Control method and device of building cold and heat source system self-adaptive to load requirements

Abstract

The invention discloses a control method and device of a building cold and heat source system self-adaptive to load requirements. The control method comprises the following steps: acquiring historical operation data, physical characteristics and equipment system data of a building, and establishing a building load prediction model; generating the hourly load curve, and predicting the cooling or heating demand of each time period in the future according to the curve; dynamically adjusting the refrigerating or heating load demand, and generating a corresponding cooling or heating curve; and constructing a machine learning algorithm based on an equipment mechanism model, optimizing a control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting control parameters of the equipment. According to the technical scheme, the load of the building cold and heat source system is intelligently predicted and adjusted, the energy efficiency of the system is improved, invalid energy consumption is reduced, energy utilization is optimized, control parameter optimization is achieved through a machine learning algorithm, efficient operation is ensured, indoor comfort is kept, and the purpose of saving energy is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to a control method and device for a building cold and heat source system with adaptive load demand. Background Art

[0002] Central air conditioning systems with centralized cold and heat sources are usually equipped with multiple chillers or heat pump units to adapt to load requirements under different working conditions. In the prior art, the regulation of the system mainly relies on feedback control. Although it is more effective in terminal equipment control, its control effect is not ideal for large-capacity and lagging systems such as chilled water. In the initial stage of chiller startup, there is a lack of optimal combination of units to meet the actual load requirements of the building, which increases the system adjustment time and frequency, and the operating energy consumption required to provide the same amount of cold and heat is higher.

[0003] In actual operation, load demand changes dynamically, and it is difficult to adjust in real time according to load changes, resulting in a mismatch between heat supply and actual demand. In order to ensure that the temperature in the building meets the preset requirements, oversupply is usually carried out, which increases the energy consumption and operating costs of the building.

[0004] How to properly solve the above problems has become an urgent issue to be solved in the industry. Summary of the invention

[0005] The present invention provides a control method, device, equipment and storage medium for a building cold and heat source system with adaptive load demand, which is used to intelligently predict and adjust the load of the building cold and heat source system, thereby improving the system energy efficiency, reducing ineffective energy consumption, optimizing energy utilization, and optimizing control parameters through a machine learning algorithm to ensure efficient operation, maintain indoor comfort, and achieve energy saving.

[0006] According to a first aspect of the present invention, a control method for a building cold and heat source system with adaptive load demand is provided, and the control method for a building cold and heat source system with adaptive load demand comprises:

[0007] Acquire historical operation data, physical characteristics and equipment system data of the building, and establish the building load prediction model;

[0008] Based on the historical operation data and the building load prediction model, the hourly load demand of the building is calculated, the hourly load curve is generated, and the cooling or heating demand in each future period is predicted according to the curve;

[0009] Acquire real-time meteorological data and personnel flow data, dynamically adjust cooling or heating load demand according to the hourly load forecast results, and generate corresponding cooling or heating curves;

[0010] Construct a machine learning algorithm based on the equipment mechanism model, optimize the control strategy of the cold and hot source system based on the historical operation data of the equipment, and output the control parameters of the equipment, including any one or more of the start and stop time, number of running units, outlet water temperature setting value and water pump frequency.

[0011] In one embodiment, establishing a building load prediction model includes:

[0012] Based on the historical operation data and physical characteristics of the building, the data collected by at least one sensor is cleaned and repaired to eliminate outliers and missing data;

[0013] The data cleaning and repair process removes abnormal data points, fills in missing data, and smoothes data.

[0014] In one embodiment, the generation of the hourly load curve includes:

[0015] In combination with the physical characteristics of the building, real-time meteorological data and personnel flow data, the hourly cooling load or heating load demand is calculated through a building load prediction model to generate the hourly load curve;

[0016] The hourly load curve is generated according to the heat transfer performance of the building envelope, the fresh air load, and the impact of indoor personnel activities on the thermal environment.

