Water, electricity, gas, heating and cooling energy consumption monitoring and collaborative energy saving method

Abstract

The invention relates to the technical field of energy consumption monitoring, in particular to a water, electricity, gas, heating and cooling energy consumption monitoring and collaborative energy saving method which comprises the following steps that energy consumption data of all energy systems are collected in real time through multiple sensor nodes installed in a building, and the energy systems comprise a water supply system, a power supply system, a gas supply system, a heating system and a cooling system; the energy consumption data comprises consumption data, fluctuation amplitude and an energy consumption time period; transmitting the energy consumption data to a central processing unit for preprocessing to form a complete energy consumption data set; comprehensively analyzing the mutual relationship among the energy systems, and generating an energy consumption interaction influence model among the energy systems; and the energy consumption peak period and the redundant use condition are identified, a collaborative energy-saving strategy is automatically generated, and scheduling among the energy systems is optimized. According to the invention, the overall energy efficiency of each energy system can be greatly improved, the load among the systems can be effectively balanced, and reasonable utilization of energy and long-term stable operation of the system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption monitoring, and in particular to a method for monitoring water, electricity, gas, heating and cooling energy consumption and for collaborative energy saving. Background Art

[0002] In modern buildings and industrial environments, multiple energy systems such as water, electricity, gas, heating and cooling jointly support the normal operation of buildings. With the increase in energy demand, how to achieve coordinated management of multiple energy systems and efficient energy saving has become an important research direction in the field of energy-saving technology. However, traditional energy management systems usually only focus on the optimization of a single energy system and lack comprehensive analysis and scheduling control of the complex relationship between various energy systems. This isolated management method often makes it difficult to form synergy between different energy systems, and even leads to problems such as low energy efficiency and waste of resources during peak or non-peak load periods.

[0003] Most existing energy management methods use energy-saving solutions based on fixed parameters or simple time series, ignoring the mutual influence and dynamic interaction between different energy systems. This method is not only difficult to adapt to complex building energy consumption needs, but also unable to cope with changes in energy loads in different time periods. For example, the heating system and the cooling system may have load conflicts in certain periods, resulting in a decrease in overall energy efficiency; at the same time, the redundant use of water supply and gas supply systems is also difficult to be effectively identified, resulting in energy waste.

[0004] In order to achieve efficient energy management and energy-saving effects, there is an urgent need for a collaborative management method that can monitor and analyze multiple energy systems such as water, electricity, gas, heating and cooling in real time. Summary of the invention

[0005] The invention provides a method for monitoring water, electricity, gas, heating and cooling energy consumption and for coordinated energy saving.

[0006] A method for monitoring water, electricity, gas, heating and cooling energy consumption and coordinated energy saving, comprising the following steps:

[0007] S1: Through the multi-sensor nodes installed in the building, the energy consumption data of each energy system is collected in real time. The energy system includes water supply, power supply, gas supply, heating and cooling systems. The energy consumption data includes usage data, fluctuation range and energy consumption time period; the energy consumption data is transmitted to the central processing unit for pre-processing to form a complete energy consumption data set;

[0008] S2: Based on the acquired energy consumption data set and combined with historical energy consumption data, the relationship between various energy systems is comprehensively analyzed to generate an energy consumption interaction model between various energy systems;

[0009] S3: Based on the established energy consumption interaction model, calculate the energy efficiency of the water supply system, power supply system, gas supply system, heating system, and cooling system in different time periods, identify peak energy consumption periods and redundant usage, automatically generate collaborative energy-saving strategies, and optimize the scheduling between various energy systems.

[0010] Optionally, the multi-sensor node includes installing multiple sensors in various energy systems in the building, specifically including:

[0011] Water flow sensor: used to collect water consumption data of the water supply system and record water flow rate and usage in real time;

[0012] Current and voltage sensors: used to collect power consumption data of the power supply system and calculate the actual power consumption by monitoring current, voltage and power factor;

[0013] Gas flow sensor: used to collect gas consumption data of the gas supply system and calculate the gas consumption by monitoring the gas flow rate;

[0014] Temperature sensor and humidity sensor: used for energy consumption monitoring of heating and cooling systems. It collects changes in ambient temperature and humidity and calculates energy consumption in combination with the operation of heating and cooling equipment.

