Energy-saving strategy optimization method for existing building air conditioner and lighting system

Abstract

The invention discloses an energy-saving strategy optimization method for an existing building air conditioner and lighting system, and belongs to the technical field of energy-saving optimization. Temperature and light intensity parameters are set according to the indoor environment requirement of the building, and operation data of all the air conditioner lighting subsystems are stored; constructing a mode switching time axis of a daily use cycle, and marking an air conditioner-illumination cooperative operation segment; counting the operation duration and the total energy consumption of each fragment in the period, and adding a mode mark for the data; constructing a collaborative operation feature map, and evaluating a mode switching state value and an energy-saving gain value of each fragment; an energy-saving optimization response range is constructed based on the gain value, and subsystem operation parameters are fed back and adjusted; according to the method, collaborative energy conservation of air conditioners and illumination is achieved, scene adaptability and energy-saving effect stability are improved, the problems that an existing scheme is poor in collaboration, difficult to quantify and lack of feedback are solved, and the method is suitable for energy-saving transformation of air conditioner illumination systems of various existing buildings and is low in implementation cost and remarkable in effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy-saving optimization, in particular to an energy-saving strategy optimization method for air conditioning and lighting systems of existing buildings. BACKGROUND

[0002] In the long-term operation of existing buildings, air conditioning and lighting systems are the two types of equipment with the highest energy consumption proportion, accounting for more than 60% of the total building energy consumption, and their energy-saving level directly determines the overall energy efficiency of the building.

[0003] Existing solutions mostly adopt the mode of independent control of air conditioning and lighting systems, such as fixed temperature threshold operation of air conditioning (26℃ in summer and 20℃ in winter) and fixed brightness operation of lighting (500 lux in office scenarios), without considering the synergistic energy-saving potential of the two in different use scenarios. For example, in the lunch break period of an office building, the number of personnel activities decreases, and only the air conditioning load is reduced while the lighting brightness is maintained, or only the lighting is dimmed while the air conditioning load is maintained, both of which result in energy waste. At the same time, most energy-saving solutions do not combine the environmental demand changes of the building's daily use cycle, adopt the "one-size-fits-all" fixed operation mode, and have no real-time feedback and dynamic optimization. For example, in an office building, the personnel density and activity intensity differ significantly in the morning (8:00-12:00), lunch break (12:00-13:00), and afternoon (13:00-18:00), and the demand for temperature and light intensity is different. However, existing solutions do not adjust the operation parameters for different time periods, resulting in "overcooling / overheating" and "overbright / dim", which not only affects the use comfort but also increases invalid energy consumption. Moreover, when the system energy consumption fluctuates due to seasonal changes (high temperature in summer and low temperature in winter), the parameters cannot be adjusted according to the actual operation state, and when the energy consumption rises due to the aging of the subsystem, it cannot be identified and optimized in time, so the energy-saving effect is difficult to maintain for a long time. SUMMARY

[0004] The purpose of the present application is to provide an energy-saving strategy optimization method for air conditioning and lighting systems of existing buildings to solve the problems raised in the background.

[0005] In order to solve the above technical problems, the present application provides the following technical solutions: An energy-saving strategy optimization method for air conditioning and lighting systems of existing buildings, the method comprising the following steps: Step S1: According to the environmental demand of the indoor environment of the existing building, set the control parameter range of indoor temperature and light intensity to form an indoor environment mode, and store the corresponding indoor environment mode data of each air conditioning and lighting subsystem, which records the operation parameters of the system; Step S2: constructing a mode switching time axis in a daily building use cycle, determining the scale and mode switching nodes of the time axis, and marking the indoor environment mode switching segments of the air conditioner and lighting system in adjacent nodes; Step S3: taking the daily building use cycle as a cycle period, adding mode labels to the indoor environment mode data, and simultaneously counting the indoor environment mode data clusters of the air conditioner and lighting system in each mode switching segment, wherein the clusters record the running time and energy consumption of the air conditioner and lighting system; Step S4: constructing an air conditioner-lighting system collaborative operation feature map, dividing the feature map into shape regions corresponding to the mode switching segments, and calculating the mode switching state values corresponding to each shape region, wherein the shape regions include triangular regions and sector regions; Step S5: based on the comparison and analysis of the shape regions, evaluating the energy saving gain value of the air conditioner and lighting system; Step S6: determining the energy saving optimization range and real-time energy saving feedback range based on all the energy saving gain values, forming an energy saving optimization response range, and adjusting the indoor environment mode data of each air conditioner and lighting system according to the energy saving optimization response range, so as to realize system energy saving optimization.