[0017] In one embodiment, the dynamically adjusting cooling or heating load demand includes:

[0018] By acquiring real-time meteorological data and personnel flow data within the building, the hourly load forecast results can be dynamically revised;

[0019] According to the revised load forecast results, adjust the cooling or heating curve of the cold and heat source system;

[0020] During the correction process, the immediate impact of meteorological data and personnel movement on cooling and heating loads is considered.

[0021] In one embodiment, the construction of the machine learning algorithm includes:

[0022] Based on the equipment mechanism model and the historical operation data of the equipment, the refrigeration capacity curve and energy efficiency curve of the cold and heat source system are established. The machine learning model is trained through historical data to optimize the control strategy of the equipment and select the optimal operating parameters.

[0023] Analyze equipment operating conditions based on historical operating data and extract characteristic parameters.

[0024] In one embodiment, the output device control parameters include:

[0025] According to the optimized control strategy, determine any one or more of the equipment start and stop time, number of running units, outlet water temperature setting value and water pump frequency;

[0026] According to the preset energy efficiency ratio, operation stability and indoor environmental comfort, the control parameters are determined based on the multi-objective optimization strategy.

[0027] Installation rights 1.

[0028] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0029] It is characterized in that the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned control methods for the building cold and heat source system with adaptive load demand is implemented.

[0030] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a computer (e.g., a processor in the computer), implements any of the above-mentioned methods for controlling a building cold and heat source system with adaptive load requirements.

[0031] In summary, the present invention provides a control method and device for a building cold and heat source system with adaptive load demand, the method comprising: obtaining the historical operation data, physical characteristics and equipment system data of the building, and establishing the building load prediction model; based on the historical operation data and the building load prediction model, calculating the hourly load demand of the building, generating the hourly load curve, and predicting the cooling or heating demand in each future period according to the curve; obtaining real-time meteorological data and personnel flow data, dynamically adjusting the cooling or heating load demand according to the hourly load prediction results, and generating corresponding cooling or heating curves; constructing a machine learning algorithm based on the equipment mechanism model, optimizing the control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting the control parameters of the equipment, including any one or more of the start and stop time, the number of running units, the water outlet temperature setting value and the water pump frequency. The technical solution of the present application achieves a significant improvement in the energy efficiency of the system by intelligently predicting and dynamically adjusting the load demand of the building cold and heat source system. By combining historical data with real-time data, it is possible to more accurately predict the cold and heat load demand, thereby optimizing energy distribution and reducing ineffective energy consumption. At the same time, the machine learning algorithm based on the equipment mechanism model realizes the optimization of equipment control parameters, enabling the system to adaptively adjust, improve the system automation and intelligence level, reduce operating costs, and ensure the comfort of the indoor environment under various environmental conditions, thereby achieving the goal of energy conservation and emission reduction.

[0032] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0033] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A flow chart of a control method for a building cold and heat source system that is adaptive to load demand provided by an embodiment of the present invention;

[0036] Figure 2 A flow chart of establishing a building load prediction model provided by an embodiment of the present invention;

[0037] Figure 3 A flow chart of a machine learning algorithm based on a device mechanism model provided for an embodiment of the present invention;

[0038] Figure 4 A flow chart of a control strategy output adapted to working condition requirements provided by an embodiment of the present invention;

[0039] Figure 5 A flowchart of an iterative optimization method for a system after executing an optimization strategy provided by an embodiment of the present invention;

[0040] Figure 6 A structural diagram of a control device for a building cold and heat source system that is adaptive to load demand provided by an embodiment of the present invention;

[0041] Figure 7 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0044] like Figure 1 As shown, the present invention provides a control method for a building cold and heat source system with adaptive load demand, and the control method for a building cold and heat source system with adaptive load demand includes:

[0045] In step S11, historical operation data, physical characteristics and equipment system data of the building are obtained to establish the building load prediction model;

[0046] In step S12, based on the historical operation data and the building load prediction model, the hourly load demand of the building is calculated, the hourly load curve is generated, and the cooling or heating demand in each future period is predicted according to the curve;

[0047] In step S13, real-time meteorological data and personnel flow data are obtained, and according to the hourly load forecast results, the cooling or heating load demand is dynamically adjusted to generate a corresponding cooling or heating curve;

[0048] In step S14, a machine learning algorithm based on the equipment mechanism model is constructed, and the control strategy of the cold and hot source system is optimized based on the historical operation data of the equipment, and the control parameters of the equipment are output, including any one or more of the start and stop time, the number of operating units, the outlet water temperature setting value and the water pump frequency.

