Energy valley mode method based on intelligent management and comprehensive utilization of energy

Abstract

The invention relates to the technical field of energy management, and discloses an energy valley mode method based on energy intelligent management and comprehensive utilization, and the method comprises the steps: obtaining real-time building environment information data, building equipment operation information data and building personnel flow data; real-time data are transmitted to a central management system in real time through a wireless network; the central management system transmits real-time building environment information data, building equipment operation information data and building personnel flow data to a visual interface of the mobile terminal through a wireless network, and a user of the mobile terminal carries out real-time regulation and control; information of real-time regulation and control by the mobile terminal user is fed back to the central management system; and the central management system performs data preprocessing and data analysis on the real-time building environment information data, the building equipment operation information data and the building personnel flow data to obtain a building energy supply scheme and a building operation scheme. The problem that dynamic adjustment of load distribution between energy systems is difficult to realize can be solved.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to an energy valley model method based on intelligent energy management and comprehensive utilization. Background Art

[0002] As global energy shortages and environmental pollution become increasingly serious, energy conservation and emission reduction have become important goals for social development. As one of the main areas of energy consumption, energy consumption management in the construction industry is particularly critical. In modern buildings, the energy supply system includes various energy forms such as heating, cooling, power supply, and water supply. The management of various energy sources and their efficient use directly affect the operating cost and comfort of the building. However, traditional building energy management models often rely on independently operated energy systems, lack of coordination and integration between systems, resulting in low energy utilization efficiency, and may even lead to resource waste and operational failures.

[0003] At present, more and more large public buildings, shopping malls, hospitals and other places have begun to adopt integrated energy management systems to coordinate and manage the use of various types of energy in the building. The integrated energy management system collects, analyzes and dispatches various forms of energy to achieve efficient use of the overall energy of the building. However, the existing integrated energy management system still has many problems in practical applications, such as the difficulty in dynamically adjusting the load distribution between the energy systems of complex data during operation, which leads to the inflexibility and inaccuracy of the energy supply plan, and the energy waste during the operation of the building is still serious.

[0004] Therefore, how to design a system that can efficiently process building energy supply information and building operation information, and adjust the energy supply plan and building operation plan in real time to improve energy utilization efficiency and optimize operating costs has become a technical problem that needs to be urgently solved in the current field of integrated energy management. Summary of the invention

[0005] In view of this, the present invention proposes an energy valley model method based on intelligent energy management and comprehensive utilization, aiming to solve the problem that load distribution between energy systems in current technology is difficult to achieve dynamic adjustment.

[0006] The present invention proposes an energy valley model method based on intelligent energy management and comprehensive utilization, including: S1: Obtain real-time building environment information data, building equipment operation information data, and building personnel flow data; S2: Transmit real-time building environment information data, building equipment operation information data and building personnel flow data to the central management system via wireless network in real time; S3: The central management system transmits real-time building environment information data, building equipment operation information data, and building personnel flow data to the mobile terminal visualization interface through the wireless network, and the mobile terminal users can make real-time adjustments; S4: The information of real-time control by mobile users is fed back to the central management system; S5: The central management system performs data preprocessing and data analysis on real-time building environment information data, building equipment operation information data, and building personnel flow data to obtain building energy supply plans and building operation plans.

[0007] Furthermore, the building environment information data in S1 includes temperature data, humidity data, CO2 concentration data and light data.

[0008] Furthermore, the building equipment operation information data in S1 includes air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data, and power consumption data of each system; Data on building personnel flow includes personnel density data.

[0009] Furthermore, the specific steps of S5 are: S501: The central management system cleans and removes noise from the building environment information data, the building equipment operation information data, and the building personnel flow data to obtain processed data; S502: The central management system uses a time series analysis algorithm to identify the transformation of data in real time for processing data and real-time control of mobile users; S503: The central management system uses big data analysis on the processed data, compares and analyzes the processed data with historical data to identify real-time energy consumption patterns and environmental change trends; S504: Obtain a building energy supply plan and a building operation plan through data transformation, real-time energy consumption patterns, and environmental change trends.

[0010] Furthermore, the specific content of S3 is: the central management system transmits temperature data, humidity data, C02 concentration data, light data, air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data, power consumption data of each system and personnel density data to the mobile terminal visualization interface through the wireless network, and the mobile terminal users can adjust the building equipment operation information data in real time through the visualization interface.

