Smart energy control system

Abstract

The embodiment of the invention provides a smart energy control system, and the system comprises an energy equipment layer which is used for the cooling and / or heating of the system, and is used for collecting basic data; the energy internet of things layer collects temperature, pressure, equipment load and efficiency intervals through a data collector to collect various operation data; the energy internet of things layer transmits data to the energy big data layer for storage and primary processing; the artificial intelligence layer comprises a meteorological prediction model, a cold and heat source optimization model and a fluid system energy efficiency model; and the green operation management layer is used for automatically controlling cooling and / or heating of the system. By establishing an outdoor meteorological model, a load response prediction model, a cold and heat source energy efficiency model and a fluid system energy efficiency model and combining an optimization algorithm, efficient adjustment and energy-saving operation of an energy system are achieved, cold and heat load changes can be quickly responded, energy supply and actual load requirements are highly matched, and the energy-saving effect is achieved.

Description

Technical Field

[0001] This specification relates to the technical field of building energy optimization, and particularly to a smart energy control system. Background Art

[0002] According to statistics, the energy consumption of the air conditioning system in public buildings accounts for about 50% of the total building energy consumption. The air conditioning system consists of a refrigeration system and an end heat exchange system (i.e., indoor air conditioning heat exchange equipment), and the energy consumption of the refrigeration machine room accounts for about 80% of the total energy consumption of the air conditioner. Improving the operating efficiency of the refrigeration machine room and reducing energy consumption are important links in reducing the energy consumption of buildings.

[0003] Currently, typical automatic control systems and high-efficiency machine room systems (BAS) monitor the temperature, pressure, and water flow rate of the cooling system to adjust the reasonable matching operation of refrigeration units, cooling towers, and chilled / cooling water pumps, improving the operating efficiency (COP) of the machine room.

[0004] With the support of the automatic control system, theoretically, the operating efficiency COP of the machine room energy system can reach above 6.0. After investigation, although the actual public building refrigeration system is equipped with an automatic control system (BAS system) or adopts a high-efficiency machine room system, the efficiency (COP) of the refrigeration machine room is still between 4.4 and 5.0. Summary of the Invention

[0005] In view of this, the embodiments of this specification provide a smart energy control system. By establishing an outdoor meteorological model, a load response prediction model, a cold and heat source energy efficiency model, and a fluid system energy efficiency model, and combining optimization algorithms, the efficient regulation and energy-saving operation of the energy system are realized. The energy system can quickly respond to changes in cooling and heating loads, achieving a high degree of matching between energy supply and actual load demand, thereby achieving higher energy-saving effects.

[0006] The embodiments of this specification provide the following technical solutions: A smart energy control system includes: Energy equipment layer: used for cooling and / or heating of the system and collecting basic data; Energy Internet of Things layer: collecting various operation data such as temperature, pressure, equipment load, and efficiency range through data collectors; Energy big data layer: The Energy Internet of Things layer transmits the data to the Energy big data layer for storage and preliminary processing; Artificial Intelligence Layer: It includes a meteorological prediction model, a cold and heat source optimization model, and a fluid system energy efficiency model. The meteorological prediction model is used to anticipate load changes in advance and enhance the system's adjustment ability. The cold and heat source optimization model establishes a data analysis model based on the performance curve, efficiency curve, and operation data of the refrigeration unit to find the high-efficiency operation range of the refrigeration unit under various operating conditions. The fluid system energy efficiency model establishes a data analysis model based on the pump efficiency and operation data of the circulation pump to adjust the frequency of the water pump motor for chilled water and cooling water in real time under various operating conditions to ensure that the water pump operates in the high-efficiency operation range; Green Operation Management Layer: It is used for the automatic control of the system's cooling and / or heating supply.

[0007] Preferably, the system further includes an outdoor weather station that monitors the temperature and humidity in real time to establish an outdoor weather model, and the outdoor weather model establishes the meteorological prediction model by monitoring the outdoor temperature and humidity meteorological data in real time.

[0008] Preferably, the Artificial Intelligence Layer is used to deeply analyze and process the data transmitted by the Energy Big Data Layer, and continuously optimize the parameters and operation strategies of each model through algorithms such as machine learning to improve the system's intelligence level and energy-saving effect.

[0009] Preferably, the Artificial Intelligence Layer has the ability of optimization algorithms, enabling the energy system to analyze, predict the operation law based on big data, and quickly respond to the changes in cooling and heating loads, achieving a high degree of matching between energy supply and actual load demand.

[0010] Preferably, the optimization algorithm is used to analyze and process the collected temperature, pressure, equipment load, and efficiency interval operation data. Each sub-model cooperates with each other to continuously optimize the system to seek the optimal operation strategy for the entire energy system, thereby achieving the purpose of energy-saving and high efficiency.

