Heating energy consumption assessment and early warning model

Abstract

This invention relates to the field of energy consumption monitoring and intelligent early warning technology for heating systems, and particularly to a heating energy consumption assessment and early warning model. The invention includes a data acquisition module, a data preprocessing module, a multi-source data fusion module, an energy consumption feature extraction module, an energy consumption benchmark construction module, an energy consumption assessment module, an anomaly identification module, an early warning output module, and a model update module. The multi-source data includes, but is not limited to, heat source operation data, heat exchange station operation data, pipeline network operation data, user-side heat consumption data, building attribute data, meteorological data, and historical energy consumption data. This invention can improve the accuracy of heating energy consumption assessment, enhance the ability to identify abnormal energy consumption, reduce energy loss in heating systems, improve the timeliness and foresight of early warnings, improve the stability of heating operation, reduce false alarms and missed alarms, improve the intelligence and refinement of heating management, reduce manual analysis and management costs, and improve system adaptability and scalability.

Description

Technical Field

[0001] This invention relates to the field of energy consumption monitoring and intelligent early warning technology for heating systems, and particularly to a heating energy consumption rating and early warning model, belonging to the fields of smart heating, energy big data analysis, building energy conservation control, and artificial intelligence-assisted decision-making. This model is applicable to energy consumption analysis of heat sources, heat networks, heat exchange stations, and user-side components in centralized heating systems, and can be used for heating energy consumption level evaluation, abnormal energy consumption identification, operational status diagnosis, and energy risk early warning. Background Technology

[0002] With the continuous expansion of urban centralized heating, the operation and management of heating systems are gradually shifting from traditional manual experience-based control to digital, intelligent, and refined management. A centralized heating system typically consists of a heat source, primary pipe network, heat exchange stations, secondary pipe network, and user-side terminals. Its operation involves multiple aspects, including heat supply, flow regulation, temperature control, pressure balance, energy consumption statistics, and ensuring adequate room temperature for users. Heating energy consumption levels are not only affected by factors such as heat source efficiency, pipe network distribution capacity, and the operating status of heat exchange stations, but are also closely related to outdoor weather conditions, building insulation performance, user heating behavior, and the hydraulic balance of the pipe network. Therefore, accurately evaluating heating energy consumption levels and promptly identifying abnormal energy consumption and operational risks are crucial issues for energy conservation, consumption reduction, and safe and stable operation of heating systems.

[0003] Most existing heating energy consumption management products or systems primarily rely on data acquisition, operational monitoring, and manual statistical analysis. Their main structure typically includes a data acquisition terminal, a communication transmission module, a heating monitoring platform, and a data display module. The data acquisition terminal collects operational data such as temperature, pressure, flow rate, heat output, electricity consumption, and water consumption from the heat source, heat exchange station, or user side. The communication transmission module uploads the field data to the monitoring platform. The heating monitoring platform stores, displays, and performs basic statistical analysis on the collected data. The data display module shows the heating operation status through reports, graphs, or large screens. Some existing systems can also trigger alarms based on set thresholds for conditions such as excessively high temperature, abnormal pressure, insufficient flow rate, and excessive energy consumption.

[0004] However, existing technologies still have the following shortcomings in terms of heating energy consumption evaluation and early warning: First, existing methods for evaluating heating energy consumption largely rely on single indicators or human experience. For example, judging heating energy consumption levels based solely on single indicators such as heat output per unit area, heat consumption, electricity consumption, or water consumption is insufficient to comprehensively reflect the true energy consumption status under different regions, building types, meteorological conditions, and operating conditions. Because heating systems exhibit significant seasonal, regional, and dynamic characteristics, evaluation based on a single indicator can easily lead to biased results and make it difficult to accurately distinguish between normal, high, and abnormal energy consumption. Second, existing systems lack a tiered evaluation mechanism for heating energy consumption. Traditional systems typically only display current energy consumption figures or historical statistics, failing to classify energy consumption levels into multiple tiers based on heating operation characteristics, historical baselines, meteorological correction factors, and building heating demands. Therefore, managers struggle to promptly determine whether a heat source, heat exchange station, building, or user unit is in a low-energy-consumption, normal-energy-consumption, high-energy-consumption, or abnormal-energy-consumption state, hindering heating companies from implementing refined energy-saving management. Third, existing early warning methods mostly rely on fixed threshold alarms, which have a low level of intelligence. Fixed thresholds are typically set manually and cannot be dynamically adjusted based on changes in outdoor temperature, heating load, historical operating patterns, and regional differences in heating consumption. When the external environment changes, fixed thresholds are prone to false alarms or missed alarms. For example, increased heating energy consumption may be normal in extremely cold weather; however, a slight increase in energy consumption may indicate an operational anomaly when the temperature is high. Existing threshold alarm methods struggle to accurately identify such dynamic changes. Fourth, existing technologies do not adequately integrate and utilize multi-source data. Heating energy consumption is affected by various factors such as heat volume, flow rate, supply and return water temperature, outdoor air temperature, building area, user room temperature, equipment operating status, and historical energy consumption. Existing systems often only monitor some operating parameters independently, lacking joint analysis of multi-source heterogeneous data. This makes it difficult to establish a deep correlation between heating energy consumption and operating status, and also makes it difficult to assist in judging the causes of abnormal energy consumption. Fifth, existing systems typically focus on post-event statistics and lack proactive early warning capabilities. Most heating management platforms only conduct energy consumption summary analysis after the heating cycle ends, or only issue alarms after energy consumption has significantly exceeded standards. They cannot identify potential high-energy-consumption trends, operational deviations, and heating risks in advance. Therefore, managers often can only passively deal with problems and find it difficult to take timely control measures, which easily leads to energy waste, increased operating costs, and a higher risk of user complaints. Sixth, existing heating energy consumption analysis models lack adaptability and scalability. Different cities, heat source types, heat exchange station sizes, and building users exhibit varying operational patterns, making it difficult for traditional models to be continuously updated and optimized based on actual operational data. When heating areas expand, equipment is upgraded, or building heating structures change, existing evaluation criteria and early warning rules may no longer be applicable, affecting the long-term operational performance of the system.

