Intelligent building energy consumption optimization control system and method based on Internet of Things

Abstract

The invention discloses an intelligent building energy consumption optimization control system and method based on the Internet of Things, relates to the technical field of energy consumption optimization, and realizes intelligent control of equipment in a building. Energy distribution is optimized, normal operation of the building and optimal control over energy consumption are ensured, and the purposes of energy conservation and emission reduction are achieved; the building energy consumption can be effectively reduced, the energy waste is reduced, the purposes of energy conservation and emission reduction are achieved, and remarkable economic and social benefits are achieved. The scheme is characterized in that a data acquisition module is used for acquiring energy consumption data and environmental parameters of a to-be-optimized building in real time; the strategy generation module is used for generating an energy consumption optimization control strategy; the control execution module is used for controlling equipment in the to-be-optimized building according to the energy consumption optimization control strategy; the energy management module is used for performing centralized management and scheduling on energy consumption of the to-be-optimized building according to the energy consumption optimization control strategy; and the communication module is used for data transmission and communication among the modules. The method is used for building energy consumption optimization control.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption optimization, and in particular to an intelligent building energy consumption optimization control system and method based on the Internet of Things. Background Art

[0002] With the development of the global economy and the acceleration of urbanization, building energy consumption has become a significant component of overall energy consumption. According to relevant statistics, building energy consumption accounts for approximately 30% to 40% of total energy consumption, and this proportion continues to rise annually. Building energy consumption primarily encompasses heating, cooling, lighting, hot water supply, elevator operation, and the use of various electrical appliances. Therefore, effectively managing and optimizing building energy consumption has become a critical component in achieving energy conservation and emission reduction goals.

[0003] In traditional buildings, energy management relies primarily on manual operations and simple timer controls. For example, energy consumption is managed by manually adjusting air conditioning temperatures, turning lighting on and off, or setting elevator operating times.

[0004] In the above energy consumption management methods, manual operations cannot achieve real-time monitoring and precise control of building energy consumption. Managers need to regularly inspect the operating status of equipment, and it is difficult to detect abnormal energy consumption in time, resulting in energy waste; it is impossible to systematically analyze and model energy consumption data, find the deep-seated laws of energy consumption changes, and formulate scientific and reasonable optimization strategies; in order to achieve energy-saving goals, it is often necessary to sacrifice user comfort, for example: excessively lowering the air-conditioning temperature or reducing the lighting brightness, resulting in poor user experience; due to the lack of intelligent control means, equipment may still maintain high energy consumption when no one is using it or running at low load, such as: lighting equipment in vacant rooms or air-conditioning equipment running for a long time; energy-consuming equipment in traditional buildings (such as air conditioning, lighting, elevators, etc.) usually operate independently, lacking unified coordination and management, making it difficult to achieve overall energy consumption optimization. Summary of the Invention

[0005] The present invention provides an intelligent building energy consumption optimization control system and method based on the Internet of Things. Through the Internet of Things technology, real-time monitoring and data analysis of building energy consumption are realized, abnormal energy consumption can be discovered in time, and an accurate basis for energy consumption optimization control is provided; a scientific and reasonable energy consumption optimization control strategy is generated to realize intelligent control of equipment in the building, reduce energy waste, and improve energy utilization efficiency; the energy consumption of the building is centrally managed and scheduled, the allocation of energy resources is optimized, the normal operation of the building and the optimized control of energy consumption are ensured, and the goal of energy conservation and emission reduction is achieved; it can effectively reduce building energy consumption, reduce energy waste, and achieve the goal of energy conservation and emission reduction, with significant economic and social benefits.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides an intelligent building energy consumption optimization control system based on the Internet of Things, comprising:

[0008] Data acquisition module, strategy generation module, control execution module, energy management module and communication module.

[0009] The data acquisition module is used to collect energy consumption data and environmental parameters of the building to be optimized in real time.

[0010] The strategy generation module is connected to the data acquisition module and is used to generate an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters.

[0011] The control execution module is connected to the strategy generation module and is used to control the equipment in the building to be optimized according to the energy consumption optimization control strategy.

[0012] The energy management module is connected to the strategy generation module and is used to centrally manage and schedule the energy consumption of the building to be optimized according to the energy consumption optimization control strategy.

[0013] The communication module is connected to the data acquisition module, the strategy generation module, the control execution module, and the energy management module respectively, and is used for data transmission and communication among the data acquisition module, the strategy generation module, the control execution module, and the energy management module.