[0011] Preferably, the Green Operation Management Layer is used to implement and monitor the optimized operation strategy, and perform self-learning, correction, and self-strengthening energy efficiency management based on historical data, be able to sense abnormal situations, and promptly prompt manual intervention for corresponding adjustments.

[0012] Preferably, the Energy Equipment Layer includes temperature sensors, pressure sensors, and flow meter collection devices.

[0013] Preferably, the system is applicable to the adjustment of the energy system of newly built or renovated public buildings.

[0014] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above technical solutions adopted in the embodiments of this specification at least include: By establishing an outdoor meteorological model, a load response prediction model, a cold and heat source energy efficiency model, and a fluid system energy efficiency model, and combining with an optimization algorithm, the efficient regulation and energy-saving operation of the energy system are realized. The energy system can quickly respond to the changes in cooling and heating loads, achieve a high degree of matching between energy supply and actual load demand, and thus achieve a higher energy-saving effect. Brief Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic diagram of the intelligent energy control system provided by the present application. Detailed Embodiments

[0017] The embodiments of the present application will be described in detail below with reference to the drawings.

[0018] The following specific examples illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The present application can also be implemented or applied through other different specific implementation manners. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0019] It should be noted that the various aspects of the embodiments within the scope of the appended claims are described below. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present application, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects described herein can be used to implement the device and / or practice the method. Additionally, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0020] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. The diagrams only show the components related to the present application, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0021] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0022] A typical energy control system has no meteorological monitoring function and adjusts the load of adding or subtracting machines based on the return water temperature. The adjustment of the energy system has obvious hysteresis, especially in large energy centers, where the hysteresis is more serious.

[0023] The following describes the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.

[0024] As Figure 1 shown, a smart energy control system includes: Energy equipment layer: used for cooling and / or heating of the system and collecting basic data; Energy Internet of Things layer: collecting various operation data such as temperature, pressure, equipment load, and efficiency range through data collectors; Energy big data layer: the Energy Internet of Things layer transmits the data to the Energy big data layer for storage and preliminary processing; Artificial intelligence layer: including a meteorological prediction model, a cold and heat source optimization model, and a fluid system energy efficiency model. The meteorological prediction model is used to predict load changes in advance and enhance the adjustment ability of the system. The cold and heat source optimization model establishes a data analysis model based on the performance curve, efficiency curve, and operation data of the refrigeration unit to find the high-efficiency operation range of the refrigeration unit under various operating conditions. The fluid system energy efficiency model establishes a data analysis model based on the pump efficiency and operation data of the circulating pump to adjust the motor frequency of the chilled water and cooling water pumps in real time under various operating conditions to ensure that the pumps are in the high-efficiency operation range; Green operation management layer: used for automatic control of cooling and / or heating of the system.

[0025] The cold and heat source model is based on the performance curve and efficiency curve of the refrigeration unit, combined with the operation data of the cold and heat source, to establish a set of data analysis models and seek the high-efficiency operation range of the refrigeration unit under various operating conditions. The energy efficiency model of the fluid system is based on the pump efficiency of the circulating pump, combined with the operation data, to establish a set of data analysis models and seek to adjust the frequency of the water pump motor in real time for the chilled water and cooling water under various operating conditions to ensure that the water pump operates in the high-efficiency operation range. The entire energy system collects various operating data such as temperature, pressure, equipment load, and efficiency range through a data collector. Each sub-model cooperates with each other and continuously optimizes the system, that is, seeks the optimal operation strategy of the entire energy system, so as to achieve the purpose of energy conservation and high efficiency.

[0026] In some embodiments, the system further includes an outdoor weather station, which monitors the temperature and humidity in real time to establish an outdoor weather model. The outdoor weather model establishes the weather prediction model by monitoring the outdoor temperature and humidity meteorological data in real time. By adding an outdoor weather station to monitor the temperature and humidity in real time and establish a weather model, it has the function of predicting load changes in advance, enhancing the adjustment ability of the system, and thus enhancing the energy use efficiency.