[0006] As a preferred scheme of the present application, the specific implementation process of step S1 comprises: Based on the environmental requirements of the existing building indoor environment, the parameter domains of the building indoor temperature and light intensity are initialized to be set to form the indoor environment mode, and the parameter domain of the building indoor temperature is controlled by the air conditioning system, and the parameter domain of the building indoor light intensity is controlled by the lighting system; The indoor environment mode data cluster with each set of air conditioner and lighting system as a data source is stored, and the mode running parameters including the refrigeration / heating capacity of the air conditioning system, the air conditioning energy consumption, the rated power of the lighting system, and the lighting energy consumption are recorded in the indoor environment mode data cluster, wherein one environmental requirement mode corresponds to one indoor environment mode data cluster.

[0007] As a preferred scheme of the present application, the specific implementation process of step S2 comprises: The mode switching time axis is constructed, the starting point of the mode switching time axis is the starting time of the daily building use, the scale t of the mode switching time axis is initialized, and the mode switching node labels are sequentially performed on the mode switching time axis based on the scale t, and any mode switching node with a label y is recorded as ; Based on the mode switching time axis, the switching time segment composed of any two adjacent mode switching nodes is obtained, and the indoor environment mode switching segment of the air conditioner-lighting system collaborative operation is marked, which is recorded as , wherein represents the mode switching node with a label y+1.

[0008] As a preferred scheme of the present application, the implementation process of step S3 comprises: taking the building daily use cycle as the mode cycle, adding mode labels to the indoor environment mode data cluster, and based on the indoor environment mode switching segment labeled by the mode switching time axis, accumulating statistics of the indoor environment mode switching segment , the running time of the air conditioning lighting subsystem formed therein, and the subsystem energy consumption, which is the cumulative sum of the air conditioning energy consumption and the lighting energy consumption; the indoor environment mode data cluster with the added mode labels is recorded as , , wherein, represents the indoor environment mode data cluster corresponding to the air conditioning lighting subsystem numbered i, represents the mode running parameters of the air conditioning lighting subsystem numbered i in the indoor environment mode switching segment represents the subsystem running time, represents the subsystem energy consumption, and k is the label of the mode switching node.

[0009] As a preferred scheme of the present application, the implementation process of step S4 comprises: constructing an air conditioning-lighting collaborative operation feature map, the air conditioning-lighting collaborative operation feature map being circular, with the center point being the fixed point and the radius being S, and S being the standard energy consumption of the air conditioning lighting subsystem in the scale t range initialized and configured; in the air conditioning-lighting collaborative operation feature map, taking the center as the center point, the circle is divided into k equal sectors, wherein one radius of the sector corresponds to one indoor environment mode switching segment; in the air conditioning-lighting collaborative operation feature map corresponding to the air conditioning lighting subsystem numbered i, the indoor environment mode switching segment corresponding sector radius is recorded as , and represents the mode switching state value of the air conditioning-lighting collaborative operation behavior, and , if , it indicates that the air conditioning lighting subsystem is not running in the indoor environment mode switching segment , and .

[0010] As a preferred scheme of the present application, the implementation process of step S5 comprises: in the air conditioning-lighting collaborative operation feature map corresponding to the air conditioning lighting subsystem numbered i, taking the center as the vertex, and linearly connecting the radius and the radius the end of the air conditioning system and the lighting system, forming a cooperative operation feature triangle, denoted as , wherein, represents the mode switching state value corresponding to the air conditioning and lighting subsystem numbered i; In the air conditioning and lighting cooperative operation feature map formed by the air conditioning and lighting subsystem numbered i, the sector area between the radius and the radius is obtained with the center of the circle as the vertex, denoted as ; The energy saving gain value of the air conditioning and lighting subsystem numbered i is evaluated , wherein, represents the area of the cooperative operation feature triangle .

[0011] As a preferred scheme of the present application, the specific implementation process of the step S6 includes: Based on the energy saving gain value, an energy saving optimization range is constructed, wherein, is the mean value of the energy saving gain value, and , is the variance of the energy saving gain value, and ; Based on the energy saving gain value, a real-time energy saving feedback range is constructed; Based on the energy saving optimization range and the real-time energy saving feedback range, an energy saving optimization response range is generated, wherein max{} and min{} are maximum and minimum value functions, respectively; The energy saving optimization response range is taken as the energy saving optimization target, and the indoor environment mode data cluster of the air conditioning and lighting subsystem numbered i is fed back to satisfy the energy saving optimization response range when the air conditioning system and the lighting system control the indoor temperature parameter domain and the light intensity parameter domain of the building, respectively.

