Intelligent management and control system for energy consumption of green building

Abstract

The invention discloses a green building energy consumption intelligent management and control system, which comprises a plurality of equipment nodes and a management and control core, and is characterized in that the equipment nodes are configured to be in a dormant state by default and send state data in an event-driven mode only when sensed environmental parameter variation exceeds a preset threshold value, the management and control core is in communication connection with the plurality of equipment nodes and is configured to receive and process the state data; according to a preset energy cascade scheduling strategy, a control instruction for scheduling and utilizing energy in the building environment is generated, and waste heat generated in a building is preferentially utilized as first cascade energy according to the instruction; and generating and sending a non-intrusive behavior prompt signal to the user terminal when the energy-saving opportunity which can be realized through the physical interaction of the user is identified. According to the invention, the problems of high energy consumption, low energy utilization efficiency and neglect of user participation in the prior art are solved, and the total energy consumption of the building is obviously reduced.

Description

Technical Field

[0001] This invention relates to the field of building automation technology, and more specifically to a smart energy consumption management system and method for large buildings based on an asynchronous self-consistent management mechanism. Background Technology

[0002] With the development of information technology, intelligent building energy-saving management and control systems have become a key technical path to reduce energy consumption in large commercial buildings. The heating, ventilation and air conditioning system of such buildings is the main energy-consuming unit, and therefore the focus of energy-saving optimization. The core idea of ​​the current mainstream technical solution is to build a complex data-driven control model, carry out high-density data collection by deploying a large number of sensors, and use cloud or local computing resources for real-time online optimization.

[0003] Such technical solutions primarily aim to accurately model the thermodynamic dynamics of buildings using complex algorithms such as machine learning or deep learning, seeking the globally optimal strategy for equipment operation while ensuring user comfort. However, to achieve online optimization, these solutions require the continuous acquisition of comprehensive data on the building environment and rely on complex algorithm models for high-frequency decision iterations. This process itself generates significant energy consumption. The electricity consumed by the sensor network, data communication links, and computing units that perform model calculations greatly diminishes the energy-saving benefits achieved through optimized control, creating a technical contradiction of increasing energy consumption for the sake of energy conservation.

[0004] Furthermore, the control logic of the existing solution also has limitations. It is essentially a strong intervention model that tends to directly confront the regulation. For example, when the temperature in a certain area is too high, the system tends to directly output an instruction to increase the cooling capacity, while ignoring the possibility of using the waste heat of that area as an internal resource and transferring it to other areas that need heat in a low-energy way. This architecture treats the building as a passive object that needs to be strongly disciplined by external intelligence, rather than an ecosystem that can achieve internal energy self-consistency, thus causing secondary energy waste.

[0005] At the same time, this type of highly automated technical solution systematically ignores the key role of humans in energy conservation. When the building environment is taken over by a fully automated, seemingly omnipotent intelligent system, it will weaken users' sense of responsibility and participation in energy conservation, and may even induce compensatory energy waste behavior, so that the energy-saving achievements made by technology are ultimately offset by human factors.

[0006] Existing technologies, based on continuous monitoring, centralized computing, and strong intervention, are facing systemic challenges such as high energy consumption, low energy utilization efficiency, and neglect of human factors. Therefore, there is an urgent need in this field for a new solution that can fundamentally change the existing architecture and achieve low-energy self-consistent operation and human-machine collaboration. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a smart energy consumption management system and method for green buildings. The main purpose is to solve the problems of high energy consumption, rigid control logic, and neglect of user participation in existing smart building management systems.

[0008] To achieve the above objectives, the present invention provides the technical solutions as described in claims 1-8: A smart energy consumption management system for green buildings includes: multiple device nodes and a management core; the multiple device nodes are configured to be in a sleep state by default, and only send status data in an event-driven manner when the sensed changes in environmental parameters exceed a preset threshold; the management core is communicatively connected to the multiple device nodes and is configured to: receive and process the status data sent by the device nodes; generate control instructions for scheduling and utilizing energy within the building environment according to a preset energy tiered scheduling strategy, wherein the instructions prioritize the use of waste heat generated inside the building as the first tier of energy; and generate and send a non-intrusive behavioral prompt signal to the user terminal when an energy-saving opportunity that can be achieved through physical user interaction is identified.