[0011] Furthermore, the specific steps of S301 include: S3011: data format normalization, using a programming language to convert different data types in the building environment information data, the building equipment operation information data, and the building personnel flow data into a unified format and a unified unit to obtain first data; S3012: Missing value processing: using the spline interpolation method to fill the missing data in the first data to maintain the integrity of the data set, and obtain the second data; S3013: outlier detection and processing, using a machine learning method to identify and process outliers on the second data to obtain third data; S3014: Noise identification, using Fourier transform method to identify noise on the third data; S3015: Remove noise: Use an efficient algorithm to remove noise from the third data.

[0012] Furthermore, the specific steps of S303 include: 3031: Data mining, using cluster analysis and association rule analysis on historical data; Cluster analysis: cluster data points, identify different energy consumption patterns in historical data, and understand energy consumption characteristics under different time periods and conditions; Association rule analysis: Analyze the relationship between different variables in historical data, that is, the reasons for increased energy consumption under specific conditions; S3032: Statistical analysis, descriptive statistics and trend analysis of historical data; Descriptive statistics: Calculate the mean, variance, maximum and minimum values ​​of energy consumption to understand the overall energy consumption level; Trend analysis: Use visualization tools to display time series data and identify long-term trends in energy consumption and environmental variables; S3033: Establish energy consumption pattern benchmarks, and establish energy consumption benchmarks by analyzing historical data; S3034: Relationship between building environmental information data and energy consumption: Analyze the impact of changes in building environmental information data on energy consumption and quantify the relationship with the help of regression analysis models; Interaction between building equipment operation information data and building environment information data: Study how the operation status of different building equipment operation information data affects the building environment, making the energy consumption pattern clearer; Use LSTM neural network on historical data to predict future energy consumption trends; S3035: Real-time comparative analysis, comparing the processed data with the historical data, identifying the difference between the real-time energy consumption and the energy consumption benchmark, and obtaining the real-time energy consumption pattern and environmental change trend.

[0013] Furthermore, the specific steps of S304 are: S3041: Evaluate building energy demand through real-time energy consumption patterns and trend analysis to identify peak load demand periods; Comprehensively consider the building's energy efficiency level, analyze the use of renewable energy and traditional power sources in the building, and determine the optimal energy supply combination to obtain a comprehensive energy efficiency evaluation; S3042: Based on comprehensive energy efficiency assessment, use machine learning algorithms to achieve efficient management of different energy sources inside the building, and automatically adjust temperature, humidity, C02 concentration, and light intensity according to real-time needs; Building equipment operation information data includes air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data and power consumption data of each system; S3043: Track the operating status of building equipment in real time, automatically adjust the operating status of air conditioners, humidifiers, ventilation systems, lighting systems, and power usage to ensure that the system operates efficiently within the predetermined range; analyze the activity patterns of personnel in the building and automatically adjust the operating strategies of the building's equipment.

[0014] Furthermore, the efficient algorithm in S3015 is the wavelet transform method.

[0015] Furthermore, energy consumption benchmarks include seasonal benchmarks and event-driven benchmarks; Energy consumption benchmark: Identify differences in energy consumption in different seasons and weather conditions; Event-driven benchmarks: The impact of major events on energy consumption.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can realize real-time monitoring and regulation: by acquiring and transmitting data on building environment, equipment operation and personnel flow, real-time monitoring is realized, and users can make instant adjustments on the mobile terminal, thereby improving management efficiency.

[0017] (2) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can realize data-driven decision support: the central management system can formulate more scientific building energy supply and operation plans through data preprocessing and analysis, and help optimize energy use.

[0018] (3) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can improve energy efficiency and reduce energy consumption: by analyzing energy consumption patterns and environmental change trends through intelligent algorithms (such as time series analysis, machine learning, etc.), it can effectively identify peak loads and optimize energy combinations, thereby reducing overall energy consumption.

[0019] (4) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can achieve enhanced comfort and healthy environment: real-time adjustment of temperature, humidity, CO2 concentration and light intensity, improve indoor environmental quality, and enhance the comfort and health of residents.

[0020] (5) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can realize intelligent equipment management: the system can automatically adjust the operating status of building equipment to ensure efficient operation of the equipment and reduce unnecessary energy consumption.

[0021] (6) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can support the utilization of renewable energy: through comprehensive energy efficiency evaluation, it can promote the efficient use of renewable energy and traditional energy and promote the development of green buildings.