[0012] An energy saving strategy optimization system for existing building air conditioning and lighting systems includes a storage and a processor. The storage is used to store a program of an energy saving strategy optimization method for existing building air conditioning and lighting systems. The processor is used to execute the program of the energy saving strategy optimization method for existing building air conditioning and lighting systems. The processor also integrates core functional modules, including an environment parameter and mode data management module, a time axis and switching segment processing module, an operation data statistical module, a cooperative feature analysis module, an energy saving gain evaluation module, and an energy saving optimization feedback module. The environment parameter and mode data management module and the time axis and switching segment processing module jointly constitute a basic data processing unit of the system. The environment parameter and mode data management module is used for setting the control parameter range of indoor temperature and light intensity to form an indoor environment mode according to the indoor environment demand of the existing building, and storing the indoor environment mode data corresponding to each air conditioning and lighting subsystem, so as to ensure that the data record is complete and corresponds to the subsystem one by one; and the time axis and switching segment processing module is used for constructing a mode switching time axis based on the daily use cycle of the building, determining the time axis scale and mode switching node, marking the air conditioning-lighting collaborative operation switching segment between adjacent nodes, and realizing the accurate matching of the switching segment and the actual use scene.

[0013] As a preferred scheme of the present application, the operation data statistical module, the collaborative feature analysis module, the energy saving gain evaluation module and the energy saving optimization feedback module jointly constitute an energy saving analysis and optimization unit of the system. The operation data statistical module is used for adding a mode mark to the indoor environment mode data with the daily use cycle of the building as a cycle period, and statistically calculating the operation time length and total energy consumption of the air conditioning and lighting subsystems in each switching segment, so as to ensure the timeliness and accuracy of data statistics; the collaborative feature analysis module is used for constructing an air conditioning-lighting collaborative operation feature map for each air conditioning and lighting subsystem, dividing the sector area corresponding to the switching segment, calculating the mode switching state value of each area, and converting the abstract operation data into intuitive graphical features; the energy saving gain evaluation module is used for constructing a collaborative operation feature triangle corresponding to adjacent state values in the collaborative operation feature map, calculating the ratio of the area of the triangle to the area of the corresponding sector, obtaining the energy saving gain value, and realizing the quantitative evaluation of the energy saving effect; and the energy saving optimization feedback module is used for constructing an energy saving optimization range, a real-time energy saving feedback range and an energy saving optimization response range of the intersection processing of the two ranges based on the mean value and variance of the energy saving gain value, feeding back the indoor environment mode data of the subsystem, and ensuring the realization of the energy saving optimization target.

[0014] As a preferred scheme of the present application, a kind of energy saving strategy optimization system for existing building air conditioning and lighting system is connected with existing building air conditioning and lighting system to realize the energy saving strategy optimization method for the existing building air conditioning and lighting system.

[0015] Compared with the prior art, the present application has the following beneficial effects: (1) Through collaborative feature analysis and gain evaluation, the collaborative energy saving space of air conditioning and lighting in different scenes is excavated to break through the limitation of independent control. For example, in the lunch break period, the air conditioning load (temperature is raised by 1-2 DEG C) and the lighting brightness (light intensity is reduced by 50%-60%) are simultaneously reduced, which can reduce the energy consumption of the period compared with the independent control scheme, and also reduce the comprehensive building energy consumption. (2) Based on the time axis and switching segments of the daily usage cycle, the design adapts to the environmental needs of different time periods, such as maintaining a high level of comfort during the morning / afternoon office hours of office buildings (temperature 24-26℃, light intensity 300-500 lux), and reducing energy consumption during the lunch break (temperature 26-27℃, light intensity 150-200 lux). (3) By statistical analysis of operational data and analysis of energy-saving gain value, the energy-saving potential and effect can be quantitatively evaluated, so as to effectively control the gain value error, accurately identify high-energy-consuming subsystems, avoid the blindness of experience-based adjustments, and improve the optimization efficiency; (4) Based on the optimization range design of mean and variance, it can respond to system operation fluctuations in real time (such as seasonal changes and subsystem aging) to achieve long-term stable energy saving (e.g., automatically reduce the upper limit of the air conditioning temperature parameter domain when the temperature is high in summer, and appropriately lower the lower limit of the temperature parameter domain when the temperature is low in winter). Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0017] Figure 1 This is a schematic diagram illustrating the steps of an energy-saving strategy optimization method for existing building air conditioning and lighting systems according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 In this first embodiment, an energy-saving strategy optimization method for air conditioning and lighting systems in existing buildings is provided, taking a 10-year-old office building (8 floors above ground, total building area of ​​5000㎡) in a certain city as the implementation scenario; the air conditioning system is a centralized air conditioning system (2 subsystems per floor, 16 subsystems in total), with a cooling / heating power of 5-8kW; the lighting system is LED lighting (4 subsystems per floor, 32 subsystems in total), with a rated power of 10-15W / lamp, 30 lamps per floor.