[0015] The raw energy consumption data collected by each sensor is transmitted to the central processing unit. After receiving the raw energy consumption data from each sensor node, the central processing unit performs data preprocessing, which includes:

[0016] S11: Usage data: Normalize the usage data of each energy system to the same unit (unify the units of water, electricity and gas into standard units) to ensure that the data of different energy types can be directly compared;

[0017] S12: Fluctuation amplitude data: Calculate the amplitude or variance of energy consumption fluctuations based on the data changes within a predetermined time period. The fluctuation amplitude is expressed as standard deviation. To indicate that, set the time period The energy consumption data is , then the fluctuation range, that is, the standard deviation is calculated as: ,in, is the mean value of energy consumption data, is the number of samples, It is Energy consumption data at each sampling moment;

[0018] S13: Energy consumption time period data: Based on time series data, classify the consumption data of each energy system in different time periods, identify the peak energy consumption period and the valley energy consumption period of each energy system, determine the optimal use time of each energy, divide the total time period into several sub-time periods, and calculate the total energy consumption of each sub-time period ,Mark the time periods in each sub-time period when the energy consumption is greater than the predetermined threshold (the threshold is set to 90% of the historical data) as peak energy consumption periods.Integrate the usage data, fluctuation range and energy consumption time period into a complete energy consumption data set.

[0019] Optionally, S2 performs a comprehensive analysis based on the energy consumption data set obtained in S1 and combines it with historical energy consumption data to generate an energy consumption interaction model between various energy systems, revealing the relationship between various energy systems (including water supply system, power supply system, gas supply system, heating system and cooling system), and providing a decision-making basis for subsequent collaborative energy-saving strategies, including:

[0020] S21, fusion of historical energy consumption data and real-time data: combining historical energy consumption data with real-time collected energy consumption data to construct a comprehensive energy consumption data set. The historical data includes the energy consumption data of each energy system obtained by the S1 method at different time periods.

[0021] S22, by calculating the correlation coefficient between each energy system , analyzing the linear correlation between consumption patterns in different time periods, including the water supply system Power supply system As an example, and In time The energy consumption is and , correlation coefficient The calculation formula is: , Indicates the time, Indicates Water supply system at all times The energy consumption value, Indicates All-time power supply system The energy consumption value, For water supply system The average energy consumption, For power supply system The average energy consumption, is the number of samples, the value range of Pearson correlation coefficient is [-1,1]. When it approaches 1 or 1, it means that the energy consumption data of the two systems are highly linearly correlated; When the value is close to 0, it means that there is no significant linear relationship between the energy consumption data of the two systems;

[0022] S23, clustering analysis is performed on the energy consumption data of each energy system using clustering algorithms to identify similar patterns of energy consumption and the laws of coordinated consumption. For example, the heating system and the cooling system may show similar load fluctuations under similar seasonal patterns, and clustering algorithms can help reveal this coordinated consumption pattern;

[0023] S24, generate energy consumption interaction model: Based on correlation coefficient and cluster analysis, construct a multi-dimensional energy consumption interaction model to reflect the mutual influence and potential conflict between various energy systems:

[0024] Usage data interaction sub-model: Analyze the consumption of different energy systems in the same time period and identify the interdependence or opposition between systems;

[0025] The synergistic effect sub-model of fluctuation amplitude: Based on the fluctuation amplitude of energy consumption of each energy system, i.e., standard deviation, it is determined whether there is a superposition or suppression effect of fluctuations between certain energy systems;

[0026] Energy consumption time period interaction sub-model: By analyzing the data of energy consumption time periods, the energy efficiency interaction patterns of different energy systems in daily and seasonal changes are identified. The energy consumption data of a certain day or season is set, and the coordinated consumption trends of different systems in these time periods are analyzed by dividing them into time periods (peak, off-peak and low-peak periods).