[0005] Therefore, while existing heating energy consumption management technologies can achieve basic data collection, operational monitoring, and simple alarms, they still have significant shortcomings in comprehensive energy consumption assessment, dynamic threshold adjustment, multi-source data fusion, abnormal trend identification, and intelligent early warning. To address these issues, it is necessary to propose a heating energy consumption assessment and early warning model. This model would comprehensively analyze heating operation data, meteorological data, building attribute data, user heating data, and historical energy consumption data to establish a heating energy consumption level evaluation mechanism and a dynamic early warning mechanism. This would enable accurate assessment of heating energy consumption status, timely identification of abnormal energy consumption, and early warning of potential risks, thereby improving the energy-saving management level and operational safety of the heating system. Summary of the Invention

[0006] This invention is a heating energy consumption assessment and early warning model, comprising: The data acquisition module is used to acquire multi-source data during the operation of the heating system; The data preprocessing module is used to standardize the collected data; The multi-source data fusion module is used to jointly model heating operation data, meteorological data, building attribute data, and historical energy consumption data; The energy consumption feature extraction module is used to extract key features reflecting the heating energy consumption level and operating status from the fused data. The energy consumption benchmark construction module is used to establish energy consumption benchmark values ​​for different evaluation objects under different operating conditions; The energy consumption assessment module is used to determine the relationship between the current energy consumption characteristics of the assessment object and the dynamic energy consumption benchmark. Anomaly identification module is used to identify abnormal energy consumption types and potential risks during the operation of the heating system; The early warning output module is used to output early warning information based on the energy consumption assessment results and anomaly identification results; The model update module is used to continuously optimize the energy consumption benchmark and rating rules based on new heating operation data.

[0007] As a further improvement of the present invention, the multi-source data includes, but is not limited to, heat source operation data, heat exchange station operation data, pipeline operation data, user-side heat consumption data, building attribute data, meteorological data, and historical energy consumption data.

[0008] As a further improvement of the present invention, the data preprocessing module first performs outlier detection, missing value imputation, time alignment, and unit unification on the raw data; then, according to the heating operation cycle, it unifies data from different sources and frequencies to the same time scale.

[0009] As a further improvement of the present invention, the multi-source data fusion module takes heat source, heat exchange station, building, unit or user as evaluation object, establishes a unified data index relationship, and associates the heating operation parameters, building parameters, environmental parameters and energy consumption parameters of the same evaluation object in the same time period to form a comprehensive energy consumption analysis sample.

[0010] As a further improvement of the present invention, the key features in the energy consumption feature extraction module include heat consumption per unit area, electricity consumption per unit area, water consumption per unit area, supply and return water temperature difference, heat load intensity, meteorological correction heat consumption, historical same period deviation rate, room temperature compliance rate, pipeline distribution efficiency, heat exchange efficiency, water replenishment anomaly rate, and operation fluctuation coefficient.

[0011] As a further improvement of the present invention, the energy consumption benchmark construction module constructs a dynamic energy consumption benchmark based on historical normal operation data, building attributes, outdoor temperature, heating area and user heat demand.

[0012] As a further improvement of the present invention, the anomaly identification module classifies and identifies abnormal situations based on energy consumption assessment results, operating parameter change trends, and the correlation between multi-source data.

[0013] As a further improvement of the present invention, the warning information includes warning level, warning object, warning time, abnormal indicators, degree of deviation, possible causes and handling suggestions.

[0014] As a further improvement of the present invention, the model update module can periodically update the historical benchmark interval, meteorological correction parameters, building energy consumption characteristics and anomaly identification rules as the heating season operation data accumulates, so that the evaluation results and early warning results can adapt to the changes in different heating areas, different building types and different operation stages.