[0020] The method includes the following steps: Step S1: Based on the environmental requirements of the existing building, set the control parameter range of indoor temperature and light intensity to form an indoor environment mode, and store the indoor environment mode data corresponding to each air conditioning and lighting subsystem. The data records the system's operating parameters. It should be noted that, starting with the building's indoor environmental requirements (comfort and functional requirements), temperature and light intensity parameter domains are set to establish a "requirement-parameter" correspondence. At the same time, the operating data of each subsystem is stored to provide basic data support for subsequent analysis and avoid parameter settings from deviating from actual requirements.

[0021] For example, based on the existing indoor environmental requirements of the building, the parameter domains of indoor temperature and light intensity are initialized to form an indoor environment mode, and the indoor temperature parameter domain is controlled by the air conditioning system, and the indoor light intensity parameter domain is controlled by the lighting system. The system stores indoor environmental model data clusters with each air conditioning and lighting subsystem as the data source. The indoor environmental model data clusters record the model operating parameters, including the cooling / heating capacity and air conditioning energy consumption of the air conditioning system, as well as the rated power and lighting energy consumption of the lighting system. One environmental demand model corresponds to one indoor environmental model data cluster. For example, according to the office comfort standard (GB / T50785-2012 "Evaluation Standard for Indoor Thermal and Humidity Environment of Civil Buildings"), the summer temperature parameter range is set to 24-26℃ and the winter temperature range to 18-22℃; the light intensity during office hours (8:00-12:00, 13:00-18:00) is 300-500 lux, and during lunch break (12:00-13:00) it is 150-200 lux; the cooling / heating capacity (5-6kW in summer) and hourly energy consumption (1.2-1.5kWh) of each air conditioning subsystem are recorded, as well as the rated power (450W / floor) and hourly energy consumption (0.3-0.4kWh) of each lighting subsystem.

[0022] Step S2: Construct a timeline for mode switching within the daily building usage cycle, determine the scale of the timeline and mode switching nodes, and mark the indoor environment mode switching segments where air conditioning and lighting operate in coordination between adjacent nodes. It should be noted that by combining the building's daily usage cycle with the construction mode switching timeline, and marking the collaborative operation segments of different time periods, energy-saving optimization is bound to actual usage scenarios (such as office, lunch break, and after get off work), solving the problem that "fixed mode" cannot adapt to dynamic needs, and achieving precise matching of "time period-parameter".

[0023] For example, a mode switching timeline is constructed, with the starting point being the daily building usage start time. The scale 't' of the mode switching timeline is initialized, and based on scale 't', mode switching nodes are sequentially labeled on the timeline. Any mode switching node labeled 'y' is denoted as... ; Based on the mode switching timeline, the switching time segment between any two adjacent mode switching nodes is obtained, and the indoor environment mode switching segment for coordinated operation of air conditioning and lighting is marked as follows: ,in, This indicates the mode switching node labeled y+1; For example, starting at 8:00 and ending at 18:00, the timeline is set to 1 hour; based on the work schedule, three nodes are determined: 8:00 (start of work), 12:00 (lunch break), 13:00 (end of get off work), and 18:00 (end of get off work), and three switching segments are marked: 8:00→12:00 (morning work), 12:00→13:00 (lunch break), and 13:00→18:00 (afternoon work).

[0024] Step S3: Using the daily usage cycle of the building as the cycle period, add mode tags to the indoor environment mode data, and at the same time, count the indoor environment mode data clusters of the air conditioning and lighting subsystems in each mode switching segment. The clusters record the running time and energy consumption of the air conditioning and lighting subsystems. It should be noted that by statistically analyzing the runtime and total energy consumption of each switching segment on a daily cycle, the system's operating status is transformed into quantifiable data, thereby providing an objective basis for energy-saving effect evaluation and avoiding the subjectivity of experience-based judgment.