[0027] In some embodiments, the artificial intelligence layer is used to deeply analyze and process the data transmitted by the energy big data layer, and continuously optimize the parameters and operation strategies of each model through algorithms such as machine learning to improve the intelligent level and energy-saving effect of the system. The artificial intelligence layer deeply mines the real-time and historical data transmitted by the energy big data layer through machine learning algorithms (such as LSTM neural network, reinforcement learning), including multi-dimensional parameters such as temperature, pressure, equipment load rate, and energy efficiency range. Its core functions include: feature extraction: identifying the threshold of the high-efficiency operation range of the cold and heat source equipment from the massive data (such as the load rate range corresponding to the peak COP of the refrigeration unit), breaking through the limitations of the traditional rule base; correlation analysis: establishing a non-linear mapping relationship between meteorological data (temperature, humidity, solar radiation) and the change of cooling and heating load to predict future dynamic demands. The artificial intelligence layer solves the problems of the traditional energy system relying on fixed rules, lagging response, and single energy efficiency optimization through the depth analysis ability driven by machine learning. Its advantages are reflected in three dimensions: data-driven decision-making, dynamic adaptive optimization, and multi-objective collaborative control, providing a full-link intelligent upgrade path from local equipment to global management for the smart energy system.

[0028] In some embodiments, the artificial intelligence layer possesses optimization algorithm capabilities, enabling the energy system to rapidly respond to changes in cooling and heating loads based on big data analysis, operational pattern analysis, and prediction, ensuring a close match between energy supply and actual load demand. This big data-driven operational pattern analysis and prediction capability includes: data fusion and feature extraction: Technical implementation: Multi-source data integration: Integrating heterogeneous data such as meteorological stations (temperature, humidity, and solar radiation), equipment sensors (temperature, pressure, and flow), and grid signals (time-of-use electricity prices) to build a unified data lake; Time Series Pattern Recognition: Using LSTM (Long Short-Term Memory) networks to periodically decompose historical load data (such as hourly cooling load) into daily, weekly, and seasonal cycles to extract load variation characteristics (such as weekday morning peaks and weekend off-peaks). Predicting load changes in advance allows cooling and heating equipment to enter a ready state 30 minutes in advance, eliminating energy waste caused by lags in traditional systems. This optimized algorithm architecture for energy supply and demand matching achieves a faster response time to sudden load fluctuations than traditional PID control, preventing a decrease in comfort caused by overcooling or overheating.

[0029] In some embodiments, the optimization algorithm analyzes and processes collected operating data on temperature, pressure, equipment load, and efficiency ranges. The sub-models collaborate to continuously optimize the system to find the optimal operating strategy for the entire energy system, thereby achieving energy conservation and efficiency. The optimization algorithm first performs the following processing on operating data such as temperature, pressure, equipment load, and efficiency ranges: Data cleaning and noise reduction: A sliding window algorithm is used to filter sensor noise and remove outliers; Feature engineering: Key features of equipment efficiency curves are extracted to construct equipment performance maps; Time series correlation analysis: An LSTM network is used to map temperature changes to cooling and heating loads, predicting load fluctuations over the next few hours. Data preprocessing improves the usability of raw data and enhances feature extraction accuracy, laying the foundation for model optimization. Cooling and heating source optimization: Based on the efficiency curves of the refrigeration units, load demands are dynamically matched to efficient operating ranges; Fluid system tuning: Model predictive control is used to adjust pump frequency based on real-time flow demand to reduce inefficient power consumption caused by "high flow rates with small temperature differences." The optimization algorithm integrates the following sub-models to achieve system-level optimization: ‌Meteorological Forecast Model‌: This inputs temperature, humidity, and solar radiation data to generate a baseline for heating and cooling loads for the next few hours, guiding equipment pre-startup; ‌Heat and Cooling Source Optimization Model‌: This uses a multi-objective algorithm to balance energy efficiency, cost, and equipment life to generate the optimal start-stop combination; ‌Fluid Energy Efficiency Model‌: This combines the pipe network resistance coefficient with the pump efficiency curve to dynamically calculate the optimal head and flow matching point.

[0030] It should be noted that through data-driven decision-making and multi-objective dynamic optimization, the optimization algorithm realizes the full-link energy efficiency management from local devices to the global system, breaking through the bottlenecks of traditional energy systems, such as relying on manual experience, having a lag in response, and a single optimization dimension.

[0031] In some embodiments, the green operation management layer is used to implement and monitor the optimized operation strategy, and perform self-learning, correction, and self-strengthening of energy efficiency management based on historical data. It can sense abnormal situations, such as important information like equipment replacement, too high condensation temperature, and too large resistance in the circulating water system, and promptly prompt manual intervention for corresponding adjustment. The dynamic implementation and monitoring of the operation strategy include: Strategy execution control: Send the operation instructions (such as pump frequency, chiller load rate) generated by the optimization algorithm to the equipment controller through the Internet of Things protocol; Real-time data feedback: Collect operation parameters such as temperature, pressure, and flow rate, update the monitoring panel once at regular intervals, and synchronize to the cloud database for model iteration. Self-learning and correction based on historical data include: Energy efficiency model iteration: Use the annual operation data stored in the time series database to update the parameters of the energy efficiency model at regular intervals through the LSTM network to adapt to long-term factors such as equipment aging and seasonal changes; Strategy library optimization: When it is detected that the resistance fluctuation in the circulating water system exceeds the threshold, automatically generate new control logic and store it in the strategy library to reduce the frequency of manual intervention. By real-time sensing of key parameters such as condensation temperature and resistance anomalies, the equipment failure rate is reduced.