[0015] As a further improvement of the present invention, the heating energy consumption assessment and early warning model includes the following steps for assessing and issuing early warnings: The first step is to collect heating system operation data, building attribute data, user-side heating data, meteorological data, and historical energy consumption data; The second step is to perform data cleaning, missing value handling, outlier correction, time alignment, unit unification and standardization on the collected multi-source data. The third step is to establish multi-source data correlation relationships using heat sources, heat exchange stations, buildings, units, or users as evaluation objects to form a comprehensive energy consumption analysis sample. The fourth step is to extract energy consumption characteristics such as heat consumption per unit area, electricity consumption per unit area, water consumption per unit area, supply and return water temperature difference, heat load intensity, meteorological correction heat consumption, historical same period deviation rate, room temperature compliance rate, heat exchange efficiency, and operating fluctuation coefficient. The fifth step is to construct a dynamic energy consumption benchmark range based on historical normal operation data, outdoor meteorological conditions, building attributes, and heating load. The sixth step is to compare the current energy consumption characteristics with the dynamic energy consumption benchmark range, and, in conjunction with the room temperature compliance and operational stability, classify and grade the heating energy consumption level. The seventh step is to identify abnormal energy consumption types and potential operational risks based on the assessment results and the changing trends of operating parameters. The eighth step is to output the corresponding level of early warning information based on the degree of abnormality, and to provide the abnormal indicators, the degree of deviation, possible causes and control suggestions; The ninth step is to update and optimize the energy consumption benchmark, rating rules, and early warning rules based on subsequent operational data and feedback from manual handling.

[0016] The working principle of this invention is as follows: the energy consumption of a heating system is not determined by a single indicator, but is jointly influenced by heating load, outdoor temperature, building characteristics, equipment operating status, and user heat demand. This invention collects and integrates multi-source data to first establish a dynamic energy consumption benchmark for different objects under different operating conditions. Then, it compares the current energy consumption status with this dynamic benchmark, and combines this with the room temperature compliance rate, historical deviations, and operational fluctuations to classify and evaluate the energy consumption level. When the model detects that the current energy consumption significantly deviates from the reasonable benchmark, or that energy consumption increases but the heating effect does not improve simultaneously, it determines that there is an abnormal energy consumption risk and outputs early warning information. Thus, this invention transforms traditional "post-event statistics" into "process monitoring, dynamic evaluation, and early warning."

[0017] Compared with the prior art, the present invention has the following advantages: First, the present invention adopts a multi-index comprehensive evaluation method, no longer relying solely on single indicators such as heat consumption, electricity consumption or water consumption per unit area, which can more comprehensively reflect the true energy consumption status of the heating system. Second, the present invention establishes a heating energy consumption rating mechanism, which can divide the energy consumption level of different evaluation objects into different levels, making it easier for heating companies to quickly identify high-energy-consuming areas, high-energy-consuming heat exchange stations, high-energy-consuming buildings, or abnormal user units. Third, the present invention uses a dynamic energy consumption benchmark instead of a fixed alarm threshold, which can automatically adjust the evaluation criteria according to outdoor temperature, building attributes, heating load and historical operating patterns, thereby reducing false alarms and missed alarms. Fourth, this invention integrates heating operation data, meteorological data, building data, user-side data, and historical energy consumption data, which can improve the accuracy of abnormal energy consumption identification and provide a basis for abnormal cause analysis. Fifth, this invention has early warning capabilities, and can issue timely warnings when energy consumption continues to rise, operating parameters deviate or abnormal trends form, so that managers can take control measures in advance to reduce energy waste and operating costs; Sixth, the present invention has good adaptability and scalability, and can be applied to heating energy consumption management at different levels such as heat source, heat exchange station, building, unit and user. It can also adjust parameters and update models according to different cities, different heating companies and different building types. Seventh, this invention can improve the level of refined management of heating systems, help achieve energy conservation and consumption reduction, reduce human judgment errors, improve operating efficiency, reduce user complaints, and provide core algorithm support for the construction of smart heating platforms. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall structure of the heating energy consumption assessment and early warning model of the present invention; Figure 2 This is a flowchart of the data processing for the heating energy consumption assessment and early warning model of the present invention; Figure 3 This is a schematic diagram illustrating the construction of the dynamic energy consumption benchmark of the present invention; Figure 4 This is a schematic diagram illustrating the heating energy consumption rating of this invention; Figure 5 This is a schematic diagram of the abnormal energy consumption identification and early warning output of the present invention; Figure 6 This is a schematic diagram illustrating the application scenario of the present invention in a heating system. Detailed Implementation

[0019] This invention provides a heating energy consumption assessment and early warning model, applicable to centralized heating systems, district heating systems, heat exchange station heating management systems, building heating energy consumption management systems, and user-side heating monitoring systems. This model can be deployed in smart heating platforms, heating dispatching platforms, energy management platforms, or cloud servers, or it can be embedded as an independent algorithm module into existing heating monitoring systems.

