary, the application of the building heating energy consumption detection system and energy-saving rating evaluation method in civil and commercial buildings, through the implementation of a series of innovative algorithms, enables precise monitoring, scientific analysis, and effective control of building heating energy consumption, significantly improving building energy efficiency. This provides strong technical support for energy conservation, emission reduction, and sustainable operation of commercial buildings, and has broad application prospects and promotional value. It provides practical verification for building energy-saving design, assists heating companies in energy-saving operations, and provides an accurate and effective scientific basis for national building energy-saving rating assessments.

Claims

1. A building heating energy consumption detection system, characterized in that: include: Building heat meters or household heat meters are used to measure the heat consumption data of buildings or households; Room thermostat, installed in each heated room to detect indoor temperature; Outdoor temperature collectors are installed in each building to collect outdoor temperature data; Electric valves are installed in each household's heating pipes to adjust the hydraulic and thermal balance of the heating system; The computing center receives and processes data from each measuring device through NB wireless remote communication; Among them, building heat meters, household heat meters, room thermostats, and outdoor temperature collector measurement devices all use NB wireless remote communication, uploading the collected data to the computing center for processing at set intervals. The installation of each device must meet specific location and environmental requirements to ensure the accuracy and reliability of data collection.

2. The building heating energy consumption detection system according to claim 1, characterized in that: The building heat meter is based on a heat metering algorithm that measures the hot water flow rate and the supply and return water temperature difference in the heating pipe and uses a heat calculation formula to accurately calculate the building heat consumption data. The formula is Q=cmΔT, where c is the specific heat capacity of water, m is the mass flow rate of water, and ΔT is the supply and return water temperature difference.

3. The building heating energy consumption detection system according to claim 1, characterized in that: The installation requirements of the device are as follows: All heated rooms with exterior walls and radiators in the building must be equipped with room thermostats. They should not be installed near exterior walls, radiators, or fan coil outlets. They should be installed in the center of the indoor space, away from heat and cold sources, and approximately 1.5 meters above the ground. Each building is equipped with an outdoor temperature collector, and the temperature probe is installed in the regular blinds to ensure air circulation, rain protection, snow protection and sunlight protection; A building heat meter is installed on the heating pipe of each building or a household heat meter is installed for each user. The installation position is on the straight pipe section of the heating pipe, and the length of the upstream straight pipe section is not less than 10 times the pipe diameter, and the downstream straight pipe section is The segment length is not less than 5 times the pipe diameter, and the heat meter sensor is in full contact with the hot water in the pipe; The building energy consumption management department has a main control computing center, which uses NB remote communication to receive thermal data collected by various measuring devices in the building. The computing center is set up in a computer room that meets constant temperature, constant humidity, dustproof and anti-static requirements, and is equipped with a complete network security protection system.

4. The building heating energy consumption detection system according to claim 1 has the following system workflow: all measurement parameters are transmitted once every 10 minutes and data processing is performed. When the collection period arrives, the building heat meter and household heat meter measure heat data, the room thermostat and the outdoor temperature collector respectively collect indoor temperature and outdoor temperature data, and the data is sent to the computing center through the NB wireless remote communication module. The computing center verifies the integrity and accuracy of the data, eliminates abnormal data, and calculates key energy consumption parameters according to the preset energy consumption calculation method. The calculation results are compared and analyzed with the local non-energy-saving building heating design parameters to determine the building energy-saving level. The energy consumption data, calculation results and energy-saving level information are organized and stored, and an energy consumption report is generated.

5. A method for calculating a temperature difference heat index (energy consumption coefficient), characterized in that: There are three calculation methods: Calculation method of temperature difference thermal index 1: Calculation method 2 for temperature difference thermal index: Method 3 for calculating temperature difference heat index: Due to different indoor areas, the indoor temperature weights are also different. In formulas (1) and (2), q c The calculation may have a few thousandths of an error, so Algorithm 3 can be used to perform accurate calculations for each household, that is, in: q c It is the temperature difference heat index (energy consumption coefficient), the unit is W / m 2 hΔ℃; Q is the heat consumption, which is the heat consumption of the building's heat meter, or the total heat consumption of each household's heat meter, and the unit is W; A is the total heating area, unit is m 2 ; T p is the actual average indoor temperature, in °C; T w is the actual average outdoor temperature in °C; q is the area heat index, the unit is W / m 2 h; A1、A2、A3、A n is the heating area of ​​each user, in m 2 ; T1, T2, T3, T n It is the indoor temperature of each user, in °C.

6. The method for calculating the temperature difference thermal index according to claim 4, characterized in that: It is further used to calculate the building energy consumption heat index, and the calculation formula is as follows: q a Energy consumption index, unit is W / m 2 ; q c It is the temperature difference heat index, the unit is W / m 2 hΔ℃; T np is the actual average indoor temperature, in °C; T wp is the actual average outdoor temperature in °C.

7. The method for calculating the temperature difference thermal index according to claim 5, characterized in that: It is further used to calculate the actual temperature difference load rate, and the calculation formula is:

8. The method for calculating the temperature difference heat index according to claim 5 is further used to calculate the converted design temperature difference heat consumption, and the calculation formula is: where Q c The unit is W, which is the heat consumed by the temperature difference.

9. A building energy-saving grade evaluation method, characterized in that: Taking the local non-energy-saving building heating design parameters as a reference, the country sets five building energy-saving grades based on the assessment of non-energy-saving buildings. By comparing the energy-saving building thermal index with the non-energy-saving building design thermal index, the formula Determine the energy conservation level, where: α is the energy-saving grade coefficient; q c It is the temperature difference thermal index of energy-saving buildings; q cj It is the design temperature difference thermal index of non-energy-saving buildings; Q c Design temperature difference heat consumption for energy-saving buildings; q j Design thermal index for non-energy-saving buildings.

10. A comprehensive application method for building heating energy consumption detection and energy-saving level evaluation, characterized in that: Data is collected using the building heating energy consumption detection system as described in claim 1, relevant energy consumption indicators are calculated using the temperature difference heat index calculation method as described in claim 5, the building energy conservation level is determined according to the building energy conservation level evaluation method as described in claim 9, and a building heating energy conservation optimization strategy is formulated based on the evaluation results. The energy conservation optimization strategy includes but is not limited to adjusting the operating parameters of the heating system, optimizing the insulation performance of the building envelope structure, and promoting the application of energy-saving equipment.

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