[0049] In one embodiment, it is necessary to obtain the historical operation data, physical characteristics and equipment system data of the building. The historical operation data includes the temperature, humidity and energy consumption records inside and outside the building. The physical characteristics involve the size, area division, enclosure structure characteristics of the building, etc. The equipment system data includes the type, specification, efficiency, etc. of the equipment. The building load prediction model established based on these data provides a basis for predicting the cooling and heating demand of the building under different conditions. The model can combine historical data, comprehensively consider the structure and usage pattern of the building, and accurately predict the demand change trend of the building in the future. After the load prediction model is established, the hourly load demand is calculated based on this model and the historical operation data of the building, so as to generate an hourly load curve. The hourly load curve can reflect the fluctuation of the cooling and heating load demand of the building in each hour in detail. Through the hourly load curve, the cooling or heating demand in each future period can be predicted, so as to make corresponding energy supply preparations in advance. The historical data is converted into actual hourly demand so as to plan the energy supply more accurately.

[0050] Since the actual situation may deviate from the historical data, it is necessary to obtain real-time meteorological data and personnel flow data. By integrating real-time meteorological data (such as current temperature and humidity, weather forecast) and changes in personnel density, combined with hourly load forecast results, the system can dynamically adjust the cooling or heating load demand. Dynamic load regulation is achieved to ensure that the cooling or heating curve can always meet actual needs.

[0051] Based on the previous hourly load curve, real-time load adjustment results, and historical operation data of the equipment, the system constructs a machine learning algorithm based on the equipment mechanism model. The above algorithm can optimize the control strategy of the cold and hot source system to improve the efficiency and stability of the system. Here, the machine learning algorithm continuously iterates and optimizes the control strategy based on the historical data of the equipment, and outputs the control parameters of the equipment, including the start and stop time of the equipment, the number of running units, the set value of the outlet water temperature, and the frequency of the water pump. These control parameters are the final specific response to the building's cooling and heating needs. They can directly act on the system equipment to achieve intelligent and efficient cold and heat source control.

[0052] The technical solution in this embodiment achieves a significant improvement in system energy efficiency by intelligently predicting and dynamically adjusting the load demand of the building's cold and heat source system. By combining historical data with real-time data, it is possible to more accurately predict the demand for cold and heat loads, thereby optimizing energy distribution and reducing ineffective energy consumption. At the same time, the machine learning algorithm based on the equipment mechanism model optimizes the equipment control parameters, allowing the system to adjust adaptively, improve the system's automation and intelligence level, reduce operating costs, and ensure that the indoor environment is comfortable under various environmental conditions, thereby achieving the goal of energy conservation and emission reduction.

[0053] In one embodiment, Figure 2 As shown, the following steps S21-S24 are also included:

[0054] In step S21, multi-dimensional historical data is collected and cleaned;

[0055] In step S22, detailed modeling of building physical characteristics;

[0056] In step S23, the load curve is generated and corrected in multiple periods;

[0057] In step S24, dynamic feedback and real-time load forecasting are adjusted.

[0058] In one embodiment, a large amount of historical data is collected to ensure the accuracy of load forecasting. The data sources include power consumption records of cold and hot source systems, operation records of cold and hot source equipment, indoor and outdoor temperature and humidity data, and daily average traffic data of buildings. The collection frequency and data granularity can meet the training requirements of the prediction model.