[0020] like Figure 1 As shown, the heating energy consumption assessment and early warning model of this invention mainly includes a data acquisition module, a data preprocessing module, a multi-source data fusion module, an energy consumption feature extraction module, an energy consumption benchmark construction module, an energy consumption assessment module, an anomaly identification module, an early warning output module, and a model update module. The modules are connected through data interfaces, databases, or platform services, and data flows in the direction of "acquisition—preprocessing—fusion—feature extraction—benchmark construction—energy consumption assessment—anomaly identification—early warning output—model update".

[0021] The data acquisition module serves as the model's data input, connecting to the heat source, primary piping network, heat exchange station, secondary piping network, building units, and user-side equipment. The data acquisition module can acquire data through temperature sensors, pressure sensors, flow meters, heat meters, electricity meters, water meters, room temperature data loggers, building metering devices, heat exchange station control cabinets, meteorological interfaces, and historical energy consumption databases. The acquired data includes heat source operation data, heat exchange station operation data, piping network operation data, user-side heat consumption data, building attribute data, meteorological data, and historical energy consumption data.

[0022] Heat source operation data includes boiler or power plant heating load, supply water temperature, return water temperature, total heat, fuel consumption, electricity consumption, water consumption, and operating time; heat exchange station operation data includes primary side supply water temperature, primary side return water temperature, secondary side supply water temperature, secondary side return water temperature, primary side flow rate, secondary side flow rate, supply and return water pressure, circulating pump frequency, makeup water volume, and heat exchange; pipeline network operation data includes pipeline branch flow rate, pressure, temperature, and hydraulic balance status; user-side heat consumption data includes user room temperature, building heat consumption, unit heat consumption, household heat consumption, and room temperature compliance status; building attribute data includes building area, building type, building year, insulation status of the building envelope, number of floors, and number of users; meteorological data includes outdoor temperature, daily average temperature, minimum temperature, maximum temperature, wind speed, humidity, and weather conditions; historical energy consumption data includes historical heat consumption, electricity consumption, water consumption, energy consumption per unit area, and historical operating parameters for the same period.

[0023] like Figure 2 As shown, after acquiring the above data, the data acquisition module transmits the raw data to the data preprocessing module. The data preprocessing module cleans and standardizes the raw data. Specifically, this includes: filling in missing data, identifying and correcting outliers in obviously unreasonable data, aligning the time of data uploaded from different devices, unifying the units of data from different units, and normalizing or standardizing continuous data. For data loss caused by short-term sensor interruptions, interpolation between previous and subsequent times, historical averages, or averages of similar objects can be used to fill the gaps. For data that clearly exceeds the normal physical range, such as sudden temperature changes, negative flow rates, or abnormal heat fluctuations, it can be removed or corrected according to threshold rules or statistical rules.

[0024] After preprocessing, the data enters the multi-source data fusion module. This module uses heat sources, heat exchange stations, network zones, buildings, units, or users as evaluation objects, establishing a unified data index relationship. This module correlates the operational data, meteorological data, building attribute data, user room temperature data, and historical energy consumption data of the same evaluation object within the same time period to form a comprehensive energy consumption analysis sample. For example, for a heat exchange station, the model fuses the station's supply and return water temperatures, flow rates, pressures, heat exchange capacity, makeup water volume, service building area, number of service users, outdoor temperature, and historical energy consumption for the same period to form the station's energy consumption analysis data for a specific time period. For a building, the model fuses the building's heat consumption per unit area, user room temperature, building type, building area, outdoor temperature, and historical energy consumption for the same period to form the building's energy consumption evaluation data.

[0025] After multi-source data fusion is completed, the data enters the energy consumption feature extraction module. This module extracts key indicators that reflect the heating energy consumption level and operating status. These key indicators include heat consumption per unit area, electricity consumption per unit area, water consumption per unit area, supply and return water temperature difference, heat load intensity, meteorologically corrected heat consumption, historical same-period deviation rate, room temperature compliance rate, heat exchange efficiency, pipeline distribution efficiency, water replenishment anomaly rate, and operating fluctuation coefficient.

[0026] Among them, heat consumption per unit area reflects the heat consumed per unit heating area; electricity consumption per unit area reflects the power consumption level of equipment such as circulating pumps and makeup water pumps; water consumption per unit area reflects makeup water, leakage, or abnormal drainage; supply and return water temperature difference reflects the heat exchange effect and system operating status; heat load intensity reflects the heating load per unit time; meteorological correction heat consumption eliminates the impact of outdoor temperature changes on energy consumption evaluation; historical same period deviation rate reflects the difference between current energy consumption and historical normal levels; room temperature compliance rate is used to determine whether the heating effect meets user needs; and operating fluctuation coefficient is used to determine whether there are unstable operating phenomena in the heating system.