[0009] Preferably, the control core is further configured to generate control instructions for activating energy-consuming devices at higher energy levels only when it is determined that the energy of the first energy level is insufficient to meet environmental requirements, based on the energy tier scheduling strategy.

[0010] Preferably, the device node is configured to automatically return to the sleep state after sending the status data, until the next change in the environmental parameters exceeds the preset threshold.

[0011] Preferably, the control core is configured to identify heat data from server rooms, lighting equipment, or human activity as the source of the first-level energy.

[0012] Preferably, the higher energy cascade energy-consuming equipment includes heat pump equipment or resistance heating equipment.

[0013] Preferably, the non-invasive behavioral prompt signal is an optical signal, an acoustic signal, or a tactile vibration signal, used to prompt the user to perform physical operations such as opening a window or adjusting a sunshade.

[0014] This invention also provides a smart energy consumption management method for green buildings, comprising the following steps: configuring multiple device nodes within the building environment to a sleep state by default; triggering the device nodes to send status data in an event-driven manner only when the changes in environmental parameters sensed by the device nodes exceed a preset threshold; scheduling and utilizing energy within the building environment based on the received status data and according to a preset energy tiered scheduling strategy, wherein waste heat generated inside the building is preferentially utilized as the first tier of energy; and sending a non-intrusive behavioral prompt signal to the user terminal when an energy-saving opportunity that can be achieved through user physical interaction is identified, to guide the user to complete the energy-saving operation.

[0015] Preferably, the step of scheduling and utilizing energy within the building environment further includes: only when the energy of the first tier is insufficient to meet environmental needs, sequentially activating energy-consuming equipment of higher energy tiers according to the energy tier scheduling strategy.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention employs an event-driven asynchronous communication mechanism, which keeps device nodes in a dormant state most of the time, greatly reducing the operating energy consumption of the sensing system itself and resolving the technical contradiction of increasing energy consumption for energy saving.

[0017] This invention introduces an energy cascade scheduling strategy, which treats the waste heat inside a building as a usable primary resource for priority scheduling. This overturns the traditional direct confrontation-style energy regulation logic and improves the building's energy self-sufficiency and overall energy efficiency.

[0018] This invention establishes a human-machine symbiotic incentive mechanism, which guides users to participate in energy-saving operations in a non-intrusive manner when a suitable energy-saving opportunity is identified. This transforms user behavior into a zero-cost adjustment variable, effectively enhancing users' sense of participation in energy saving and achieving synergistic effects between technological and behavioral energy saving. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 A system architecture block diagram of a smart energy consumption management system for green buildings provided in an embodiment of the present invention; Figure 2 This is an overall flowchart of a smart energy consumption management method for green buildings provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the core data interaction process in an embodiment of the present invention; Figure 4This is a flowchart illustrating the collaborative decision-making logic within the control core in this embodiment of the invention. Figure 5 This is a timing diagram of human-machine symbiotic stimulation interaction in an embodiment of the present invention. Detailed Implementation

[0020] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Example 1 like Figures 1-5 As shown, this embodiment of the invention provides a smart energy management and control system 100 for green buildings. In this embodiment, the system 100 is applied to the heating, ventilation, air conditioning and environmental control of large commercial buildings. The system is logically divided into a perception layer 110, a network transmission layer 120, an execution layer 130, a data and model platform 140, a management and control core 150 and a human-computer interaction layer 160.

[0022] The system, through the unified scheduling of the control core 150, reconstructs the originally isolated, high-frequency, and high-energy-consumption sensing and control behaviors into a silent-response mode based on the real needs of the physical world, with extremely low frequency and extremely low energy consumption, and introduces cross-regional tiered utilization of energy and human-machine collaboration.