[0022] (7) The energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can achieve data integrity and reliability: it adopts a variety of data processing technologies (such as outlier detection, noise removal, etc.) to ensure the integrity and reliability of the data and provide an accurate basis for subsequent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 This is a flow chart of an energy valley model method based on intelligent energy management and comprehensive utilization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] The Energy Valley model is a management model based on energy scheduling and optimization. Its main purpose is to optimize energy utilization efficiency by adjusting energy supply and demand, reducing energy consumption during peak periods, and increasing energy use during valley periods. This model involves the rational deployment and storage technology of different energy forms, and is usually applied to various energy systems such as electricity and natural gas.

[0026] See also Figure 1 As shown, an embodiment of the present invention provides an energy valley model method based on energy intelligent management and comprehensive utilization, including: S1: Obtain real-time building environment information data, building equipment operation information data, and building personnel flow data; S2: Transmit real-time building environment information data, building equipment operation information data and building personnel flow data to the central management system via wireless network in real time; Specifically, the collected real-time building environment information data, building equipment operation information data and building personnel flow data are transmitted to the central management system in real time via wireless networks (Wi-Fi, Zigbee or LoRa). The transmission delay is controlled within 1 second, and encryption technology is used to ensure the security of data transmission.

[0027] Data packets are sent regularly and a standardized data packet format is designed, including sensor ID, data type, timestamp, and data value, so that the central management system can quickly parse and process them. The data packets are sent every 5 seconds to ensure that the central system obtains the latest data.

[0028] S3: The central management system transmits real-time building environment information data, building equipment operation information data, and building personnel flow data to the mobile terminal visualization interface through the wireless network, and the mobile terminal users can make real-time adjustments; S4: The information of real-time control by mobile users is fed back to the central management system; S5: The central management system performs data preprocessing and data analysis on real-time building environment information data, building equipment operation information data, and building personnel flow data to obtain building energy supply plans and building operation plans.

[0029] Furthermore, the building environment information data in S1 includes temperature data, humidity data, C02 concentration data and light data; Building equipment operation information data includes air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data and power consumption data of each system; Data on building personnel flow includes personnel density data.

[0030] Specifically, sensors and smart devices are used to collect environmental information data in the building in real time; Environmental information data collection: Temperature sensor: Place temperature sensors in various areas of the building (such as offices, conference rooms, corridors, etc.) to monitor temperature changes on each floor and in each room in real time. Set the data collection frequency to every 5 seconds and select sensors with high precision (±0.5°C) to ensure data accuracy.

[0031] Humidity sensor: Install humidity sensors to ensure real-time monitoring of humidity levels, especially in humid areas. Use relative humidity sensors that can monitor humidity changes in real time, with a range of 0-100%RH and an accuracy of ±3%RH.

[0032] CO2 sensor: Deploy infrared CO2 concentration sensors to monitor air quality, especially in meeting rooms and crowded areas, ensuring a monitoring range of 0-5000ppm with an accuracy of ±50ppm.

[0033] Light Sensors: Use light sensors to monitor natural light levels to optimize indoor lighting.

[0034] Building equipment operation data collection: Air conditioning system: The intelligent controller monitors the air conditioning's on / off status, set temperature, operating mode (cooling / heating) and energy consumption.

[0035] Humidifier and ventilation system: record the working status of the humidifier and the operating frequency and energy consumption of the ventilation system to ensure indoor air quality.

[0036] Lighting system: monitor the working status, real-time power consumption and usage frequency of the lighting system in each area, and prepare for subsequent analysis.

[0037] Personnel flow data collection: Crowd density monitoring: Use infrared sensors or crowd counters at entrances, exits and public areas to count the flow of people and distribution of people, and generate dynamic density maps.

[0038] Through the above detailed environmental information data collection scheme, comprehensive monitoring and intelligent management of the internal environment of the building can be achieved. Real-time temperature, humidity, CO2 concentration and light data will provide data support for subsequent energy efficiency optimization and user comfort improvement. The integration of equipment operation status and personnel flow data further enhances the intelligence level of the system and realizes efficient, energy-saving and sustainable building management.

[0039] Furthermore, the specific steps of S5 are: S501: The central management system cleans and removes noise from the building environment information data, the building equipment operation information data, and the building personnel flow data to obtain processed data; S502: The central management system uses a time series analysis algorithm to identify the transformation of data in real time for processing data and real-time control of mobile users; S503: The central management system uses big data analysis on the processed data, compares and analyzes the processed data with historical data to identify real-time energy consumption patterns and environmental change trends; S504: Obtain a building energy supply plan and a building operation plan through data transformation, real-time energy consumption patterns, and environmental change trends.