[0059] The flow meter and temperature sensor of the chilled water system main pipe are configured to record the two-dimensional curve of cooling capacity supply hour by hour. During the data processing, a continuous historical data set is generated by removing abnormal data points and filling missing values. This cleaning process provides accurate and reliable input data for load forecasting.

[0060] In order to accurately predict the load demands of different areas and types, this embodiment models the physical characteristics of the building in detail, including the building area, the division of each area, the material and characteristics of the enclosure structure, etc.

[0061] Load types are subdivided into: fresh air load, radiation load, heat transfer load, heat dissipation load of personnel and equipment, building heat storage load, water capacity load, etc. Fresh air load is determined according to the building use and air exchange rate, and radiation load is estimated based on the solar radiation heat gain coefficient under different seasons and weather conditions.

[0062] The system corrects the load forecast by calculating the difference between the historical cooling supply curve Q and the load demand curve L. The generated hourly load curve is dynamically adjusted according to conditions such as meteorological data, passenger flow and usage demand.

[0063] In the initial stage, the system automatically adjusts the load forecast according to the deviation between the cooling capacity supply curve and the actual load. For example, when the deviation between the load curve and the cooling capacity supply curve exceeds 10%, the system will calculate the correction value Qi of the load in the area by comparing the return air temperature with the set value according to the following formula:

[0064] Q'i=(Q×ΔT) / A

[0065] The final cooling supply curve Qt is generated as the time-based cooling demand of the building, covering the influence of factors such as weather, traffic, and building work calendar.

[0066] In the case of large changes in load demand (such as weather changes, fluctuations in population density, etc.), the system will dynamically adjust the load forecast based on real-time weather data and building usage. Real-time weather information obtained through the weather data interface can update the load forecast results at any time, making the forecast model more consistent with the actual operating conditions.

[0067] Load forecasts are generated through historical data and physical characteristic modeling, and forecast accuracy is improved through dynamic feedback, ensuring the stability of heating and cooling supply and energy-saving effects, providing a reliable basis for regulating the building environment.

[0068] In one embodiment, Figure 3 As shown, the following steps S31-S34 are also included:

[0069] In step S31, the system collects equipment performance data and generates curves;

[0070] In step S32, a multi-operating-condition equipment operation rule base is established;

[0071] In step S33, the equipment operating parameters are dynamically set;

[0072] In step S34, dynamic intelligent regulation and working condition switching are performed.

[0073] In one embodiment, a knowledge graph is constructed through equipment performance analysis and data learning to support equipment combinations and operation strategies under different working conditions and ensure efficient operation and intelligent adjustment of the system.

[0074] The system collects historical operation data from cold and heat source equipment (such as chillers, heat pumps, refrigeration pumps, cooling water systems, etc.). These data include the cooling capacity of the equipment at different outlet water temperatures, load rate data, cooling water system data, equipment COP energy efficiency curves, and water pump power and flow curves.

[0075] According to the characteristics of different seasons, the performance parameters of each device are associated with the ambient temperature and humidity data to generate the cooling capacity curve under different water outlet temperature, cooling load and cooling water temperature. For example, when the total load demand of the system is Qt i, the system will select the most energy-efficient equipment combination according to the COP curve.

[0076] The knowledge graph of the equipment system is based on physical associations and operating rules, and performs multi-dimensional rule configuration for equipment such as water pumps, cooling towers, and chillers to ensure the optimal equipment combination under different loads.

[0077] The system establishes three main working modes, namely standard working conditions, comfortable working conditions and energy-saving working conditions. Under each working condition, the system will match the appropriate equipment combination and operating parameters. For example, under comfortable working conditions, the system will give priority to combinations with sufficient cooling supply, while under energy-saving conditions, it will give priority to matching equipment combinations with higher energy efficiency.

[0078] Taking the water pump as an example, the system calculates the flow rate based on the load demand and the supply and return water temperature difference set by the working condition to meet the selection of water pump combinations under different cooling requirements. The calculation formula is as follows:

[0079]

[0080] Where Qt i is the target cooling demand, and the supply and return water temperature difference is set to 5°C. The system matches the best pump combination and its operating frequency by analyzing the historical pump flow curve.