[0023] In this embodiment, the sensing layer 110 is equipped with multiple ultra-low power wireless sensors, including temperature and humidity sensors, carbon dioxide sensors, human presence sensors, and door and window status sensors. The sensors are characterized in that their working modes are configured by the control core 150 using a unified strategy, mainly to minimize unnecessary data transmission.

[0024] The network transmission layer 120 adopts a hybrid networking approach, consisting of a privately deployed low-power wide-area wireless communication network, a wireless local area network covering the entire building, and a wired Ethernet. In this embodiment, the low-power wide-area wireless communication network can specifically be a network based on LoRaWAN technology. The data from the perception layer 110 is aggregated to the gateway through this network. The gateway publishes the parsed data to the server cluster of the local data center in a standardized data exchange format, such as JSON, through a lightweight publish-subscribe message transmission protocol, such as MQTT.

[0025] The execution layer 130 consists of a series of protocol gateways and intelligent actuators. In this embodiment, a protocol gateway is deployed to realize the conversion of building automation control network protocol to industrial fieldbus protocol. The building automation control network protocol can be BACnet / IP protocol, and the industrial fieldbus protocol can be Modbus RTU protocol.

[0026] The data and model platform 140 is built on a local server cluster and includes a time-series database, a relational database, and a core building thermodynamics digital twin model. The model can be built based on a building energy consumption simulation engine, and in this embodiment, the EnergyPlus engine can be used.

[0027] The control core 150 is the decision-making center of the system. Its internal energy cascade scheduling module has the core objective of minimizing the total operating cost of the system. The optimization problem it follows in making decisions can be expressed as: In the formula, The optimal control strategy; The space for all available control actions; For a specific control action; To perform the action Direct operating energy consumption; For action The energy level is a discrete integer, for example, passive waste heat utilization is level 0, high-efficiency heat pump transfer is level 1, and pure electric direct heating is level 2; As a tiered weighting factor; For building thermodynamic transfer functions; This is the current state vector; Let be the target state vector to be achieved.

[0028] Simultaneously, the control core 150 sends a query to the human-machine collaborative decision-making module in parallel. This module is responsible for introducing human intelligence as a zero-cost adjustment variable. Its core is an activation decision function, the logic of which can be expressed as: if If the behavior is initiated, then behavioral guidance will be launched; otherwise, it will not be launched.

[0029] In the formula, For user compliance probability; For incremental energy-saving benefits; Cost of interaction disruption.

[0030] Example 2 Based on Embodiment 1, this embodiment optimizes and upgrades the internal algorithm model of the control core 150.

[0031] As a preferred implementation, the optimization solution unit in the energy cascade scheduling module can be upgraded from the linear programming-based algorithm in Embodiment 1 to a second-order cone programming solver. The second-order cone programming solver can better handle the nonlinear convex constraints in building thermodynamics, thereby finding a more accurate optimal solution.

[0032] Meanwhile, the model used to predict the probability of user compliance in the human-machine collaborative decision-making module can be replaced from the logistic regression model in Embodiment 1 with a gradient boosting decision tree model. The gradient boosting decision tree model can learn the complex nonlinear interactions between high-dimensional features, thereby creating a more refined profile of the user's decision-making habits.

[0033] Furthermore, the anomaly handling mechanism in this embodiment adds predictive maintenance functionality. In the event of sensor data loss, the control core 150 can input this event into a device failure prediction model based on survival analysis to predict the remaining effective lifespan of the device, thereby realizing the transformation from passive response maintenance to proactive predictive maintenance.

[0034] Example 3 As a preferred implementation, this embodiment adjusts the system architecture and adopts a federated edge-core architecture. One or more edge computing gateways are added between the control core 150, the network transmission layer 120, and the execution layer 130. Each edge computing gateway runs a lightweight micro control core. The central control core 150 deployed in the local data center serves as the macro control core, mainly undertaking the roles of global optimization, model training, and cross-regional energy scheduling.