[0040] The data after the above cleaning and noise removal has higher accuracy and reliability, providing a solid foundation for subsequent analysis; the central management system can identify data changes in real time and adjust strategies in time to cope with dynamically changing environments and usage needs. The application of big data analysis methods enables the system to deeply explore the relationship between energy consumption patterns and environmental variables, providing a more scientific basis for decision-making; real-time analysis results support the dynamic formulation of building energy supply plans, reduce peak load impacts, and improve energy efficiency; through real-time monitoring and intelligent adjustment, the operating status of building equipment is optimized, energy efficiency is improved, operating costs are reduced, and user comfort experience is enhanced.

[0041] Furthermore, the specific content of S3 is: the central management system transmits temperature data, humidity data, C02 concentration data, light data, air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data, power consumption data of each system and personnel density data to the mobile terminal visualization interface through the wireless network, and the mobile terminal users can adjust the building equipment operation information data in real time through the visualization interface.

[0042] Specifically, a user-friendly mobile application was developed, including a real-time monitoring panel that displays information such as temperature, humidity, CO2 concentration, light, equipment status, and personnel density.

[0043] Provides a graphical historical data analysis view, allowing users to view energy consumption trends over a period of time.

[0044] Users can directly adjust device settings through simple sliders or switch icons, such as adjusting air conditioning temperature, turning on and off lighting, setting humidifier working status, etc., and feedback the adjustment instructions to the central management system.

[0045] After the central management system confirms the user's instructions, it immediately updates the device status and returns the change results to the user.

[0046] Through the above implementation details, the mobile application will provide users with an intuitive and comprehensive building equipment monitoring and control experience. Users can obtain real-time environmental and equipment status information and easily adjust equipment settings, thereby effectively improving the building's energy management and utilization efficiency, and enhancing the comfort and satisfaction of residents. The system's feedback mechanism will also ensure the real-time and accuracy of user operations, further promoting the realization of intelligent management.

[0047] Furthermore, the specific steps of S301 include: S3011: Data format standardization, using programming language to convert different data types in building environment information data, building equipment operation information data and building personnel flow data into a unified format and unified unit to obtain the first data, ensure the compatibility between different data sources, reduce errors in data processing, and provide a clear data structure for subsequent analysis and processing steps, making data operations more intuitive and efficient; Specifically, the unified format includes normalizing data types (such as temperature in degrees Celsius and humidity in percentage); unifying timestamps into UTC format to facilitate time series analysis; and setting unified units (such as electricity consumption in kilowatt-hours kWh).

[0048] S3012: Missing value processing: Use spline interpolation to fill missing data in the first data to maintain the integrity of the data set, and obtain the second data. Use spline interpolation to fill missing values ​​to avoid the impact of null values ​​in the data set on the analysis results, ensure that the data used in the analysis is continuous, and use interpolation methods to maintain data trends and characteristics, reduce deviations caused by missing data, and improve the credibility of the results.

[0049] S3013: Outlier detection and processing: Use machine learning methods (such as Isolation Forest) to identify and process outliers on the second data to obtain third data. Identifying and processing outliers through the Isolation Forest algorithm can significantly improve the accuracy of data, reduce the interference of extreme values ​​on analysis results, ensure that subsequent analysis results reflect the actual situation, and improve the system's prediction ability and the reliability of decision support; S3014: Noise identification: Fourier transform method is used to identify noise in the third data. By identifying noise through Fourier transform, the useful signal and noise components in the data can be effectively distinguished, providing a basis for subsequent denoising. After identifying the noise, it is possible to clarify which parts of the data need to be paid attention to, thereby improving the pertinence of data analysis; S3015: Remove noise. Use efficient algorithms (such as wavelet transform) to remove noise from the third data. By removing noise through wavelet transform, effective signals are retained, and the signal-to-noise ratio of the data is improved, making subsequent analysis more accurate. The denoised data is smoother, which helps to build a more stable and accurate prediction model and enhance the system's intelligent analysis capabilities.

[0050] It should be noted that after a series of preprocessing, the final data set has high completeness, high accuracy and high reliability, which greatly improves the quality of data analysis.

[0051] The cleaned data provides a solid foundation for subsequent data analysis, model building and decision support, and helps to achieve efficient operation of the building intelligent management system.