[0027] like Figure 3 As shown, the feature data output by the energy consumption feature extraction module enters the energy consumption benchmark construction module. The energy consumption benchmark construction module is used to establish a dynamic energy consumption benchmark interval. This dynamic energy consumption benchmark is not a fixed, single threshold, but is dynamically generated based on historical normal operation data, outdoor temperature, building area, building type, heating load, and room temperature compliance. Specifically, the model first selects periods in historical operation where heating performance meets standards, equipment operates normally, and there are no obvious abnormal alarms as normal samples; then it groups these samples according to outdoor temperature range, building type, heating area, and heating object category; finally, it calculates the reasonable energy consumption range under different operating conditions to form the dynamic energy consumption benchmark interval.

[0028] For example, when the outdoor temperature is low, the heating load increases, and the reasonable energy consumption benchmark is raised accordingly; when the outdoor temperature rises, the heating load decreases, and the reasonable energy consumption benchmark is lowered accordingly. For buildings with poor insulation or older construction dates, the energy consumption benchmark can be appropriately higher than that of buildings with better insulation; for heat exchange stations with a large heating area and a large number of users, the energy consumption benchmark can be adjusted according to the actual heating scale. Through the above methods, the model can automatically adjust the evaluation criteria according to actual operating conditions, avoiding false alarms or missed alarms caused by traditional fixed threshold alarm methods under different weather and building scenarios.

[0029] After the energy consumption baseline is constructed, the current energy consumption characteristics and the dynamic energy consumption baseline are jointly input into the energy consumption rating module. For example... Figure 4 As shown, the energy consumption rating module classifies heating energy consumption levels based on the deviation between the current energy consumption value of the evaluated object and the dynamic benchmark range, combined with the room temperature compliance rate, historical deviation rate, and operational stability. Specifically, the energy consumption levels can be divided into five levels: Level 1 is low energy consumption, Level 2 is normal energy consumption, Level 3 is slightly high, Level 4 is moderately high, and Level 5 is severely abnormal.

[0030] When the meteorological correction heat consumption of the evaluated object is lower than or close to the lower limit of the dynamic benchmark, and the room temperature meets the standard and the operation is stable, it is judged as low energy consumption or normal energy consumption; when the energy consumption of the evaluated object is slightly higher than the dynamic benchmark, but the room temperature basically meets the standard and the fluctuation of the operating parameters is small, it is judged as slightly high; when the energy consumption of the evaluated object is significantly higher than the dynamic benchmark, and the historical deviation is large or the operation fluctuates significantly, it is judged as moderately high; when the energy consumption of the evaluated object is consistently significantly higher than the dynamic benchmark, and is accompanied by abnormal water replenishment, abnormal supply and return water temperature, abnormal pressure, abnormal flow, or room temperature not meeting the standard, it is judged as severely abnormal.

[0031] After the energy consumption assessment module outputs the assessment results, the data enters the anomaly detection module. For example... Figure 5 As shown, the anomaly identification module identifies abnormal energy consumption types and potential risks based on the rating results, energy consumption characteristic change trends, and the correlation between operating parameters. The anomaly identification module can identify anomalies including abnormally high heat consumption, abnormally high electricity consumption, abnormally high water consumption, abnormal water replenishment, abnormal differences between supply and return water temperatures, abnormal flow rates, abnormal pressure, network imbalance, decreased heat exchange efficiency, substandard room temperature but high energy consumption, high energy consumption after meteorological correction, and excessive deviation from historical data.

[0032] For example, when the heat consumption per unit area increases significantly, but the room temperature of users does not increase synchronously, the model judges that there may be problems such as excessive heating, increased pipe network losses, or poor building insulation performance; when the water replenishment volume continues to increase and the pressure fluctuates, the model judges that there may be pipe network leakage or abnormal water replenishment system; when the temperature difference between supply and return water is too small and the flow rate is large, the model judges that there may be hydraulic imbalance, decreased heat exchange efficiency, or unreasonable setting of circulating pump operating parameters; when the power consumption increases but the heat exchange does not increase significantly, the model judges that there may be inefficient operation of circulating pump or unreasonable control strategy.

[0033] The anomaly identification module transmits the anomaly type, anomaly indicator, degree of deviation, and possible causes to the early warning output module. The early warning output module generates corresponding early warning levels based on the degree of anomaly and the scope of impact. Early warning levels are categorized into alert, general, important, and severe. Alert alerts remind managers to pay attention to energy consumption trends; general alerts indicate slightly elevated energy consumption in localized areas; important alerts indicate persistent energy consumption anomalies or significant operational deviations; and severe alerts indicate potential equipment failure, pipeline leaks, widespread high energy consumption, or risks to heating services.