[0014] Furthermore, the energy consumption optimization control system for intelligent buildings based on the Internet of Things also includes: a preprocessing module.

[0015] The preprocessing module is connected to the data acquisition module, the strategy generation module, and the communication module respectively, and is used to preprocess the energy consumption data and the environmental parameters; the preprocessing includes but is not limited to data cleaning, denoising, and formatting.

[0016] Furthermore, the IoT-based intelligent building energy consumption optimization control system further includes: a user interaction module.

[0017] The user interaction module is respectively connected to the data acquisition module, the strategy generation module, the control execution module, the energy management module, and the communication module, and is used for allowing the user to view the energy consumption data, the environmental parameters, the energy consumption optimization control strategy, and the operating status of the equipment in the building to be optimized, and to perform manual intervention and settings.

[0018] Furthermore, the data acquisition module of the intelligent building energy consumption optimization control system based on the Internet of Things includes:

[0019] The data acquisition module includes an energy consumption acquisition unit and an environmental parameter acquisition unit.

[0020] The energy consumption collection unit is used to collect the energy consumption data of the building to be optimized in real time.

[0021] The environmental parameter acquisition unit is used to acquire the environmental parameters of the building to be optimized in real time.

[0022] Furthermore, the energy consumption collection unit of the intelligent building energy consumption optimization control system based on the Internet of Things includes:

[0023] The energy consumption collection unit includes but is not limited to electricity meters, water meters and gas meters.

[0024] Furthermore, in the IoT-based intelligent building energy consumption optimization control system, the environmental parameter acquisition unit includes:

[0025] The environmental parameter acquisition unit includes but is not limited to a temperature sensor, a humidity sensor, and a light sensor.

[0026] A second aspect of the present invention provides an intelligent building energy consumption optimization control method based on the Internet of Things, comprising:

[0027] Real-time collection of energy consumption data and environmental parameters of the building to be optimized.

[0028] An energy consumption optimization control strategy is generated according to the energy consumption data and the environmental parameters.

[0029] The equipment in the building to be optimized is controlled according to the energy consumption optimization control strategy, and energy consumption is centrally managed and scheduled.

[0030] Furthermore, the IoT-based intelligent building energy consumption optimization control method, after collecting the energy consumption data and environmental parameters of the building to be optimized in real time, further includes:

[0031] The energy consumption data and the environmental parameters are preprocessed; the preprocessing includes but is not limited to data cleaning, denoising and formatting.

[0032] Furthermore, the energy consumption optimization control method for smart buildings based on the Internet of Things, after controlling the equipment in the building to be optimized according to the energy consumption optimization control strategy and centrally managing and scheduling energy consumption, further includes:

[0033] Check the energy consumption data, the environmental parameters, the energy consumption optimization control strategy and the operating status of the equipment in the building to be optimized, and perform manual intervention and settings.

[0034] Furthermore, the energy consumption optimization control method for smart buildings based on the Internet of Things generates an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters, including:

[0035] The energy consumption data is modeled using a machine learning algorithm to obtain an energy consumption model, that is, a relationship model between the energy consumption data and time, and between the energy consumption data and the environmental parameters is established.

[0036] Based on the established energy consumption model, the energy consumption data change trend within a preset time period in the future is predicted to obtain a prediction result.

[0037] The energy consumption optimization control strategy is formulated according to the prediction results.

[0038] The present invention provides an intelligent building energy consumption optimization control system and method based on the Internet of Things, including: a data acquisition module for real-time acquisition of energy consumption data and environmental parameters of a building to be optimized; a strategy generation module for generating an energy consumption optimization control strategy based on the energy consumption data and environmental parameters; a control execution module for controlling equipment in the building to be optimized according to the energy consumption optimization control strategy; an energy management module for centralized management and scheduling of energy consumption of the building to be optimized according to the energy consumption optimization control strategy; a communication module for data transmission and communication among the data acquisition module, the strategy generation module, the control execution module, and the energy management module. Compared with the existing technology, the present invention realizes real-time monitoring and data analysis of building energy consumption through the Internet of Things technology, can timely discover abnormal energy consumption, and provide an accurate basis for energy consumption optimization control; generates a scientific and reasonable energy consumption optimization control strategy, realizes intelligent control of equipment in the building, reduces energy waste, and improves energy utilization efficiency; centrally manages and schedules the building's energy consumption, optimizes the allocation of energy resources, ensures the normal operation of the building and the optimized control of energy consumption, and achieves the goal of energy conservation and emission reduction; can effectively reduce building energy consumption, reduce energy waste, achieve the goal of energy conservation and emission reduction, and has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings are only used to illustrate the implementation methods and are not to be considered as limiting the present invention.