[0025] For example, taking the daily usage cycle of the building as the pattern cycle, pattern tags are added to the indoor environment pattern data clusters, and based on the indoor environment pattern switching segments marked by the pattern switching time axis, the statistics are accumulated within the indoor environment pattern switching segments. The running time of the air conditioning and lighting subsystem formed within the system, as well as the energy consumption of the subsystem, which is the sum of the energy consumption of air conditioning and the energy consumption of lighting; The indoor environmental model data cluster with additional pattern tags is denoted as... , ,in, This represents the indoor environment pattern data cluster corresponding to the air conditioning and lighting subsystem numbered i. This indicates a segment where the environment mode is switched. Operating parameters for the air conditioning and lighting subsystem with internal number i. Indicates the subsystem runtime. This represents the energy consumption of the subsystem, where k is the label of the mode switching node; For example, daily statistics are compiled on the runtime of each segment (4 hours in the morning, 1 hour during lunch break, and 5 hours in the afternoon) and total energy consumption (air conditioning + lighting); "season-time period" labels are added to the data clusters (such as "summer-morning office" and "summer-lunch break").

[0026] Step S4: Construct a feature map of air conditioning-lighting coordinated operation, divide the feature map into shape regions corresponding to mode switching segments, and calculate the mode switching state value corresponding to each shape region. The shape regions include triangular regions and fan-shaped regions. It should be noted that by using a circular collaborative operation feature map, abstract energy consumption and duration data are transformed into intuitive graphic features (sector area, radius length). Combined with geometric calculations (area of ​​feature triangles), the operation status can be visualized and analyzed, thereby reducing the complexity of energy-saving analysis and saving algorithm resources.

[0027] For example, an air conditioning-lighting coordinated operation feature map is constructed. The air conditioning-lighting coordinated operation feature map is circular, with a fixed center point and a radius of S. S is the standard energy consumption of the air conditioning and lighting subsystem within the scale t range of the initial configuration. In the characteristic diagram of air conditioning-lighting coordinated operation, the circle is divided into k equal sectors with the center point as the center point. One radius of the sector corresponds to an indoor environment mode switching segment. In the characteristic diagram of coordinated operation of air conditioning and lighting corresponding to the air conditioning and lighting subsystem numbered i, the indoor environment mode switching segment is included. The corresponding sector radius is denoted as and use The mode switching state value indicates the coordinated operation behavior of air conditioning and lighting, and ,like This indicates a segment where the indoor environment mode is switched. If the interior air conditioning and lighting subsystem is not operating, then... ; For example, the radius of the circular feature map is set to 8 (corresponding to a standard energy consumption of 8 kWh / period), and it is divided into 3 equal sectors (each 120°); the status value = (total energy consumption of the segment / running time) × scale (1 hour), the status value of subsystem 1 on the east side of the 8th floor in the morning = 6.0 / 4×1=1.5, the status value during lunch break 1.15 / 1×1=1.15, and the status value during the afternoon 7.5 / 5×1=1.5.

[0028] Step S5: Based on the comparative analysis of shape regions, evaluate the energy-saving gain value of the air conditioning and lighting subsystem; It should be noted that the energy-saving gain value is quantified by using the "area of ​​the characteristic triangle / area of ​​the sector" to avoid blind adjustments.

[0029] For example, in the characteristic diagram of air conditioning-lighting coordinated operation corresponding to the air conditioning and lighting subsystem numbered i, the center of the circle is taken as the vertex, and the radii are connected by straight lines. With radius The ends of the triangle form a cooperative operation characteristic triangle, denoted as... ,in, This represents the mode switching status value corresponding to the air conditioning and lighting subsystem numbered i; In the characteristic diagram of the coordinated operation of air conditioning and lighting subsystem i, with the center of the circle as the vertex, the radius is obtained. With radius The area of ​​the sector between them is denoted as ; The energy-saving gain value of the air conditioning and lighting subsystem with evaluation number i is evaluated. In the formula, Represents the characteristic triangle of cooperative operation The area; For example, the area of ​​the characteristic triangle formed by adjacent radii (such as 1.5 in the morning and 1.15 at noon) is 0.86, the area of ​​the sector is (120 / 360) × π × 8² ≈ 67.02, and the gain value is 0.86 / 67.02 ≈ 0.0128; the mean gain value of all subsystems in summer is 0.013, and the variance is 0.002.