[0035] This architecture pushes most of the real-time-critical computing and decision-making capabilities to the physical edge of the building, achieving local autonomy and cloud collaboration. The AI ​​models deployed at the edge are compressed through model quantization and pruning techniques, and continuously optimized by the central macro-control core through a federated learning mechanism.

[0036] Example 4 This embodiment analyzes the performance and robustness of the system under extreme operating conditions.

[0037] In response to large-scale instantaneous gatherings of people, the low-power wide-area wireless communication protocol of the network transmission layer 120 has an adaptive data rate mechanism that can effectively cope with channel congestion. At the same time, the control core 150 will identify the sudden event pattern and temporarily increase the rate of change threshold of the relevant area sensors to achieve intelligent peak shaving of data storms.

[0038] In response to extreme weather events, upon receiving a severe weather warning, the control core 150 will enter a prepared emergency mode, invoke the digital twin model to conduct long-term simulations, and generate emergency control plans. For example, by pre-cooling or preheating the building during periods of low electricity prices, the building itself can be used as a thermal energy storage unit.

[0039] In response to sudden failures of critical infrastructure, when a critical device is detected to be offline, the control core 150 will immediately trigger the fault emergency response logic, remove the faulty device from the set of available actions and recalculate the global optimization to maximize the use of the temperature difference between areas within the building for cross-regional heat dispatch.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart energy consumption management system for green buildings, characterized in that, include: Multiple device nodes and the aforementioned control core; The multiple device nodes are configured to be in a sleep state by default, and only send status data in an event-driven manner when the sensed changes in environmental parameters exceed a preset threshold. The control core is communicatively connected to the multiple device nodes and is configured to: receive and process the status data sent by the device nodes; generate control instructions for scheduling and utilizing energy within the building environment according to a preset energy tiered scheduling strategy, wherein the instructions prioritize the use of waste heat generated inside the building as the first tier of energy; and generate and send non-intrusive behavioral prompt signals to the user terminal when an energy-saving opportunity that can be achieved through user physical interaction is identified.

2. The intelligent energy consumption management system for green buildings according to claim 1, characterized in that, The control core is further configured to generate control commands for activating energy-consuming devices at higher energy levels only when it is determined that the energy of the first energy level is insufficient to meet environmental requirements, based on the energy tier scheduling strategy.

3. The intelligent energy consumption management system for green buildings according to claim 1, characterized in that, The device node is configured to automatically return to the sleep state after sending the status data, until the next change in the environmental parameters exceeds the preset threshold.

4. The intelligent energy consumption management system for green buildings according to claim 1, characterized in that, The control core is configured to identify heat data from server rooms, lighting equipment, or human activity as the source of the first tier of energy.

5. The intelligent energy consumption management system for green buildings according to claim 2, characterized in that, The higher energy cascade energy-consuming equipment includes heat pump equipment or resistance heating equipment.

6. The intelligent energy consumption management system for green buildings according to claim 1, characterized in that, The non-invasive behavioral prompt signal is an optical signal, acoustic signal, or tactile vibration signal, used to prompt the user to perform physical operations such as opening a window or adjusting a sunshade.

7. A method for intelligent management and control of energy consumption in green buildings, characterized in that, Includes the following steps: Multiple device nodes within the construction environment are configured to be in a dormant state by default. The device node is triggered to send status data in an event-driven manner only when the change in environmental parameters sensed by the device node exceeds a preset threshold. Based on the received status data and according to the preset energy tiered scheduling strategy, the energy in the building environment is scheduled and utilized, wherein the waste heat generated inside the building is given priority as the first tier of energy. In addition, when an energy-saving opportunity that can be achieved through physical user interaction is identified, a non-intrusive behavioral prompt signal is sent to the user terminal to guide the user to complete the energy-saving operation.

8. The intelligent energy consumption management method for green buildings according to claim 7, characterized in that, The step of scheduling and utilizing energy within the building environment further includes: only when the energy of the first tier is insufficient to meet environmental needs, then according to the energy tier scheduling strategy, sequentially activating energy-consuming equipment of higher energy tiers.

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