[0052] The system can respond quickly to environmental changes and adjust the operating status of equipment in real time, thereby optimizing energy management within the building, improving energy efficiency and reducing operating costs.

[0053] By ensuring the accuracy and reliability of data, users can obtain more accurate information feedback and control experience when using mobile applications, thereby improving user satisfaction.

[0054] Furthermore, the specific steps of S303 include: 3031: Data mining, using cluster analysis and association rule analysis on historical data; Cluster analysis: Use clustering algorithms such as K-Means or DBSCAN to group data points, identify different energy consumption patterns in historical data, and understand energy consumption characteristics under different time periods and conditions. For example, it can be identified that the energy consumption of air conditioning and lighting increases significantly in a specific season or time period. By understanding the energy consumption characteristics of different time periods, managers can formulate strategies more effectively to avoid excessive energy consumption during peak periods, thereby improving overall energy efficiency. Association rule analysis: Apriori algorithm is used to analyze the relationship between different variables in historical data, that is, the reasons for the increase in energy consumption under specific conditions. For example, in hot weather, the energy consumption of air conditioners increases significantly. This provides an important basis for subsequent energy consumption prediction and management strategies. After identifying the relationship between different variables, the equipment operation mode can be adjusted in a targeted manner to optimize energy configuration. S3032: Statistical analysis, descriptive statistics and trend analysis of historical data; Descriptive statistics: Calculate the mean, variance, maximum and minimum values ​​of historical energy consumption data to understand the overall energy consumption level and identify long-term change patterns; By calculating statistics such as mean and variance, we can have a clear understanding of the overall energy consumption level of the building, help find abnormal or excessive energy consumption, and through descriptive statistics, we can identify the fluctuation range of energy consumption and ensure that it can be kept within a reasonable range in future energy management. Trend analysis: Use visualization tools to display time series data and identify long-term trends in energy consumption and environmental variables; Using visualization tools to display time series data can clearly identify long-term trends between energy consumption and environmental variables, such as the rising trend in energy consumption caused by high temperatures in summer. Identifying long-term change patterns can help managers make more accurate predictions and decisions when planning future energy needs.

[0055] S3033: Establish energy consumption pattern benchmarks, and establish energy consumption benchmarks by analyzing historical data; Energy consumption benchmarks include seasonal benchmarks and event-driven benchmarks; Energy consumption benchmark: Identify differences in energy consumption in different seasons and weather conditions. By analyzing energy consumption patterns in different seasons, a reliable seasonal benchmark can be established to help managers predict energy consumption needs in different seasons. Event-driven benchmarking: Identify the impact of major events (such as holidays, meetings, etc.) on energy consumption, analyze changes in energy consumption during these special events, identify the impact of major events on energy consumption, and provide references based on historical data to support energy consumption management of building activities; S3034: Relationship between building environmental information data and energy consumption: Analyze the impact of changes in building environmental information data on energy consumption and quantify the relationship with the help of regression analysis models; Through regression analysis, the impact of building environment information (such as temperature and humidity) on energy consumption is quantified, providing a scientific basis for building design and energy management. After identifying the key influencing factors, energy consumption can be effectively controlled by adjusting the building environment (such as optimizing air conditioning settings); Interaction between building equipment operation information data and building environment information data: Study how the operation status of different building equipment operation information data affects the building environment, making the energy consumption pattern clearer; Use LSTM neural network on historical data to predict future energy consumption trends; By using the LSTM neural network, future energy consumption trends can be accurately predicted, improving the foresight of energy management. The prediction results can be fed back to the central management system in real time, supporting dynamic adjustment of energy supply plans and reducing energy waste.

[0056] S3035: Real-time comparative analysis, comparing the processed data with the historical data, identifying the difference between the real-time energy consumption and the energy consumption benchmark, and obtaining the real-time energy consumption pattern and environmental change trend; By comparing real-time data with historical data, abnormal energy consumption can be identified in a timely manner, such as an abnormal increase in energy consumption of a certain device, and an alarm can be issued in time to ensure that measures are taken quickly; real-time monitoring and analysis can help managers make quick decisions, thereby improving overall energy utilization efficiency and reducing unnecessary energy consumption; by comparing real-time energy consumption with energy consumption benchmarks, the building's operating strategy can be dynamically optimized (such as adjusting air-conditioning temperature, lighting intensity, etc.), and flexibly adjusted according to real-time demand changes; real-time comparative analysis enables the building management system to respond quickly to environmental changes, enhancing the building's adaptability to different environmental conditions.