[0034] The early warning output module outputs warning information including the warning object, warning time, warning level, abnormal indicators, degree of deviation, possible causes, and handling suggestions. Warning information can be displayed to management personnel through the smart heating platform, dispatch screen, computer terminal, mobile application, SMS, WeChat, pop-ups, or reports. For example, if a heat exchange station experiences moderately high energy consumption, the system can output a warning stating, "The heat consumption per unit area of ​​a certain heat exchange station is 30% higher than the dynamic benchmark, and the room temperature compliance rate has not significantly improved. It is recommended to check the secondary side flow settings, circulation pump frequency, and heat exchange efficiency." Similarly, if an area experiences abnormal water replenishment, the system can output a warning stating, "The water replenishment volume in a certain pipeline zone is continuously increasing, and pressure fluctuations are increasing, indicating a risk of pipeline leakage. It is recommended to arrange an inspection."

[0035] After an early warning is issued, the model update module receives new operational data, manual handling results, and subsequent verification results to update the energy consumption benchmark and early warning rules. The model update module can update historical normal samples, dynamic benchmark intervals, meteorological correction parameters, anomaly identification rules, and ranking thresholds based on the continuous accumulation of operational data during the heating season. For example, when a heating area completes pipeline renovation or building energy-saving renovation, its historical energy consumption level will change. The model update module can reconstruct the energy consumption benchmark for that area based on the new normal operation data, ensuring that the model evaluation standards are consistent with the actual operating status.

[0036] like Figure 6As shown, this invention can be deployed and applied in actual heating systems. The heat source, primary pipeline network, heat exchange station, secondary pipeline network, building units, and user-side equipment transmit operational data to the heating energy consumption assessment and early warning model platform. After completing data processing, feature analysis, energy consumption assessment, anomaly identification, early warning output, and model updates, the model platform sends the results to the management platform's large screen, computer, mobile application, or SMS platform. Heating company managers adjust the water supply temperature, heating flow rate, circulating pump frequency, heat exchange station parameters, water replenishment system, and pipeline network operating status based on the early warning results and handling suggestions, thereby achieving energy conservation, consumption reduction, and stable heating.

[0037] The reason why the technical solution of this invention can achieve its purpose is that: heating energy consumption status is jointly affected by meteorological conditions, building characteristics, heating load, equipment operating status, and user heating effect, making it difficult to accurately evaluate using traditional single-indicator or fixed-threshold methods. This invention, through multi-source data fusion, enables the model to obtain heating energy consumption status from multiple dimensions; through energy consumption feature extraction, the model can quantify heating energy consumption and operating status; through dynamic energy consumption benchmark construction, the evaluation criteria can be adjusted according to changes in outdoor temperature, building type, and heating load; through the energy consumption rating module, managers can clearly determine the energy consumption level of different objects; through the anomaly identification and early warning output module, the system can detect high energy consumption trends and potential operational risks in advance; and through the model update module, the model can continuously adapt to changes in different regions and different heating stages. Therefore, this invention can achieve the technical objectives of accurate heating energy consumption rating, timely identification of abnormal energy consumption, and early warning of potential risks.

[0038] In a preferred embodiment, the present invention can perform data collection and assessment on an hourly scale, that is, collect heating operation data, meteorological data, and user room temperature data every hour, and output hourly energy consumption assessment results and early warning information. This method is suitable for the refined operation and management of heat exchange stations, pipeline zones, and key buildings.

[0039] In another preferred embodiment, the present invention can collect and rank data on a daily scale, that is, summarize heating operation data, meteorological data, and energy consumption data daily, and output daily energy consumption ranking results and energy-saving control suggestions. This method is suitable for daily energy consumption statistics of heating companies, regional energy consumption ranking, and operational performance evaluation.

[0040] In another preferred embodiment, the present invention can perform data analysis according to the heating cycle scale. That is, after the end of the entire heating season, the energy consumption levels of different heat sources, heat exchange stations, buildings, and user units are comprehensively evaluated to form a heating season energy consumption evaluation report. This method is suitable for annual energy-saving assessments, equipment renovation decisions, and optimized management of heating areas.

[0041] This invention can also employ various alternative solutions to achieve the same or similar technical objectives. For the data acquisition module, wired or wireless communication can be used; data can be collected through on-site sensors or from existing SCADA systems, smart heating platforms, heat metering platforms, or energy management platforms. For the data preprocessing module, missing value handling can employ mean imputation, interpolation imputation, historical period imputation, or machine learning prediction imputation; outlier identification can employ fixed range judgment, statistical distribution judgment, box plot judgment, or model recognition methods. For the dynamic energy consumption benchmark construction module, statistical quantile methods, regression models, clustering models, time series models, or machine learning models can be used for construction. For the energy consumption rating module, five rating levels can be set, or three, four, or more levels can be set according to management needs. For the anomaly identification module, rule-based judgment, expert experience rules, machine learning classification models, or multi-model fusion methods can be used. For the early warning output module, platform pop-ups, SMS, mobile push notifications, voice reminders, report output, or interface push notifications can be used. For model deployment, it can be deployed on local servers, enterprise private clouds, public cloud platforms, or edge computing devices.