[0032] In some embodiments, the energy equipment layer includes temperature sensors, pressure sensors, and flowmeter acquisition devices. The temperature sensors collect the inlet and outlet temperature data of the cold and heat source equipment (such as chillers, heat exchangers) in real time, and trigger control instructions in combination with preset thresholds; The pressure sensors monitor the pressure changes at key nodes such as the circulating water system and the steam pipe network in real time, and trigger an alarm and start a protection mechanism when abnormal resistance is detected; The flowmeters accurately monitor the flow rates of media such as cold and hot water, steam, etc. Through high-precision data acquisition and multi-parameter collaborative analysis, the temperature, pressure, and flow sensors realize the dynamic optimization and refined management of the energy system, supporting the intelligent closed-loop from equipment-level energy efficiency improvement to global energy scheduling.

[0033] The devices in the energy Internet of Things layer (such as data collectors, controllers) and the energy equipment layer (such as temperature sensors, pressure sensors, flowmeters, etc.) are all purchased externally. After completing the acquisition of basic technical parameters and data transmission work, the deployment and application of this energy control system are carried out.

[0034] In some embodiments, the system is applicable to the regulation of the energy systems of newly built or renovated public buildings. The energy system can quickly respond to the changes in cooling and heating loads, achieving a high degree of matching between energy supply and actual load demand, thereby achieving higher energy-saving effects. For the same or similar parts among the various embodiments in this specification, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the method embodiments described later, since they correspond to the system, the description is relatively simple, and reference can be made to the relevant parts of the system embodiments for the relevant content.

[0035] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent energy control system, characterized in that, Including: Energy equipment layer: used for cooling and / or heating of the system and collecting basic data; Energy Internet of Things layer: collecting various operation data such as temperature, pressure, equipment load, and efficiency range through data collectors; Energy big data layer: the Energy Internet of Things layer transmits data to the Energy big data layer for storage and preliminary processing; Artificial intelligence layer: including a meteorological prediction model, a cold and heat source optimization model, and a fluid system energy efficiency model. The meteorological prediction model is used to predict load changes in advance and enhance the adjustment ability of the system. The cold and heat source optimization model establishes a data analysis model based on the performance curve, efficiency curve, and operation data of the refrigeration unit to find the high-efficiency operation range of the refrigeration unit under various operating conditions. The fluid system energy efficiency model establishes a data analysis model based on the pump efficiency and operation data of the circulating pump to adjust the motor frequency of the chilled water and cooling water pumps in real time under various operating conditions to ensure that the pumps are in the high-efficiency operation range; Green operation management layer: used for automatic control of cooling and / or heating of the system.

2. The intelligent energy control system according to claim 1, characterized in that, The system further includes an outdoor weather station, which monitors temperature and humidity in real time to establish an outdoor weather model, and the outdoor weather model establishes the meteorological prediction model by monitoring outdoor temperature and humidity meteorological data in real time.

3. The intelligent energy control system according to claim 1, wherein The artificial intelligence layer is used to deeply analyze and process the data transmitted by the energy big data layer, and continuously optimize the parameters and operation strategies of each model through algorithms such as machine learning to improve the intelligent level and energy-saving effect of the system.

4. The intelligent energy control system according to claim 3, characterized in that The artificial intelligence layer has the ability of optimization algorithms, enabling the energy system to analyze, predict the operation law based on big data, and quickly respond to the changes in cooling and heating loads, achieving a high degree of matching between energy supply and actual load demand.

5. The intelligent energy control system according to claim 4, wherein The optimization algorithm is used to analyze and process the collected operation data of temperature, pressure, equipment load, and efficiency range. Each sub-model cooperates with each other to continuously optimize the system to seek the optimal operation strategy of the entire energy system, thereby achieving the purpose of energy saving and high efficiency.

6. The intelligent energy control system according to claim 1, characterized in that, The green operation management layer is used to implement and monitor the optimized operation strategy, and perform self-learning, correction, and self-strengthening energy efficiency management according to historical data, be able to sense abnormal situations, and timely prompt manual intervention for corresponding adjustments.

7. The intelligent energy control system according to claim 1, wherein The energy equipment layer includes temperature sensors, pressure sensors, and flowmeter collection devices.

8. The intelligent energy control system according to any one of claims 1-7, characterized in that, The system is applicable to the adjustment of energy systems in newly built or renovated public buildings.

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