[0057] It should be noted that through clustering and association rule analysis, typical patterns of building energy consumption are identified to provide a basis for optimizing energy management; through statistical analysis and benchmark establishment, detailed data support is provided to help managers make more scientific decisions during operations; the use of LSTM models for energy consumption trend forecasts improves the forward-looking nature of the system and enables it to better respond to future changes in energy consumption demand; real-time comparative analysis enables managers to quickly identify problems, adjust strategies in a timely manner, and optimize energy efficiency within the building.

[0058] Furthermore, the specific steps of S304 are: S3041: Evaluate the energy demand of buildings through real-time energy consumption patterns and trend analysis, and identify peak load demand periods. Through time series analysis technology, the peak load periods of buildings can be accurately identified. For example, during the hot summer period, air conditioning energy consumption will increase sharply. The central management system can adjust the energy supply strategy in advance based on peak load information to reduce the impact of energy demand during peak periods and optimize overall energy use. Comprehensively consider the energy efficiency level of the building, analyze the use of renewable energy and traditional power sources in the building, determine the optimal energy supply combination to obtain a comprehensive energy efficiency assessment. Through the assessment of the building's energy efficiency level, it is possible to identify potential areas for energy-saving improvements and implement corresponding measures to improve the overall energy efficiency of the building. Analyzing the use of renewable energy and traditional energy will help increase the proportion of renewable energy, reduce dependence on fossil fuels, and reduce carbon emissions; S3042: Based on comprehensive energy efficiency assessment, use machine learning algorithms to achieve efficient management of different energy sources inside the building, and automatically adjust temperature, humidity, C02 concentration, and light intensity according to real-time needs; Building equipment operation information data includes air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data and power consumption data of each system; Based on real-time monitoring data, the central management system can dynamically adjust air conditioners, humidifiers and lighting equipment to ensure that energy consumption is within a reasonable range while meeting the comfort needs of users; intelligent management can effectively reduce the ineffective operation time of equipment, improve overall energy efficiency, and reduce electricity bills; by automatically adjusting indoor temperature and humidity, it ensures comfort in the building and enhances user satisfaction and experience; S3043: Track the operating status of building equipment in real time, automatically adjust the operating status of air conditioners, humidifiers, ventilation systems, lighting systems, and power usage to ensure that the system operates efficiently within the predetermined range; analyze the activity patterns of personnel in the building and automatically adjust the operating strategies of the building's equipment.

[0059] In the existing smart building, there are multiple office areas, conference rooms and lounges, equipped with intelligent air conditioners, humidifiers, ventilation systems and LED lighting. A temperature sensor is installed in conference room A using a high-precision sensor (such as DS18B20) to monitor temperature in real time, a relative humidity sensor (such as DHT22) to monitor humidity and CO2 concentration in real time, and an infrared CO2 sensor (such as MH-Z19) to monitor CO2 concentration in real time and send data to the central management system every 5 seconds. The high-precision sensor has the characteristics of rapid response and can accurately capture small changes, so as to make timely adjustments when necessary. The sensor in conference room A shows that the current temperature is 26℃, the humidity is 45%, and the CO2 concentration is 900 ppm. Through the central management system, the temperature data, humidity data, CO2 concentration data and light intensity data are monitored in real time. The central management system compares the real-time data with the historical data to identify any abnormal changes. By comparing the benchmark values, the system can accurately determine whether the current environment is within the comfortable range. When the temperature of conference room A exceeds the set comfort threshold (for example, 25°C), the central management system automatically adjusts the air-conditioning operation status, that is, lowers the air-conditioning temperature setting from 24°C to 22°C, while taking into account external climate factors and the number of people in the conference room to ensure the rationality and efficiency of the adjustment. At the same time, if the humidity is too low (below 40%), the central management system automatically adjusts the humidifier operation status, automatically starts the humidifier, increases water vapor, increases indoor humidity, creates a suitable environment, ensures that the humidity is within the optimal range, and achieves a healthy breathing environment. The ventilation system of conference room A will automatically start when the CO2 concentration exceeds 800 ppm, automatically adjust the operating status of the ventilation system and increase its operating rate to ensure fresh indoor air. An infrared sensor is installed at the entrance of Conference Room A to count the number of people entering and leaving in real time. According to this activity pattern, the central management system will start the air conditioning and lighting equipment in advance to ensure that the environment is suitable when the meeting starts. If the system detects a decrease in the number of people (for example, only two people stay in the room after the meeting), the central management system will automatically reduce the air conditioning power to 30% and dim the lighting to 50% brightness to save energy. The central management system continuously updates and adjusts the operating status of the equipment based on the real-time collected data and personnel flow patterns to achieve optimal control of the environment. Through real-time monitoring and dynamic adjustment, the overall energy consumption of the smart building is expected to be reduced by 15%, saving a lot of electricity bills every month. Through the intelligent management of Conference Room A, not only the comfort and safety of the indoor environment are ensured, but also efficient energy use and significant energy consumption reduction are achieved, reflecting the great potential of the Energy Valley model in sustainable development and environmental friendliness. This modern management model will provide a better experience for building users, save operating costs for enterprises, and promote the development goal of energy saving.