[0027] Optionally, the clustering algorithm in S23 is used to analyze the energy consumption patterns of different energy systems to discover the rules of collaborative consumption in the time period The energy consumption data of water supply system, power supply system, gas supply system, heating system and cooling system are as follows: ,in Representing different energy systems, K-means clustering is used to cluster similar consumption patterns into the same class. The K-means clustering goal is to minimize the variance of energy consumption data within the class:

[0028] initialization Cluster Centers ;

[0029] Each sample data Assign to the closest cluster center ,form clusters;

[0030] Calculate the center point of each cluster (new cluster centers), and update the cluster centers: ,in, Is a cluster The set of all samples of Is a collection The number of samples in ;

[0031] The cluster centers are updated repeatedly until they converge or the maximum number of iterations is reached.

[0032] The clustering results will reveal the collaborative consumption patterns among energy systems. For example, some energy systems (such as heating and cooling) may show similar energy consumption fluctuations in certain seasons or time periods.

[0033] Optionally, the usage data interaction sub-model is implemented through a multi-dimensional interaction matrix Indicates that based on the Pearson correlation coefficient , which represents the interactive relationship between energy consumption data among energy systems.

[0034] Optionally, the multidimensional interaction matrix Elements It is expressed as follows:

[0035] ,in, is a The matrix of is the number of energy systems, each element Indicates energy system and The correlation between energy systems.

[0036] Optionally, the energy efficiency in S3 is calculated as:

[0037] ,in, Indicates the time period Internal energy system Energy efficiency, Indicates the time period Internal energy system Output of services provided, Indicates the time period Internal energy system energy consumption, Represents energy system and The correlation coefficient between : Indicates time period Internal energy system Energy efficiency change, used to correct The efficiency of this formula shows that by introducing the interaction effects of other systems (i.e. and ), which can more accurately calculate the actual efficiency of each energy system and comprehensively consider the interactions between them.

[0038] Optionally, the identifying of the peak energy consumption period and the redundant usage in S3 specifically includes:

[0039] Peak energy consumption identification during peak hours: By analyzing the energy consumption data and correlation coefficients of each energy system, the peak energy consumption during peak hours is identified. The identification of peak energy consumption not only depends on the energy consumption changes of a single energy system, but also considers the impact of other energy systems on the peak period. For example, the load fluctuations of the heating and cooling systems may have a strong correlation with the peak load of the power supply system, affecting its efficiency; Peak energy consumption during peak hours Calculated as:

[0040] .

[0041] Redundant usage identification: Combine the correlation coefficient and energy efficiency in the energy consumption interaction model to analyze the redundant usage. If an energy system still maintains a high energy consumption level without the need for additional energy input, it indicates that there is redundant usage; the judgment is expressed as:

[0042] ; During low demand periods (such as at night), if The system's high energy consumption and low efficiency indicate redundant usage.

[0043] Optionally, the automatically generated collaborative energy-saving strategy in S3 specifically includes:

[0044] During the peak period of a certain energy system, the collaborative energy-saving strategy adjusts the load of other energy systems to reduce overall energy consumption. For example, when the air-conditioning system is in a high-load period, unnecessary power consumption can be appropriately reduced and power supply can be concentrated to priority equipment. At the same time, the operating parameters of the cooling system (such as the operating frequency of the compressor) can be optimized to balance the load and avoid excessive consumption of the system;

[0045] For redundant usage identified during off-peak hours, collaborative energy-saving strategies adjust the operating status of the corresponding energy systems to avoid ineffective consumption. For example, during low-usage periods at night, the output of gas, water or cooling systems or the energy consumption of standby mode are reduced to avoid excessive energy consumption during low-demand periods.

[0046] According to the interaction between various energy systems, the coordinated energy-saving strategy disperses or transfers the energy consumption of high-load energy systems. For example, when the load of the power supply system is high and the correlation analysis shows that the demand for the cooling system will increase the pressure on the power supply system, the operating load of the cooling system can be appropriately reduced, or part of the power demand can be dispersed through other systems (such as the water supply system) to form a coordinated transfer of load. This can ensure the balance of energy supply during peak hours and avoid overloading of a single system.