[0040] Figure 1 This is a schematic diagram of the structure of an intelligent building energy consumption optimization control system based on the Internet of Things in an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of the structure of another intelligent building energy consumption optimization control system based on the Internet of Things in an embodiment of the present invention;

[0042] Figure 3This is a flow chart of a method for optimizing energy consumption in smart buildings based on the Internet of Things according to an embodiment of the present invention;

[0043] Figure 4 The figure is a flow chart of another method for optimizing energy consumption of smart buildings based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present invention belongs; the terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions.

[0046] In the description of the embodiments of the present invention, technical terms such as "first" and "second" are used solely to distinguish between different objects and should not be understood to indicate or imply relative importance or to implicitly specify the quantity, specific order, or primary and secondary relationship of the technical features indicated. In the description of the embodiments of the present invention, "plurality" means more than two, unless otherwise specifically defined.

[0047] In the description of the embodiments of the present invention, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exists simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0048] In the description of the embodiments of the present invention, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0049] In the description of the embodiments of the present invention, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the embodiments of the present invention.

[0050] In the description of the embodiments of the present invention, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and can refer to internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on specific circumstances.

[0051] Example 1

[0052] The embodiment of the present invention provides an intelligent building energy consumption optimization control system based on the Internet of Things, such as Figure 1 Shown, including:

[0053] Data acquisition module 11 , strategy generation module 12 , control execution module 13 , energy management module 14 and communication module 15 .

[0054] The data acquisition module 11 is used to collect energy consumption data and environmental parameters of the building to be optimized in real time.

[0055] Among them, real-time acquisition emphasizes that the data acquisition module 11 can continuously obtain the latest data, rather than collecting data once at long intervals. Real-time performance is crucial for energy consumption optimization because it ensures that the system responds quickly to environmental changes or changes in the operating status of the equipment, thereby adjusting the energy consumption optimization control strategy in a timely manner to achieve optimal energy consumption management. For example: when the indoor temperature suddenly rises, the system needs to immediately collect this change and adjust the air-conditioning system through the energy consumption optimization control strategy to reduce energy consumption. When the light intensity changes, the system needs to collect light data in real time in order to adjust the brightness of the lighting system in a timely manner. The data collected in real time can help the system more accurately reflect the actual operating status of the building to be optimized, thereby improving the efficiency and effectiveness of energy consumption optimization.

[0056] Buildings to be optimized refer to buildings that need energy consumption optimization. These buildings may have energy waste problems due to design, aging equipment, poor operation and management, etc. By installing the data acquisition module 11, the energy consumption of these buildings can be comprehensively monitored and analyzed, and then targeted energy consumption optimization control strategies can be formulated.

[0057] Energy consumption data refers to various data related to the energy consumption of the building to be optimized, mainly including: (1) Electricity consumption: The electricity consumption data of each area or equipment in the building to be optimized is collected through smart meters. (2) Water consumption: The water consumption data of the building to be optimized is collected through water meters. (3) Gas consumption: The gas usage data of the building to be optimized is collected through gas meters. (4) Equipment operating status data: For example, the operating power of the air-conditioning system, the speed of the fan, the number of elevator operations, etc. These data reflect the energy consumption of the building to be optimized in different time periods and are an important basis for evaluating energy consumption levels and formulating energy consumption optimization control strategies.

[0058] Environmental parameters refer to the environmental condition data related to the energy consumption optimization control strategy of the building to be optimized, mainly including: (1) Indoor temperature: The temperature data of each area in the building to be optimized is collected through temperature sensors. (2) Indoor humidity: The humidity data of each area in the building to be optimized is collected through humidity sensors. (3) Light intensity: The light intensity data of each area in the building to be optimized is collected through light sensors.

[0059] Environmental parameters have a direct impact on energy consumption. For example, high or low indoor temperatures can affect the energy consumption of air conditioning systems, while light intensity affects the energy consumption of lighting systems. By collecting these environmental parameters, the system can dynamically adjust the operating status of equipment based on actual environmental conditions to optimize energy consumption.