[0030] Step S6: Determine the energy-saving optimization range and real-time energy-saving feedback range based on all energy-saving gain values ​​to form an energy-saving optimization response range. Adjust the indoor environmental mode data of each air conditioning and lighting subsystem according to the feedback of the energy-saving optimization response range to achieve system energy-saving optimization. It should be noted that the optimization range is constructed based on the mean and variance of the gain value, and the intersection of the "global optimization requirements" and the "real-time status of the single system" is taken as the optimization target. The subsystem parameters are dynamically adjusted to ensure that the energy-saving effect is stable and meets the actual operating conditions, so as to form a closed loop of "evaluation-adjustment-re-evaluation".

[0031] For example, an energy-saving optimization range is constructed based on the energy-saving gain value. ,in, This is the average value of the energy-saving gain, and , Let be the variance of the energy-saving gain value, and ; Construct a real-time energy-saving feedback range based on the energy-saving gain value. ; Based on the energy-saving optimization range and the real-time energy-saving feedback range, an energy-saving optimization response range is generated. In the formula, max{} and min{} are the maximum and minimum value functions, respectively; Optimize response range for energy saving To achieve energy-saving optimization, when the air conditioning system and lighting system control the indoor temperature parameter domain and light intensity parameter domain respectively, the indoor environmental pattern data cluster of the air conditioning and lighting subsystem numbered i is fed back. To meet the energy-saving optimization response range; For example, the energy-saving optimization range is 0.013±0.002 (0.011-0.015), the real-time feedback range of subsystem No. 2 on the west side of the 8th floor is 0.012±0.002 (0.010-0.014), and the optimization response range is 0.011-0.014. If the air conditioning temperature parameter range during the lunch break of this subsystem is increased to 26-27℃ and the lighting light intensity parameter range is decreased to 150-180 lux, it will meet the energy-saving optimization range.

[0032] In this first embodiment: an energy-saving strategy optimization system for existing building air conditioning and lighting systems is provided. The system is communicatively connected to the existing building air conditioning and lighting systems for application in the first embodiment. The system includes: a storage unit and a processor. The storage unit stores a program for an energy-saving strategy optimization method for existing building air conditioning and lighting systems. The processor executes the program for the energy-saving strategy optimization method for existing building air conditioning and lighting systems. The processor also integrates core functional modules, including an environmental parameter and mode data management module, a time axis and switching segment processing module, an operational data statistics module, a collaborative feature analysis module, an energy-saving gain evaluation module, and an energy-saving optimization feedback module. The environmental parameter and mode data management module, and the time axis and switching segment processing module together constitute the basic data processing unit of the system. The environmental parameter and mode data management module is used to set the control parameter range of indoor temperature and light intensity according to the existing building's indoor environment requirements to form an indoor environment mode. At the same time, it stores the indoor environment mode data corresponding to each air conditioning and lighting subsystem to ensure that the data records are complete and correspond one-to-one with the subsystems. The time axis and switching segment processing module is used to construct a mode switching time axis based on the building's daily usage cycle, determine the time axis scale and mode switching nodes, and mark the air conditioning-lighting coordinated operation switching segments between adjacent nodes to achieve accurate matching between switching segments and actual usage scenarios. The system's energy-saving analysis and optimization unit comprises the operation data statistics module, the collaborative feature analysis module, the energy-saving gain evaluation module, and the energy-saving optimization feedback module. The system comprises several modules: Operational Data Statistics Module, which adds pattern markers to indoor environmental mode data based on the building's daily usage cycle, and calculates the runtime and total energy consumption of the air conditioning and lighting subsystems within each switching segment, ensuring the timeliness and accuracy of data statistics; Collaborative Feature Analysis Module, which constructs an air conditioning-lighting collaborative operation feature map for each subsystem, divides the data into sector regions corresponding to the switching segments, calculates the mode switching state values ​​for each region, and transforms abstract operational data into intuitive graphical features; Energy Saving Gain Assessment Module, which constructs collaborative operation feature triangles corresponding to adjacent state values ​​in the collaborative operation feature map, calculates the ratio of the triangle area to the corresponding sector area to obtain the energy saving gain value, and achieves a quantitative assessment of energy saving effects; and Energy Saving Optimization Feedback Module, which constructs the energy saving optimization range, the real-time energy saving feedback range, and the energy saving optimization response range (the intersection of the two) based on the mean and variance of the energy saving gain value, and provides feedback to adjust the indoor environmental mode data of the subsystems to ensure the achievement of energy saving optimization goals.