[0047] Beneficial effects of the present invention:

[0048] The present invention establishes an energy consumption interaction model between energy systems by real-time monitoring of multiple energy systems in a building. The model can not only reveal the mutual relationship and synergy between the systems, but also dynamically calculate the energy efficiency of each system in different time periods. This analysis method based on interactive relationships enables the collaborative energy-saving strategy to automatically optimize the scheduling of each system in different time periods to avoid the problem of overload or low energy efficiency of a single system. Through this method, not only the overall energy efficiency of each energy system can be greatly improved, but also the load between the systems can be effectively balanced, ensuring the rational use of energy and the long-term stable operation of the system.

[0049] The present invention can accurately identify the load characteristics of peak hours and redundant energy consumption during non-peak hours through in-depth analysis of the energy efficiency of each energy system. Based on this analysis, the system can generate collaborative energy-saving strategies, automatically optimize energy allocation, and reduce unnecessary energy waste. Especially during peak hours, the system can prioritize the energy allocation of key systems and postpone or reduce the energy consumption of secondary systems based on the energy consumption interaction model, thereby achieving the effect of alleviating high loads and reducing energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A schematic diagram of a collaborative energy-saving method flow chart of an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of the energy consumption interaction model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0054] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0055] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0056] like Figure 1-Figure 2 As shown, a method for monitoring water, electricity, gas, heating and cooling energy consumption and coordinated energy saving comprises the following steps:

[0057] S1: Through the multi-sensor nodes installed in the building, the energy consumption data of each energy system is collected in real time. The energy system includes the water supply system, power supply system, gas supply system, heating system, and cooling system. The energy consumption data includes the usage data, fluctuation range, and energy consumption time period; the energy consumption data is transmitted to the central processing unit for pre-processing to form a complete energy consumption data set;

[0058] S2: Based on the acquired energy consumption data set and combined with historical energy consumption data, the relationship between various energy systems is comprehensively analyzed to generate an energy consumption interaction model between various energy systems;

[0059] S3: Based on the established energy consumption interaction model, calculate the energy efficiency of the water supply system, power supply system, gas supply system, heating system, and cooling system in different time periods, identify peak energy consumption periods and redundant usage, automatically generate collaborative energy-saving strategies, and optimize the scheduling between various energy systems.

[0060] A multi-sensor node consists of installing a variety of sensors in various energy systems within a building, including:

[0061] Water flow sensor: used to collect water consumption data of the water supply system and record water flow rate and usage in real time;

[0062] Current and voltage sensors: used to collect power consumption data of the power supply system and calculate the actual power consumption by monitoring current, voltage and power factor;

[0063] Gas flow sensor: used to collect gas consumption data of the gas supply system and calculate the gas consumption by monitoring the gas flow rate;

[0064] Temperature sensor and humidity sensor: used for energy consumption monitoring of heating and cooling systems. It collects changes in ambient temperature and humidity and calculates energy consumption in combination with the operation of heating and cooling equipment.

[0065] The raw energy consumption data collected by each sensor is transmitted to the central processing unit. After receiving the raw energy consumption data from each sensor node, the central processing unit performs data preprocessing, which includes:

[0066] S11: Usage data: Normalize the usage data of each energy system to the same unit (unify the units of water, electricity and gas into standard units) to ensure that the data of different energy types can be directly compared;

[0067] S12: Fluctuation amplitude data: Calculate the amplitude or variance of energy consumption fluctuations based on the data changes within a predetermined time period. The fluctuation amplitude is expressed as standard deviation. To indicate that, set the time period The energy consumption data is , then the fluctuation range, that is, the standard deviation is calculated as: ,in, is the mean value of energy consumption data, is the number of samples, It is Energy consumption data at each sampling moment;

[0068] S13: Energy consumption time period data: Based on time series data, classify the consumption data of each energy system in different time periods, identify the peak energy consumption period and the valley energy consumption period of each energy system, determine the optimal use time of each energy, divide the total time period into several sub-time periods, and calculate the total energy consumption of each sub-time period ,Mark the time periods in each sub-time period when the energy consumption is greater than the predetermined threshold (the threshold is set to 90% of the historical data) as peak energy consumption periods.Integrate the usage data, fluctuation range and energy consumption time period into a complete energy consumption data set.