[0060] The data acquisition module 11 is the foundation of the entire intelligent building energy optimization control system. It is responsible for collecting various data related to the energy consumption optimization of the building to be optimized. This data serves as the basis for subsequent analysis, modeling, and the generation of energy optimization control strategies. The data acquisition module typically includes a variety of sensors and data transmission devices to obtain real-time data from various parts of the building to be optimized.

[0061] The main functions of the data acquisition module 11 include: (1) Sensor deployment: various sensors are reasonably arranged in the building to be optimized to ensure that they can fully cover the areas and equipment that need to be monitored. (2) Data acquisition: each sensor collects energy consumption data and environmental parameters in real time and converts the data into electrical signals or digital signals. (3) Data transmission: the collected data is transmitted to the strategy generation module 12 through the communication module 15 (such as Wi-Fi, ZigBee, NB-IoT, etc.). (4) Preliminary data processing: during the data transmission process, it may be necessary to perform preliminary processing on the data, such as data format conversion, simple data verification, etc., to ensure the integrity and accuracy of the data.

[0062] The strategy generation module 12 is connected to the data acquisition module 11 and is used to generate an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters.

[0063] Among them, generating energy consumption optimization control strategies based on energy consumption data and environmental parameters mainly includes: using machine learning algorithms to model energy consumption data to obtain an energy consumption model, that is, establishing a relationship model between energy consumption data and time, and energy consumption data and environmental parameters; based on the established energy consumption model, predicting the energy consumption data change trend within a preset time period in the future to obtain prediction results; and formulating energy consumption optimization control strategies based on the prediction results.

[0064] The close connection between the strategy generation module 12 and the data acquisition module 11, as well as the core role of the strategy generation module 12 in energy consumption optimization, by receiving energy consumption data and environmental parameters in real time, the strategy generation module 12 can scientifically and dynamically generate energy consumption optimization control strategies, thereby achieving effective management and optimization of building energy consumption.

[0065] The control execution module 13 is connected to the strategy generation module 12 and is used to control the equipment in the building to be optimized according to the energy consumption optimization control strategy.

[0066] The energy consumption optimization control strategy is a set of instructions developed by the strategy generation module 12 based on real-time data and analysis results. It is used to guide the control execution module 13 on how to adjust the operating status of the equipment in the building to be optimized. These strategies include the following:

[0067] Adjustment of equipment operating parameters: for example, adjusting the temperature setting value of the air-conditioning system, the brightness of the lighting system, the operating mode of the elevator, etc.

[0068] Equipment start and stop control: For example, turning off some lighting equipment when there is sufficient light, or turning off equipment in certain areas when no one is using them.

[0069] Energy distribution adjustment: For example, according to the actual needs of different areas in the building to be optimized, resources such as electricity, water and gas are reasonably allocated.

[0070] The energy management module 14 is connected to the strategy generation module 12 and is used to centrally manage and schedule the energy consumption of the building to be optimized according to the energy consumption optimization control strategy.

[0071] Among them, the main function of the energy management module 14 is to centrally manage and dispatch the energy consumption in the building to be optimized according to the energy consumption optimization control strategy. Its specific functions include: (1) Energy allocation: According to the energy consumption optimization control strategy, reasonably allocate the energy resources in the building to be optimized. For example, dynamically adjust the distribution of electricity, water and gas according to the energy consumption demand in different time periods. (2) Equipment priority management: Determine the energy usage priority of each device or area in the building to be optimized, and give priority to ensuring the energy supply of key equipment or areas. For example, ensure that the server room has sufficient power supply at all times. (3) Dynamic scheduling: Dynamically adjust energy allocation according to real-time energy consumption data and environmental parameters. For example, when the energy consumption demand of a certain area suddenly increases, the energy management module 14 can automatically adjust the energy allocation of other areas to meet the current demand. (4) Energy monitoring: Real-time monitoring of energy usage in each area of ​​the building to be optimized to ensure the rationality and effectiveness of energy allocation. For example, the energy management system can view the power consumption of each area in real time. (5) Fault handling: When there is a problem with the energy supply, the energy management module 14 can automatically switch to backup energy or adjust energy allocation to ensure the normal operation of the building.

[0072] The communication module 15 is connected to the data acquisition module 11, the strategy generation module 12, the control execution module 13, and the energy management module 14 respectively, and is used for data transmission and communication among the data acquisition module 11, the strategy generation module 12, the control execution module 13, and the energy management module 14.