[0081] The cooling tower is selected based on the wet bulb temperature plus 3°C as the water outlet temperature setting, and is adjusted according to the change in wet bulb temperature to ensure that the cooling tower meets the cooling needs with the lowest energy consumption.

[0082] The system selects the most suitable operation combination according to the current load demand and environmental conditions of the building. When the environment changes or the load demand changes, the system will give priority to the equipment combination that meets the conditions from the equipment knowledge map to achieve energy saving and high efficiency.

[0083] This embodiment generates a rule base by combining multi-mode and multi-condition equipment, thereby realizing intelligent adjustment of the building's cold and heat source systems and providing optimal solutions for different environments and load requirements.

[0084] In one embodiment, Figure 4 As shown, the following steps S41-S44 are also included:

[0085] In step S41, the load forecast is matched with the equipment rule base;

[0086] In step S42, strategy generation and parameter setting;

[0087] In step S43, real-time strategy adjustment and optimization;

[0088] In step S44, dynamic intelligent regulation and working condition switching are performed.

[0089] In one embodiment, the system scores multiple indicators of equipment operation based on load forecast results and actual building needs to generate a control strategy. The scoring indicators include host energy efficiency, system power consumption, switching frequency, cooling margin, etc.

[0090] The control strategy generation is based on the following formula for comprehensive scoring:

[0091] S=ξ1·A+ξ2·B+ξ3·C+ξ4·D

[0092] Among them, A is the host energy efficiency value, B is the system power consumption (kWh), C is the number of switching times, and D is the cooling supply margin (kWh). Each weight coefficient is set according to the operating mode.

[0093] The system outputs operating parameters according to the scoring weights, including the host start and stop time, water outlet temperature, chiller pump frequency, etc., and updates the strategy when the load forecast changes. For example, in comfort mode, the system may increase the weight of the cooling capacity surplus to ensure sufficient cooling supply.

[0094] During the strategy application, the system monitors the difference between actual load and predicted load. When the actual load deviation exceeds 10%, the system recalculates and adjusts the strategy to ensure that the control strategy matches the real-time demand of the building.

[0095] The system records and stores the strategies of each operation cycle in the database. As time goes by, the strategy library is gradually enriched. In the future, when the system generates new strategies, it can refer to historical data and give priority to the strategy combination that meets current needs and has the best historical effect.

[0096] Through this embodiment, the control strategy can achieve self-optimization and efficient operation under dynamic demand, ensuring the stability of the building's cold and heat source supply and the rational use of energy.

[0097] In one embodiment, Figure 5 As shown, the following steps S51-S54 are also included:

[0098] In step S51, the multi-dimensional operation effect is evaluated;

[0099] In step S52, the feedback mechanism and rule base are updated;

[0100] In step S53, dynamic self-learning and optimization adjustment;

[0101] In step S54, the feedback strategy is cyclically updated.

[0102] In one embodiment, the system evaluates the effect of strategy execution after the operation cycle ends. The evaluation dimensions include: load accuracy, environmental comfort, energy saving rate, and ease of operation. The load accuracy requires a deviation of ≤5%, the environmental comfort is full score at ±1°C in temperature and humidity, the energy saving rate is scored according to the actual energy saving percentage, and the ease of operation is based on the number of host switching. Strategies with excellent evaluation results will enter the historical rule base and form a reference target for subsequent operation. If the strategy effect does not meet expectations in actual operation, the system will refer to historical strategies with higher scores and give priority to them in the next round of optimization. As the running time increases, the system adds high-quality solutions in historical strategies to the target set and realizes self-adjustment of the rule base by learning new control parameters. For example, when the system recognizes that a certain strategy is effective under current conditions, the strategy will be recorded and applied preferentially to subsequent cycles under similar conditions. The evaluation and feedback of each operation cycle of the system form an update cycle of the strategy base, and ultimately realize self-learning and optimization of the strategy base. Whether the original strategy or the updated strategy is adopted, the system will achieve continuous strategy optimization through feedback. Through the feedback mechanism and historical data accumulation, this embodiment enables the control strategy to have the ability of self-learning and continuous optimization, providing a guarantee for the intelligent operation of the building's cold and heat source system.