[0069] In S2, based on the energy consumption data set obtained in S1 and combined with historical energy consumption data, a comprehensive analysis is conducted to generate an energy consumption interaction model between energy systems, revealing the relationship between various energy systems (including water supply system, power supply system, gas supply system, heating system and cooling system), and providing a decision-making basis for subsequent collaborative energy-saving strategies, including:

[0070] S21, fusion of historical energy consumption data and real-time data: combining historical energy consumption data with real-time collected energy consumption data to construct a comprehensive energy consumption data set. The historical data includes the energy consumption data of each energy system obtained by the S1 method at different time periods.

[0071] S22, by calculating the correlation coefficients between various energy systems , analyzing the linear correlation between consumption patterns in different time periods, including the water supply system Power supply system As an example, and In time The energy consumption is and , correlation coefficient The calculation formula is: , Indicates the time, Indicates Water supply system at all times The energy consumption value, Indicates All-time power supply system The energy consumption value, For water supply system The average energy consumption, For power supply system The average energy consumption, is the number of samples, the value range of Pearson correlation coefficient is [-1,1]. When it approaches 1 or 1, it means that the energy consumption data of the two systems are highly linearly correlated; When the value is close to 0, it means that there is no significant linear relationship between the energy consumption data of the two systems;

[0072] S23, clustering analysis is performed on the energy consumption data of each energy system using clustering algorithms to identify similar patterns of energy consumption and the laws of coordinated consumption. For example, the heating system and the cooling system may show similar load fluctuations under similar seasonal patterns, and clustering algorithms can help reveal this coordinated consumption pattern;

[0073] S24, generate energy consumption interaction model: Based on correlation coefficient and cluster analysis, construct a multi-dimensional energy consumption interaction model to reflect the mutual influence and potential conflict between various energy systems:

[0074] Usage data interaction sub-model: Analyze the consumption of different energy systems in the same time period and identify the interdependence or opposition between systems;

[0075] The synergistic effect sub-model of fluctuation amplitude: According to the fluctuation amplitude of energy consumption of each energy system, i.e., standard deviation, it is judged whether there is a superposition or suppression effect of fluctuations between certain energy systems. Suppose the energy consumption of the heating system and the cooling system in a certain period of time are and , their fluctuation range (standard deviation) is calculated by the following formula:

[0076] ;

[0077] ;

[0078] if and The fluctuation amplitude of is large and their correlation is strong, indicating that there may be a phenomenon of coordinated load fluctuation between the two systems;

[0079] Energy consumption time period interaction sub-model: By analyzing the data of energy consumption time periods, the energy efficiency interaction patterns of different energy systems in daily and seasonal changes are identified. The energy consumption data of a certain day or season is set, and the coordinated consumption trends of different systems in these time periods are analyzed by dividing them into time periods (peak, off-peak and low-peak periods).

[0080] The clustering algorithm in S23 is used to analyze the energy consumption patterns of different energy systems to discover the laws of collaborative consumption in time periods. The energy consumption data of water supply system, power supply system, gas supply system, heating system and cooling system are as follows: ,in Representing different energy systems, K-means clustering is used to cluster similar consumption patterns into the same class. The K-means clustering goal is to minimize the variance of energy consumption data within the class:

[0081] initialization Cluster Centers ;

[0082] Each sample data Assign to the closest cluster center ,form clusters;

[0083] Calculate the center point of each cluster (new cluster centers) and update the cluster centers: ,in, Is a cluster The set of all samples of Is a collection The number of samples in ;

[0084] The cluster centers are updated repeatedly until they converge or the maximum number of iterations is reached.