[0042] Although the above alternative solutions differ in their specific implementation, they all achieve the purpose of heating energy consumption assessment and early warning under the technical concept of this invention through multi-source data fusion, dynamic energy consumption benchmark construction, energy consumption classification evaluation, and anomaly early warning output. Therefore, these equivalent alternatives or modified implementations should all be included within the protection scope of this invention.

[0043] The heating energy consumption assessment and early warning model provided by this invention can fuse and analyze multi-source data during the operation of the heating system, establish a dynamic energy consumption evaluation benchmark, and classify and intelligently warn of heating energy consumption levels. Compared with existing heating energy consumption monitoring and alarm methods, this invention has the following advantages: First, improve the accuracy of heating energy consumption assessment; This invention no longer relies solely on single indicators such as heat consumption, electricity consumption, and water consumption per unit area for judgment. Instead, it comprehensively considers heating operation data, meteorological data, building attribute data, user room temperature data, and historical energy consumption data. Through joint analysis of multiple indicators, it can more accurately reflect the true energy consumption status of different heat sources, heat exchange stations, buildings, units, or users, avoiding the problem of biased evaluation caused by fluctuations in a single indicator.

[0044] Second, improve the ability to identify abnormal energy consumption; This invention, by constructing features such as meteorologically corrected heat consumption, historical same-period deviation rate, room temperature compliance rate, supply and return water temperature difference, and operational fluctuation coefficient, can identify various abnormal situations, including abnormally high heat consumption, abnormally high electricity consumption, abnormally high water consumption, abnormal water replenishment, pipeline imbalance, decreased heat exchange efficiency, and room temperature not meeting standards but with high energy consumption. Compared to traditional fixed threshold alarm methods, this invention can more accurately detect abnormal energy consumption and its potential causes.

[0045] Third, reduce energy consumption in the heating system; This invention can promptly identify high-energy-consuming areas, high-energy-consuming heat exchange stations, high-energy-consuming buildings, and abnormal heat-consuming units, providing heating companies with targeted control measures. Managers can adjust water supply temperature, flow rate, pump frequency, heat exchange station parameters, and heating strategies in a timely manner based on assessment results and early warning information, thereby reducing the waste of heat, electricity, and water resources caused by excessive heating, ineffective heating, and inefficient equipment operation.

[0046] Fourth, improve the timeliness and foresight of early warnings; Existing heating management systems typically only issue alarms after energy consumption has significantly exceeded limits, exhibiting a certain degree of lag. This invention, by analyzing energy consumption trends, historical benchmark deviations, and operational fluctuations, can issue early warnings during the formation of abnormal trends, enabling managers to take measures before risks escalate, thereby reducing operational accidents, energy waste, and user complaints.

[0047] Fifth, improve the stability of heating operation; This invention can continuously monitor the operating status of the heating system, identifying and issuing early warnings for problems such as abnormal differences in supply and return water temperatures, pressure fluctuations, abnormal flow rates, abnormal water replenishment, and decreased heat exchange efficiency. By promptly detecting operational deviations, it can help heating companies maintain an operational balance between the heat source, pipeline network, heat exchange stations, and the user side, thereby improving the overall stability and safety of the heating system.

[0048] Sixth, reduce false alarms and missed alarms; This invention uses a dynamic energy consumption benchmark instead of a traditional fixed threshold. This dynamic benchmark can automatically adjust the evaluation criteria based on outdoor temperature, building type, heating area, historical operating patterns, and changes in heating load. Under complex operating conditions such as extreme low temperatures, rising temperatures, and sudden load changes, this invention can more reasonably distinguish between normal and abnormal energy consumption changes, thereby reducing false alarms and missed alarms caused by fixed threshold alarms.

[0049] Seventh, improve the intelligence and precision of heating management; This invention can output energy consumption assessment results according to different levels such as heat source, heat exchange station, building, unit, or user, enabling heating companies to quickly identify key energy-consuming entities. Through graded evaluation and classified early warning, managers can take different management measures for different levels of energy consumption status, realizing the transformation from extensive management to refined and intelligent management.

[0050] Eighth, reduce the costs of manual analysis and management; Traditional heating energy consumption analysis often relies on manual experience, manual reports, and manual inspections, which is labor-intensive, inefficient, and the judgment results are greatly influenced by the experience of the personnel. This invention automatically completes data analysis, energy consumption assessment, anomaly identification, and early warning output through a model, which can reduce the workload of manual statistics and judgment, improve management efficiency, and reduce the operating and management costs of heating companies.

[0051] Ninth, improve system adaptability and scalability; This invention allows for parameter adjustments and model updates based on different cities, heating companies, heat source types, heat exchange station scales, and building user characteristics. As heating operation data accumulates, the model can continuously optimize energy consumption benchmarks and early warning rules, making it suitable for heating energy consumption management across multiple regions, levels, and scenarios.