[0103] In one embodiment, Figure 6 FIG. 1 is a block diagram of a control device for a building cold and heat source system that is adaptive to load demand according to an exemplary embodiment. Figure 6 As shown, the control device of the building cold and heat source system with adaptive load demand includes an establishment module 61, a calculation module 62, a generation module 63 and an output module 64.

[0104] The building module 61 is used to obtain the historical operation data, physical characteristics and equipment system data of the building, and establish the building load prediction model to provide an accurate estimate of future load demand;

[0105] The calculation module 62 is used to calculate the hourly load demand of the building based on the historical operation data and the building load prediction model, generate the hourly load curve, and predict the cooling or heating demand in each future period according to the curve;

[0106] The generation module 63 is used to obtain real-time meteorological data and personnel flow data, dynamically adjust the cooling or heating load demand according to the hourly load forecast result, and generate a corresponding cooling or heating curve;

[0107] The output module 64 is used to construct a machine learning algorithm based on the equipment mechanism model, optimize the control strategy of the cold and hot source system based on the historical operation data of the equipment, and output the control parameters of the equipment, including any one or more of the start and stop time, the number of operating units, the water outlet temperature setting value and the water pump frequency.

[0108] The establishment module 61, calculation module 62, generation module 63 and output module 64 included in the control device block diagram of the building cold and heat source system with adaptive load demand are controlled to execute the control method of the building cold and heat source system with adaptive load demand described in any of the above embodiments.

[0109] like Figure 7 As shown, the present invention provides an electronic device 700, which includes: a communication interface, a processor 701, and a memory 702;

[0110] Among them, the memory 702 is used to store program instructions. When the program instructions are executed by the processor 701 that is communicatively connected to the memory 702 through the communication interface, the historical operation data, physical characteristics and equipment system data of the building are obtained to establish the building load prediction model; based on the historical operation data and the building load prediction model, the hourly load demand of the building is calculated, the hourly load curve is generated, and the cooling or heating demand in each future time period is predicted based on the curve; real-time meteorological data and personnel flow data are obtained, and according to the hourly load prediction results, the cooling or heating load demand is dynamically adjusted to generate the corresponding cooling or heating curve; a machine learning algorithm based on the equipment mechanism model is constructed to optimize the control strategy of the cold and heat source system based on the historical operation data of the equipment, and the control parameters of the equipment are output, including any one or more of the start and stop time, the number of operating units, the outlet water temperature setting value and the water pump frequency.

[0111] The present invention provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the historical operation data, physical characteristics and equipment system data of a building are obtained, and the building load prediction model is established; based on the historical operation data and the building load prediction model, the hourly load demand of the building is calculated, the hourly load curve is generated, and the cooling or heating demand in each future time period is predicted according to the curve; real-time meteorological data and personnel flow data are obtained, and according to the hourly load prediction results, the cooling or heating load demand is dynamically adjusted to generate a corresponding cooling or heating curve; a machine learning algorithm based on an equipment mechanism model is constructed, and the control strategy of the cold and heat source system is optimized based on the historical operation data of the equipment, and the control parameters of the equipment are output, including any one or more of the start and stop time, the number of operating units, the outlet water temperature setting value and the water pump frequency.

[0112] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0113] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0114] In one embodiment, a computer device is provided, which includes a memory and a processor, and a computer instruction executable by the processor is stored on the memory. When the computer instruction is executed by the processor, it instructs the processor to execute each step of the method of the embodiment of the present invention. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The computer program executes the method of the present invention when it is executed by the processor.