[0085] The clustering results will reveal the collaborative consumption patterns among energy systems. For example, some energy systems (such as heating and cooling) may show similar energy consumption fluctuations in certain seasons or time periods.

[0086] The usage data interaction sub-model uses a multi-dimensional interaction matrix Indicates that based on the Pearson correlation coefficient , which represents the interactive relationship between energy consumption data among energy systems.

[0087] Multidimensional interaction matrix Elements It is expressed as follows:

[0088] ,in, is a The matrix of is the number of energy systems, each element Indicates energy system and The correlation between energy systems.

[0089] In the energy consumption interaction model generated above, the mutual influence between various energy systems has been revealed through correlation analysis and cluster analysis. Based on this model, the energy efficiency performance of each energy system in different time periods is identified, especially the impact of the relationship between each system on the overall energy utilization efficiency.

[0090] For example, assuming that there is a strong correlation between the water supply system and the power supply system, the energy efficiency changes of these two systems may be synchronized to a certain extent. In order to take into account the interactive relationship between their energy consumption, the energy efficiency in S3 is calculated as:

[0091] ,in, Indicates the time period Internal energy system Energy efficiency, Indicates the time period Internal energy system Output of services provided, Indicates the time period Internal energy system energy consumption, Represents energy system and The correlation coefficient between : Indicates time period Internal energy system Energy efficiency change, used to correct The efficiency of this formula shows that by introducing the interaction effects of other systems (i.e. and ), which can more accurately calculate the actual efficiency of each energy system and comprehensively consider the interactions between them.

[0092] Identifying peak energy consumption periods and redundant usage in S3 specifically includes:

[0093] Peak energy consumption identification during peak hours: By analyzing the energy consumption data and correlation coefficients of each energy system, the peak energy consumption during peak hours is identified. The identification of peak energy consumption not only depends on the energy consumption changes of a single energy system, but also considers the impact of other energy systems on the peak period. For example, the load fluctuations of the heating and cooling systems may have a strong correlation with the peak load of the power supply system, affecting its efficiency; Peak energy consumption during peak hours Calculated as:

[0094] .

[0095] Redundant usage identification: Combine the correlation coefficient and energy efficiency in the energy consumption interaction model to analyze the redundant usage. If an energy system still maintains a high energy consumption level without the need for additional energy input, it indicates that there is redundant usage; the judgment is expressed as:

[0096] ; During low demand periods (such as at night), if The system's high energy consumption and low efficiency indicate redundant usage.

[0097] The automatic generation of collaborative energy-saving strategies in S3 specifically includes:

[0098] Some energy systems (such as cooling and heating systems) have heavy loads, which may lead to reduced energy efficiency and waste of resources. Therefore, during the peak period of a certain energy system, the coordinated energy-saving strategy adjusts the load of other energy systems to reduce overall energy consumption. For example, when the air-conditioning system is in a high-load period, unnecessary power consumption can be appropriately reduced and power supply can be concentrated to priority equipment. At the same time, the operating parameters of the cooling system (such as the operating frequency of the compressor) can be optimized to balance the load and avoid excessive consumption of the system;

[0099] For redundant usage identified during off-peak hours, collaborative energy-saving strategies adjust the operating status of the corresponding energy systems to avoid ineffective consumption. For example, during low-usage periods at night, the output of gas, water or cooling systems or the energy consumption of standby mode are reduced to avoid excessive energy consumption during low-demand periods.

[0100] According to the interaction between various energy systems, the coordinated energy-saving strategy disperses or transfers the energy consumption of high-load energy systems. For example, when the load of the power supply system is high and the correlation analysis shows that the demand for the cooling system will increase the pressure on the power supply system, the operating load of the cooling system can be appropriately reduced, or part of the power demand can be dispersed through other systems (such as the water supply system) to form a coordinated transfer of load. This can ensure the balance of energy supply during peak hours and avoid overloading of a single system.