[0052] Tenth, it facilitates widespread application; This invention primarily analyzes existing operational data, meteorological data, building data, and historical energy consumption data from the heating system. It does not require large-scale changes to the existing heating system structure or complex modifications to existing equipment. The model can be embedded into existing smart heating platforms, energy management platforms, or heating dispatching systems, and features convenient deployment, low application costs, and high promotional value.

[0053] In summary, this invention can improve the accuracy of heating energy consumption evaluation, anomaly identification capability, timely early warning and system operation stability without significantly increasing hardware modification costs, thereby reducing heating energy loss and manual management costs, and providing effective technical support for energy conservation, intelligent control and safe and stable operation of centralized heating systems.

Claims

1. A heating energy consumption assessment and early warning model, characterized in that... include: The data acquisition module is used to acquire multi-source data during the operation of the heating system; The data preprocessing module is used to standardize the collected data; The multi-source data fusion module is used to jointly model heating operation data, meteorological data, building attribute data, and historical energy consumption data; The energy consumption feature extraction module is used to extract key features reflecting the heating energy consumption level and operating status from the fused data. The energy consumption benchmark construction module is used to establish energy consumption benchmark values ​​for different evaluation objects under different operating conditions; The energy consumption assessment module is used to determine the relationship between the current energy consumption characteristics of the assessment object and the dynamic energy consumption benchmark. Anomaly identification module is used to identify abnormal energy consumption types and potential risks during the operation of the heating system; The early warning output module is used to output early warning information based on the energy consumption assessment results and anomaly identification results; The model update module is used to continuously optimize the energy consumption benchmark and rating rules based on new heating operation data.

2. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The multi-source data includes, but is not limited to, heat source operation data, heat exchange station operation data, pipeline operation data, user-side heat consumption data, building attribute data, meteorological data, and historical energy consumption data.

3. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The data preprocessing module first performs outlier detection, missing value imputation, time alignment, and unit unification on the raw data; then, based on the heating operation cycle, it unifies data from different sources and frequencies to the same time scale.

4. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The multi-source data fusion module takes heat sources, heat exchange stations, buildings, units or users as evaluation objects, establishes a unified data index relationship, and associates the heating operation parameters, building parameters, environmental parameters and energy consumption parameters of the same evaluation object in the same time period to form a comprehensive energy consumption analysis sample.

5. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The key features in the energy consumption feature extraction module include heat consumption per unit area, electricity consumption per unit area, water consumption per unit area, supply and return water temperature difference, heat load intensity, meteorological correction heat consumption, historical same period deviation rate, room temperature compliance rate, pipeline distribution efficiency, heat exchange efficiency, water replenishment anomaly rate, and operation fluctuation coefficient.

6. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The energy consumption benchmark construction module constructs a dynamic energy consumption benchmark based on historical normal operation data, building attributes, outdoor temperature, heating area, and user heat demand.

7. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The anomaly identification module classifies and identifies abnormal situations based on energy consumption assessment results, operating parameter change trends, and the correlation between multi-source data.

8. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: The early warning information includes the early warning level, the target of the early warning, the early warning time, the abnormal indicators, the degree of deviation, the possible causes, and the handling suggestions.

9. The heating energy consumption assessment and early warning model as described in claim 1, characterized in that: As operational data accumulates during the heating season, the model update module can periodically update historical benchmark intervals, meteorological correction parameters, building energy consumption characteristics, and anomaly identification rules, enabling the assessment and early warning results to adapt to changes in different heating areas, building types, and operational stages.

10. A method for assessing and issuing early warnings of heating energy consumption using the heating energy consumption assessment and early warning model described in claim 1, comprising the following steps: The first step is to collect heating system operation data, building attribute data, user-side heating data, meteorological data, and historical energy consumption data; The second step is to perform data cleaning, missing value handling, outlier correction, time alignment, unit unification and standardization on the collected multi-source data. The third step is to establish multi-source data correlation relationships using heat sources, heat exchange stations, buildings, units, or users as evaluation objects to form a comprehensive energy consumption analysis sample. The fourth step is to extract energy consumption characteristics such as heat consumption per unit area, electricity consumption per unit area, water consumption per unit area, supply and return water temperature difference, heat load intensity, meteorological correction heat consumption, historical same period deviation rate, room temperature compliance rate, heat exchange efficiency, and operating fluctuation coefficient. The fifth step is to construct a dynamic energy consumption benchmark range based on historical normal operation data, outdoor meteorological conditions, building attributes, and heating load. The sixth step is to compare the current energy consumption characteristics with the dynamic energy consumption benchmark range, and, in conjunction with the room temperature compliance and operational stability, classify and grade the heating energy consumption level. The seventh step is to identify abnormal energy consumption types and potential operational risks based on the assessment results and the changing trends of operating parameters. The eighth step is to output the corresponding level of early warning information based on the degree of abnormality, and to provide the abnormal indicators, the degree of deviation, possible causes and control suggestions; The ninth step is to update and optimize the energy consumption benchmark, rating rules, and early warning rules based on subsequent operational data and feedback from manual handling.

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