[0073] The communication module 15 is a key component of the entire system responsible for data transmission and communication. It ensures that the various modules in the system can exchange data and instructions efficiently and accurately, thereby achieving system collaboration. The main functions of the communication module 15 include:

[0074] Data transmission: responsible for transmitting the energy consumption data and environmental parameters collected by the data acquisition module 11 to the strategy generation module 12.

[0075] Instruction transmission: The energy consumption optimization control strategy generated by the strategy generation module 12 is transmitted to the control execution module 13 and the energy management module 14 .

[0076] State feedback: The execution results of the control execution module 13 and the energy management module 14 are fed back to the strategy generation module 12 to further adjust and optimize the energy consumption optimization control strategy.

[0077] The communication module 15 ensures that data is not lost or damaged during transmission, ensuring the integrity and accuracy of the data; the communication module 15 can quickly transmit data and instructions, ensuring that the system can respond to environmental changes and changes in equipment operating status in real time, and achieve dynamic energy consumption optimization; the communication module 15 needs to have high reliability to ensure that the system can operate normally in various environments. For example: even in the event of partial network failure, the communication module 15 can still transmit data through the backup communication link; the communication module 15 can support multiple communication protocols and device types, ensuring that the system can be integrated with equipment and modules from different manufacturers; the communication module 15 uses data encryption and security mechanisms to ensure the security of data transmission and prevent unauthorized access and data leakage.

[0078] The present invention provides an intelligent building energy consumption optimization control system based on the Internet of Things, comprising: a data acquisition module for real-time acquisition of energy consumption data and environmental parameters of a building to be optimized; a strategy generation module for generating an energy consumption optimization control strategy based on the energy consumption data and environmental parameters; a control execution module for controlling equipment in the building to be optimized according to the energy consumption optimization control strategy; an energy management module for centrally managing and scheduling the energy consumption of the building to be optimized according to the energy consumption optimization control strategy; and a communication module for data transmission and communication among the data acquisition module, the strategy generation module, the control execution module, and the energy management module. Compared with the prior art, the embodiment of the present invention realizes real-time monitoring and data analysis of building energy consumption through the Internet of Things technology, can promptly detect abnormal energy consumption, and provide an accurate basis for energy consumption optimization control; generates a scientific and reasonable energy consumption optimization control strategy, realizes intelligent control of equipment in the building, reduces energy waste, and improves energy utilization efficiency; centrally manages and schedules the building's energy consumption, optimizes the allocation of energy resources, ensures the normal operation of the building and the optimized control of energy consumption, and achieves the goal of energy conservation and emission reduction. The embodiment of the present invention can effectively reduce building energy consumption, reduce energy waste, achieve the goal of energy conservation and emission reduction, and has significant economic and social benefits.

[0079] Example 2

[0080] The embodiment of the present invention provides an intelligent building energy consumption optimization control system based on the Internet of Things, such as Figure 2 Shown, including:

[0081] Data acquisition module 11 , strategy generation module 12 , control execution module 13 , energy management module 14 and communication module 15 .

[0082] The data acquisition module 11 is used to collect energy consumption data and environmental parameters of the building to be optimized in real time.

[0083] The strategy generation module 12 is connected to the data acquisition module 11 and is used to generate an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters.

[0084] The control execution module 13 is connected to the strategy generation module 12 and is used to control the equipment in the building to be optimized according to the energy consumption optimization control strategy.

[0085] The energy management module 14 is connected to the strategy generation module 12 and is used to centrally manage and schedule the energy consumption of the building to be optimized according to the energy consumption optimization control strategy.

[0086] The communication module 15 is connected to the data acquisition module 11, the strategy generation module 12, the control execution module 13, and the energy management module 14 respectively, and is used for data transmission and communication among the data acquisition module 11, the strategy generation module 12, the control execution module 13, and the energy management module 14.

[0087] Preferably, the IoT-based intelligent building energy consumption optimization control system further includes: a preprocessing module 16 .

[0088] The preprocessing module 16 is connected to the data acquisition module 11, the strategy generation module 12, and the communication module 15 respectively, and is used to preprocess the energy consumption data and the environmental parameters; the preprocessing includes but is not limited to data cleaning, denoising, and formatting.

[0089] The pre-processing module 16 is a key step in the data processing process. It is responsible for the preliminary processing of the collected energy consumption data and environmental parameters to ensure the quality and consistency of the data. The purpose of pre-processing is to provide high-quality data support for the subsequent control strategy generation.