[0101] Also includes intelligent priority scheduling:

[0102] According to the urgency of peak energy consumption periods and the importance of the system, priorities can be assigned to different energy systems when automatically generating energy-saving strategies. During peak periods, priority is given to ensuring the energy needs of core energy systems, and energy consumption of non-critical energy systems is delayed or reduced. For example, during peak periods, priority is given to ensuring the load needs of power supply and heating systems, while reducing the load on non-core functions of cooling and gas supply systems. This ensures that the overall energy utilization efficiency is optimized while ensuring important energy needs.

[0103] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0104] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring water, electricity, gas, heating and cooling energy consumption and collaborative energy saving, characterized in that: The following steps are involved: S1: Through the multi-sensor nodes installed in the building, the energy consumption data of each energy system is collected in real time. The energy system includes the water supply system, power supply system, gas supply system, heating system, and cooling system. The energy consumption data includes the usage data, fluctuation range, and energy consumption time period; the energy consumption data is transmitted to the central processing unit for pre-processing to form a complete energy consumption data set; S2: Based on the acquired energy consumption data set and combined with historical energy consumption data, the relationship between various energy systems is comprehensively analyzed to generate an energy consumption interaction model between various energy systems; S3: Based on the established energy consumption interaction model, calculate the energy efficiency of the water supply system, power supply system, gas supply system, heating system, and cooling system in different time periods, identify peak energy consumption periods and redundant usage, automatically generate collaborative energy-saving strategies, and optimize the scheduling between various energy systems.

2. A method for monitoring and collaborative energy saving of water, electricity, gas, heating and cooling energy consumption according to claim 1, characterized in that: The multi-sensor node includes installing multiple sensors in various energy systems within the building, specifically including: Water flow sensor: used to collect water consumption data of the water supply system and record water flow rate and usage in real time; Current and voltage sensors: used to collect power consumption data of the power supply system and calculate the actual power consumption by monitoring current, voltage and power factor; Gas flow sensor: used to collect gas consumption data of the gas supply system and calculate the gas consumption by monitoring the gas flow rate; Temperature sensor and humidity sensor: used for energy consumption monitoring of heating and cooling systems. It collects changes in ambient temperature and humidity and calculates energy consumption in combination with the operation of heating and cooling equipment. The raw energy consumption data collected by each sensor is transmitted to the central processing unit. After receiving the raw energy consumption data from each sensor node, the central processing unit performs data preprocessing, which includes: S11: Usage data: Normalize the usage data of each energy system to the same unit; S12: Fluctuation amplitude data: Calculate the amplitude or variance of energy consumption fluctuations based on the data changes within a predetermined time period. The fluctuation amplitude is expressed as standard deviation. To indicate that, set the time period The energy consumption data is , then the fluctuation range, that is, the standard deviation is calculated as: ,in, is the mean value of energy consumption data, is the number of samples, It is Energy consumption data at each sampling moment; S13: Energy consumption time period data: Based on time series data, classify the consumption data of each energy system in different time periods, identify the peak energy consumption period and the valley energy consumption period of each energy system, determine the optimal use time of each energy, divide the total time period into several sub-time periods, and calculate the total energy consumption of each sub-time period , marking the time periods in each sub-time period when the energy consumption is greater than a predetermined threshold as peak energy consumption periods.