[0115] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0116] It can be understood by a person skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the computer program causes the present invention to be executed when it is executed. Depending on the circumstances, any reference to memory, storage, database or other media in this article may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0117] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a building cold and heat source system with adaptive load demand, characterized in that: The method comprises: Acquire historical operation data, physical characteristics and equipment system data of the building, and establish the building load prediction model; Based on the historical operation data and the building load prediction model, the hourly load demand of the building is calculated, the hourly load curve is generated, and the cooling or heating demand in each future period is predicted according to the curve; Acquire real-time meteorological data and personnel flow data, dynamically adjust cooling or heating load demand according to the hourly load forecast results, and generate corresponding cooling or heating curves; Construct a machine learning algorithm based on the equipment mechanism model, optimize the control strategy of the cold and hot source system based on the historical operation data of the equipment, and output the control parameters of the equipment, including any one or more of the start and stop time, number of running units, outlet water temperature setting value and water pump frequency.

2. The control method of the building cold and heat source system according to claim 1 is characterized in that: The building load prediction model is established including: Based on the historical operation data and physical characteristics of the building, the data collected by at least one sensor is cleaned and repaired to eliminate outliers and missing data; The data cleaning and repair process involves removing outlier data points, filling missing data, and data smoothing.

3. The control method of the building cold and heat source system according to claim 1 is characterized in that: The generation of the hourly load curve includes: In combination with the physical characteristics of the building, real-time meteorological data and personnel flow data, the hourly cooling load or heating load demand is calculated through a building load prediction model to generate the hourly load curve; The hourly load curve is generated according to the heat transfer performance of the building envelope, the fresh air load, and the impact of indoor personnel activities on the thermal environment.

4. The control method of the building cold and heat source system according to claim 1 is characterized in that: The dynamic adjustment of cooling or heating load demand includes: By acquiring real-time meteorological data and personnel flow data within the building, the hourly load forecast results can be dynamically revised; According to the revised load forecast results, adjust the cooling or heating curve of the cold and heat source system; During the correction process, the immediate impact of meteorological data and personnel movement on cooling and heating loads is considered.

5. The control method of the building cold and heat source system according to claim 1 is characterized in that: The construction of the machine learning algorithm includes: Based on the equipment mechanism model and the historical operation data of the equipment, the refrigeration capacity curve and energy efficiency curve of the cold and heat source system are established. The machine learning model is trained through historical data to optimize the control strategy of the equipment and select the optimal operating parameters. Analyze equipment operating conditions based on historical operating data and extract characteristic parameters.

6. The control method of the building cold and heat source system according to claim 1 is characterized in that: The output device control parameters include: According to the optimized control strategy, determine any one or more of the equipment start and stop time, number of running units, outlet water temperature setting value and water pump frequency; According to the preset energy efficiency ratio, operation stability and indoor environmental comfort, the control parameters are determined based on the multi-objective optimization strategy.

7. A control device for a building cold and heat source system that is adaptive to load demand, characterized in that: include: Establishing a module for obtaining historical operation data, physical characteristics and equipment system data of the building and establishing a load forecasting model for the building to provide an accurate estimate of future load demand; A calculation module, for calculating the hourly load demand of the building based on the historical operation data and the building load prediction model, generating the hourly load curve, and predicting the cooling or heating demand in each future period according to the curve; A generation module, used to obtain real-time meteorological data and personnel flow data, dynamically adjust the cooling or heating load demand according to the hourly load forecast results, and generate corresponding cooling or heating curves; The output module is used to construct a machine learning algorithm based on the equipment mechanism model, optimize the control strategy of the cold and hot source system based on the historical operation data of the equipment, and output the control parameters of the equipment, including any one or more of the start and stop time, the number of running units, the set value of the outlet water temperature and the water pump frequency.

8. The control device for a building cold and heat source system according to claim 7, characterized in that: The establishment module, the calculation module, the generation module and the output module are controlled to execute the control method of the building cold and heat source system with adaptive load demand as described in any one of claims 1-6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the control method of the building cold and heat source system with adaptive load demand as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the control method of the building cold and heat source system with adaptive load demand as described in any one of claims 1 to 6.

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