[0033] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0034] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing energy-saving strategies for air conditioning and lighting systems in existing buildings, characterized in that, The method includes the following steps: Step S1: Based on the environmental requirements of the existing building, set the control parameter range of indoor temperature and light intensity to form an indoor environment mode, and store the indoor environment mode data corresponding to each air conditioning and lighting subsystem. The data records the system's operating parameters. Step S2: Construct a timeline for mode switching within the daily building usage cycle, determine the scale of the timeline and mode switching nodes, and mark the indoor environment mode switching segments where air conditioning and lighting operate in coordination between adjacent nodes. Step S3: Using the daily usage cycle of the building as the cycle period, add mode tags to the indoor environment mode data, and at the same time, count the indoor environment mode data clusters of the air conditioning and lighting subsystems in each mode switching segment. The clusters record the running time and energy consumption of the air conditioning and lighting subsystems. Step S4: Construct a feature map of air conditioning-lighting coordinated operation, divide the feature map into shape regions corresponding to mode switching segments, and calculate the mode switching state value corresponding to each shape region. The shape regions include triangular regions and fan-shaped regions. Step S5: Based on the comparative analysis of shape regions, evaluate the energy-saving gain value of the air conditioning and lighting subsystem; Step S6: Determine the energy-saving optimization range and real-time energy-saving feedback range based on all energy-saving gain values ​​to form an energy-saving optimization response range. Adjust the indoor environmental mode data of each air conditioning and lighting subsystem according to the feedback of the energy-saving optimization response range to achieve system energy-saving optimization.

2. The energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 1, characterized in that, The specific implementation process of step S1 includes: Based on the existing indoor environmental requirements of the building, the parameter domains of indoor temperature and light intensity are initialized to form an indoor environment mode. The indoor temperature parameter domain is controlled by the air conditioning system, and the indoor light intensity parameter domain is controlled by the lighting system. The system stores indoor environment mode data clusters with each air conditioning and lighting subsystem as the data source. The indoor environment mode data clusters record mode operation parameters, including the cooling / heating capacity and air conditioning energy consumption of the air conditioning system, and the rated power and lighting energy consumption of the lighting system. Each environmental demand mode corresponds to one indoor environment mode data cluster.

3. The energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 2, characterized in that, The specific implementation process of step S2 includes: A mode switching timeline is constructed, starting from the daily building usage start time. The scale 't' of the timeline is initialized, and based on scale 't', mode switching nodes are sequentially labeled on the timeline. Any mode switching node labeled 'y' is denoted as... ; Based on the mode switching time axis, the switching time segment between any two adjacent mode switching nodes is obtained, and the indoor environment mode switching segment for coordinated operation of air conditioning and lighting is marked as follows: ,in, This indicates the mode switching node with the label y+1.

4. The energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 3, characterized in that, The specific implementation process of step S3 includes: Using the daily usage cycle of the building as the pattern cycle, pattern tags are added to the indoor environment pattern data clusters, and based on the indoor environment pattern switching segments marked by the pattern switching time axis, the data are accumulated and statistically analyzed within the indoor environment pattern switching segments. The running time of the air conditioning and lighting subsystem formed within the system, and the energy consumption of the subsystem, wherein the energy consumption of the subsystem is the sum of the energy consumption of air conditioning and the energy consumption of lighting; The indoor environmental model data cluster with additional pattern tags is denoted as... , ,in, This represents the indoor environment pattern data cluster corresponding to the air conditioning and lighting subsystem numbered i. This indicates a segment where the environment mode is switched. Operating parameters for the air conditioning and lighting subsystem with internal number i. Indicates the subsystem runtime. This represents the energy consumption of the subsystem, and k is the label of the mode switching node.

5. The energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 4, characterized in that, The specific implementation process of step S4 includes: Construct a characteristic map of air conditioning-lighting coordinated operation. The characteristic map of air conditioning-lighting coordinated operation is circular, with a fixed center point and a radius of S. S is the standard energy consumption of the air conditioning and lighting subsystem within the scale t range of the initial configuration. In the air conditioning-lighting coordinated operation feature diagram, the circle is divided into k equal sectors with the center point as the center point, and one radius of the sector corresponds to an indoor environment mode switching segment. In the characteristic diagram of coordinated operation of air conditioning and lighting corresponding to the air conditioning and lighting subsystem numbered i, the indoor environment mode switching segment is included. The corresponding sector radius is denoted as and use The mode switching state value indicates the coordinated operation behavior of air conditioning and lighting, and ,like This indicates a segment where the indoor environment mode is switched. If the interior air conditioning and lighting subsystem is not operating, then... .