3. A method for monitoring and collaborative energy saving of water, electricity, gas, heating and cooling energy consumption according to claim 2, characterized in that: In S2, based on the energy consumption data set obtained in S1, combined with historical energy consumption data, a comprehensive analysis is performed to generate an energy consumption interaction model between various energy systems, specifically including: S21, fusion of historical energy consumption data and real-time data: combining historical energy consumption data with real-time collected energy consumption data to construct a comprehensive energy consumption data set. The historical data includes the energy consumption data of each energy system obtained by the S1 method at different time periods. S22, by calculating the correlation coefficient between each energy system , analyzing the linear correlation between consumption patterns in different time periods, including water supply systems Power supply system The correlation coefficient calculation is and In time The energy consumption is and ; S23, clustering analysis is performed on the energy consumption data of each energy system using a clustering algorithm to identify similar patterns of energy consumption and the rules of coordinated consumption; S24, generate energy consumption interaction model: Based on correlation coefficient and cluster analysis, construct a multi-dimensional energy consumption interaction model to reflect the mutual influence and potential conflict between various energy systems: Usage data interaction sub-model: Analyze the consumption of different energy systems in the same time period and identify the interdependence or opposition between systems; The synergistic effect sub-model of fluctuation amplitude: Based on the fluctuation amplitude of energy consumption of each energy system, i.e., standard deviation, it is determined whether there is a superposition or suppression effect of fluctuations between certain energy systems; Energy consumption time period interaction sub-model: By analyzing the data of energy consumption time periods, the energy efficiency interaction patterns of different energy systems in daily and seasonal changes are identified.

4. A method for monitoring and coordinating energy consumption of water, electricity, gas, heating and cooling according to claim 3, characterized in that: The clustering algorithm in S23 is used to analyze the energy consumption patterns of different energy systems to discover the rules of coordinated consumption in the time period The energy consumption data of water supply system, power supply system, gas supply system, heating system and cooling system are as follows: ,in Representing different energy systems, K-means clustering is used to cluster similar consumption patterns into the same class. The K-means clustering goal is to minimize the variance of energy consumption data within the class: initialization Cluster Centers ; Each sample data Assign to the closest cluster center ,form clusters; Calculate the center point of each cluster , and update the cluster centers: ,in, Is a cluster The set of all samples of Is a collection The number of samples in ; The cluster centers are updated repeatedly until they converge or the maximum number of iterations is reached.

5. A method for monitoring and collaborative energy saving of water, electricity, gas, heating and cooling energy consumption according to claim 4, characterized in that: The usage data interaction sub-model is implemented through a multi-dimensional interaction matrix Indicates that based on the Pearson correlation coefficient , which represents the interactive relationship between energy consumption data among energy systems.

6. A method for monitoring and coordinating energy consumption of water, electricity, gas, heating and cooling according to claim 5, characterized in that: The multidimensional interaction matrix Elements It is expressed as follows: ,in, is a The matrix of is the number of energy systems, each element Indicates energy system and The correlation between energy systems.

7. A method for monitoring and collaborative energy saving of water, electricity, gas, heating and cooling energy consumption according to claim 6, characterized in that: The energy efficiency in S3 is calculated as: ,in, Indicates the time period Internal energy system Energy efficiency, Indicates the time period Internal energy system Output of services provided, Indicates the time period Internal energy system energy consumption, Represents energy system and The correlation coefficient between : Indicates time period Internal energy system Energy efficiency change, used to correct efficiency.

8. A method for monitoring and collaborative energy saving of water, electricity, gas, heating and cooling energy consumption according to claim 7, characterized in that: The identification of energy consumption peak hours and redundant usage in S3 specifically includes: Peak energy consumption identification during peak hours: By analyzing the energy consumption data and correlation coefficients of each energy system, the peak energy consumption during peak hours is identified. The identification of peak energy consumption depends not only on the energy consumption changes of a single energy system, but also on the impact of other energy systems on the peak period. Calculated as: ; Identification of redundant usage: Combine the correlation coefficient and energy efficiency in the energy consumption interaction model to analyze redundant usage. If an energy system maintains a high energy consumption level without the need for additional energy input, it indicates that there is redundant usage.

9. A method for monitoring and collaborative energy saving of water, electricity, gas, heating and cooling energy consumption according to claim 8, characterized in that: The automatic generation of collaborative energy-saving strategies in S3 specifically includes: During the peak period of a certain energy system, the collaborative energy-saving strategy adjusts the load of other energy systems to reduce the overall energy consumption; For redundant usage identified during off-peak hours, collaborative energy-saving strategies are used to adjust the operating status of the corresponding energy systems to avoid ineffective consumption; Based on the interactive relationship between various energy systems, the collaborative energy-saving strategy disperses or transfers the energy consumption of high-load energy systems.

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