[0060] The central management system monitors the operating status of equipment in real time through sensors, and can instantly obtain the working status of the equipment to ensure the accuracy of energy consumption data; the central management system can promptly identify equipment abnormalities (such as high energy consumption, failures, etc.), provide early warnings, facilitate maintenance and repairs, and reduce equipment failure rates; by automatically adjusting the operating status of the equipment, it ensures that the building's energy use remains efficient under different environmental conditions; the system can analyze the user's activity patterns in the building and intelligently adjust the equipment's operating strategies (for example, the conference room automatically increases ventilation during peak meeting times), thereby further optimizing energy consumption.

[0061] It should be noted that after the above series of analyses, the central management system can evaluate the energy supply demand of the building in real time, ensure the rational allocation of energy during peak hours, and reduce the pressure of peak load on the energy system; by analyzing the use of renewable energy and traditional power sources, it can achieve the optimal configuration of energy, reduce operating costs, and improve the overall energy efficiency of the building; the management system based on machine learning can automatically adjust equipment settings, reduce manual intervention, and improve management efficiency and response speed; through real-time monitoring and adjustment, it can effectively improve the quality of the indoor environment, enhance user comfort, and reduce energy waste caused by environmental discomfort; the system's real-time tracking and intelligent adjustment capabilities ensure that under different environmental conditions and usage modes, the building's energy use can always be optimized to promote the realization of sustainable development goals. While optimizing the use of resources, it also provides a feasible solution for the future urbanization process.

[0062] In summary, the energy valley model method based on intelligent energy management and comprehensive utilization of the present invention can realize real-time monitoring and regulation: by acquiring and transmitting data on building environment, equipment operation and personnel flow, real-time monitoring is realized, and users can make instant adjustments on the mobile terminal, thereby improving management efficiency; data-driven decision support: the central management system can formulate more scientific building energy supply and operation plans through data preprocessing and analysis, and help optimize energy use; improve energy efficiency and reduce energy consumption: through intelligent algorithms (such as time series analysis, machine learning, etc.), analyze energy consumption patterns and environmental change trends, and effectively identify peak loads and optimize energy combination, thereby reducing overall energy consumption; enhancing comfort and healthy environment: real-time adjustment of temperature, humidity, CO2 concentration and light intensity, improving indoor environmental quality, enhancing occupants' comfort and health protection; intelligent equipment management: the system can automatically adjust the operating status of building equipment to ensure efficient operation of equipment and reduce unnecessary energy consumption; supporting the use of renewable energy: through comprehensive energy efficiency evaluation, promoting the efficient use of renewable energy and traditional energy, and promoting the development of green buildings; data integrity and reliability: using a variety of data processing technologies (such as outlier detection, noise removal, etc.) to ensure data integrity and reliability, and provide an accurate basis for subsequent analysis.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An energy valley model method based on intelligent energy management and comprehensive utilization, characterized in that: include: S1: Obtain real-time building environment information data, building equipment operation information data, and building personnel flow data; S2: Transmit real-time building environment information data, building equipment operation information data and building personnel flow data to the central management system via wireless network in real time; S3: The central management system transmits real-time building environment information data, building equipment operation information data, and building personnel flow data to the mobile terminal visualization interface through the wireless network, and the mobile terminal users can make real-time adjustments; S4: The information of real-time control by mobile users is fed back to the central management system; S5: The central management system performs data preprocessing and data analysis on real-time building environment information data, building equipment operation information data, and building personnel flow data to obtain building energy supply plans and building operation plans.

2. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 1 is characterized in that: The building environment information data in S1 includes temperature data, humidity data, C02 concentration data and light data.

3. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 2 is characterized in that: The building equipment operation information data in S1 includes air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data and power consumption data of each system; Data on building personnel flow includes personnel density data.

4. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 3 is characterized in that: The specific steps of S5 are: S501: The central management system cleans and removes noise from the building environment information data, the building equipment operation information data, and the building personnel flow data to obtain processed data; S502: The central management system uses a time series analysis algorithm to identify the transformation of data in real time for processing data and real-time control of mobile users; S503: The central management system uses big data analysis on the processed data, compares and analyzes the processed data with historical data to identify real-time energy consumption patterns and environmental change trends; S504: Obtain a building energy supply plan and a building operation plan through data transformation, real-time energy consumption patterns, and environmental change trends.

5. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 4 is characterized in that: The specific content of S3 is: the central management system transmits temperature data, humidity data, C02 concentration data, lighting data, air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data, power consumption data of each system and personnel density data to the mobile terminal visualization interface through the wireless network. Mobile terminal users can adjust the building equipment operation information data in real time through the visualization interface.

6. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 5 is characterized in that: S301 specific steps include: S3011: data format normalization, using a programming language to convert different data types in the building environment information data, the building equipment operation information data, and the building personnel flow data into a unified format and a unified unit to obtain first data; S3012: Missing value processing: using the spline interpolation method to fill the missing data in the first data to maintain the integrity of the data set, and obtain the second data; S3013: outlier detection and processing, using a machine learning method to identify and process outliers on the second data to obtain third data; S3014: Noise identification, using Fourier transform method to identify noise on the third data; S3015: Remove noise: Use an efficient algorithm to remove noise from the third data.

7. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 6 is characterized in that: S303 specifically includes the following steps: 3031: Data mining, using cluster analysis and association rule analysis on historical data; Cluster analysis: cluster data points, identify different energy consumption patterns in historical data, and understand energy consumption characteristics under different time periods and conditions; Association rule analysis: Analyze the relationship between different variables in historical data, that is, the reasons for increased energy consumption under specific conditions; S3032: Statistical analysis, descriptive statistics and trend analysis of historical data; Descriptive statistics: Calculate the mean, variance, maximum and minimum values ​​of energy consumption to understand the overall energy consumption level; Trend analysis: Use visualization tools to display time series data and identify long-term trends in energy consumption and environmental variables; S3033: Establish energy consumption pattern benchmarks, and establish energy consumption benchmarks by analyzing historical data; S3034: Relationship between building environmental information data and energy consumption: Analyze the impact of changes in building environmental information data on energy consumption and quantify the relationship with the help of regression analysis models; Interaction between building equipment operation information data and building environment information data: Study how the operation status of different building equipment operation information data affects the building environment, making the energy consumption pattern clearer; Use LSTM neural network on historical data to predict future energy consumption trends; S3035: Real-time comparative analysis, comparing the processed data with the historical data, identifying the difference between the real-time energy consumption and the energy consumption benchmark, and obtaining the real-time energy consumption pattern and environmental change trend.

8. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 7 is characterized in that: The specific steps of S304 are: S3041: Evaluate building energy demand through real-time energy consumption patterns and trend analysis to identify peak load demand periods; Comprehensively consider the building's energy efficiency level, analyze the use of renewable energy and traditional power sources in the building, and determine the optimal energy supply combination to obtain a comprehensive energy efficiency evaluation; S3042: Based on comprehensive energy efficiency assessment, use machine learning algorithms to achieve efficient management of different energy sources inside the building, and automatically adjust temperature, humidity, C02 concentration, and light intensity according to real-time needs; Building equipment operation information data includes air conditioning operation data, humidifier operation data, ventilation system operation data, lighting system operation data and power consumption data of each system; S3043: Track the operating status of building equipment in real time, automatically adjust the operating status of air conditioners, humidifiers, ventilation systems, lighting systems, and power usage to ensure that the system operates efficiently within the predetermined range; analyze the activity patterns of personnel in the building and automatically adjust the operating strategies of the building's equipment.

9. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 8 is characterized in that: The efficient algorithm in S3015 is the wavelet transform method.

10. The energy valley model method based on energy intelligent management and comprehensive utilization according to claim 9 is characterized in that: Energy consumption benchmarks include seasonal benchmarks and event-driven benchmarks; Energy consumption benchmark: Identify differences in energy consumption in different seasons and weather conditions; Event-driven benchmarks: The impact of major events on energy consumption.

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