6. The energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 5, characterized in that, The specific implementation process of step S5 includes: In the characteristic diagram of the coordinated operation of air conditioning and lighting subsystem i, with the center of the circle as the vertex and the radii connected by straight lines, With radius The ends of the triangle form a cooperative operation characteristic triangle, denoted as... ,in, This represents the mode switching status value corresponding to the air conditioning and lighting subsystem numbered i; In the characteristic diagram of the coordinated operation of air conditioning and lighting subsystem i, with the center of the circle as the vertex, the radius is obtained. With radius The area of ​​the sector between them is denoted as ; The energy-saving gain value of the air conditioning and lighting subsystem with evaluation number i is evaluated. In the formula, Represents the characteristic triangle of cooperative operation The area.

7. The energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 6, characterized in that, The specific implementation process of step S6 includes: Based on the energy-saving gain value, construct the energy-saving optimization range. ,in, This is the average value of the energy-saving gain, and , Let be the variance of the energy-saving gain value, and ; Construct a real-time energy-saving feedback range based on the energy-saving gain value. ; Based on the energy-saving optimization range and the real-time energy-saving feedback range, an energy-saving optimization response range is generated. In the formula, max{} and min{} are the maximum and minimum value functions, respectively; Optimize response range for energy saving To achieve energy-saving optimization, when the air conditioning system and lighting system control the indoor temperature parameter domain and light intensity parameter domain respectively, the indoor environmental pattern data cluster of the air conditioning and lighting subsystem numbered i is fed back. To meet the energy-saving optimization response range.

8. The energy-saving strategy optimization system for an energy-saving strategy optimization method for existing building air conditioning and lighting systems according to claim 1, characterized in that, The energy-saving strategy optimization method for existing building air conditioning and lighting systems is integrated into a storage device in the form of a program. The storage device is built into the energy-saving strategy optimization system, which also includes a processor for executing the program of the energy-saving strategy optimization method for existing building air conditioning and lighting systems. The processor also integrates core functional modules, including an environmental parameter and mode data management module, a time axis and switching segment processing module, an operation data statistics module, a collaborative feature analysis module, an energy-saving gain evaluation module, and an energy-saving optimization feedback module. The environmental parameter and mode data management module, the time axis and switching segment processing module together constitute the basic data processing unit of the system. The environmental parameter and mode data management module is used to set the control parameter range of indoor temperature and light intensity according to the existing building's indoor environment requirements to form an indoor environment mode. At the same time, it stores the indoor environment mode data corresponding to each air conditioning and lighting subsystem to ensure that the data records are complete and correspond one-to-one with the subsystems. The time axis and switching segment processing module is used to construct a mode switching time axis based on the building's daily usage cycle, determine the time axis scale and mode switching nodes, and mark the air conditioning-lighting coordinated operation switching segments between adjacent nodes to achieve accurate matching between switching segments and actual usage scenarios.

9. The energy-saving strategy optimization system according to claim 8, characterized in that, The operation data statistics module, collaborative feature analysis module, energy-saving gain evaluation module, and energy-saving optimization feedback module together constitute the energy-saving analysis and optimization unit of the system. The system comprises several modules: Operational Data Statistics Module, which adds pattern markers to indoor environmental mode data based on the building's daily usage cycle, and calculates the runtime and total energy consumption of the air conditioning and lighting subsystems within each switching segment, ensuring the timeliness and accuracy of data statistics; Collaborative Feature Analysis Module, which constructs an air conditioning-lighting collaborative operation feature map for each subsystem, divides the data into sector regions corresponding to the switching segments, calculates the mode switching state values ​​for each region, and transforms abstract operational data into intuitive graphical features; Energy Saving Gain Assessment Module, which constructs collaborative operation feature triangles corresponding to adjacent state values ​​in the collaborative operation feature map, calculates the ratio of the triangle area to the corresponding sector area to obtain the energy saving gain value, and achieves a quantitative assessment of energy saving effects; and Energy Saving Optimization Feedback Module, which constructs the energy saving optimization range, the real-time energy saving feedback range, and the energy saving optimization response range (the intersection of the two) based on the mean and variance of the energy saving gain value, and provides feedback to adjust the indoor environmental mode data of the subsystems to ensure the achievement of energy saving optimization goals.

10. The energy-saving strategy optimization system according to claim 8, characterized in that, The energy-saving strategy optimization system is communicatively connected to the existing building's air conditioning and lighting systems to implement the energy-saving strategy optimization method for existing building air conditioning and lighting systems as described in any one of claims 1-7.

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