[0090] Specifically, the processing operations performed by the pre-processing module 16 on the energy consumption data and environmental parameters include but are not limited to the following:

[0091] (1) Data cleaning: Identify and remove abnormal data points caused by sensor failure, transmission errors, or environmental interference. For example, a temperature sensor may output an incorrect temperature value (such as -50°C or 100°C) due to a fault. This data needs to be identified and removed. During the data collection process, data may be missing due to sensor failure, communication interruption, etc. The preprocessing module can fill in missing data through methods such as interpolation, averaging, or model prediction. Ensure the consistency of data in the time series, for example, check whether the energy consumption data conforms to physical laws (such as energy consumption cannot be negative).

[0092] (2) Denoising: Reduce random fluctuations in the data through smoothing (such as moving average method, exponential smoothing method), making the data smoother and easier to analyze later; use low-pass filter or high-pass filter to remove high-frequency noise or low-frequency interference.

[0093] (3) Formatting: Convert the data into dimensionless standardized values, such as by Z-score standardization (subtracting the mean and dividing by the standard deviation) or normalization (scaling the data to the range of [0, 1] or [-1, 1]); ensure the consistency of data types, for example: convert all time data into a unified time format (such as ISO 8601 format) and convert all energy consumption data into a unified unit (such as kilowatt-hour); convert the collected raw data into a structured data format for easy storage and query, for example: store sensor data in a table format, where each row represents the data at a time point and each column represents the measurement value of a sensor.

[0094] Preferably, the energy consumption optimization control system for intelligent buildings based on the Internet of Things further includes: a user interaction module 17;

[0095] The user interaction module 17 is respectively connected to the data acquisition module 11, the strategy generation module 12, the control execution module 13, the energy management module 14, and the communication module 15, and is used for allowing the user to view the energy consumption data, the environmental parameters, the energy consumption optimization control strategy and the operating status of the equipment in the building to be optimized, and to perform manual intervention and settings.

[0096] User Interaction Module 17 is the interface between the system and the user. Its primary function is to provide an intuitive and convenient user interface, enabling users to view real-time building energy consumption data, environmental parameters, energy optimization control strategies, and the operating status of equipment within the building to be optimized, and allowing users to manually intervene and configure settings. The core functions of User Interaction Module 17 include: displaying real-time energy consumption data, environmental parameters, energy optimization control strategies, and equipment operating status to users; allowing users to manually adjust or intervene in control strategies based on actual conditions; and providing a user interface for users to set system parameters, configure equipment, and perform other operations.

[0097] Preferably, the data acquisition module 11 includes an energy consumption acquisition unit 11 and an environmental parameter acquisition unit 12 .

[0098] The energy consumption collection unit 11 is used to collect the energy consumption data of the building to be optimized in real time.

[0099] The energy consumption collection unit includes but is not limited to electricity meters, water meters and gas meters.

[0100] Specifically, electricity meters are used to measure electricity consumption within the building to be optimized. Meters include single-phase, three-phase, and smart meters, and can provide detailed electricity usage data, such as real-time power and cumulative electricity usage. Water meters are used to measure water consumption within the building to be optimized. Meters include mechanical, electronic, and smart meters, and can provide detailed water usage data, such as real-time flow and cumulative water usage. Gas meters are used to measure gas consumption within the building to be optimized. Meters include mechanical, electronic, and smart meters, and can provide detailed gas data, such as real-time flow and cumulative gas usage.

[0101] The environmental parameter acquisition unit 12 is used to acquire the environmental parameters of the building to be optimized in real time.

[0102] The environmental parameter acquisition unit includes but is not limited to a temperature sensor, a humidity sensor, and a light sensor.

[0103] Temperature sensors are used to measure the temperature within the building being optimized. Temperature is a key factor affecting the energy consumption of air conditioning systems. By monitoring indoor temperature in real time, the system can dynamically adjust the air conditioning system's operating mode based on temperature changes to achieve energy savings. Common temperature sensors include thermistors, thermocouples, and infrared temperature sensors, which provide highly accurate temperature measurement data.

[0104] Humidity sensors are used to measure humidity within buildings. Humidity has a significant impact on indoor comfort and energy consumption. For example, excessively high or low humidity may require adjustment through air conditioning systems or dehumidification equipment, thus affecting energy consumption. Common humidity sensors include capacitive humidity sensors and resistive humidity sensors, which can monitor indoor humidity changes in real time.

[0105] Light sensors are used to measure light intensity within a building. Light intensity has a direct impact on the energy consumption of indoor lighting systems. By monitoring light intensity in real time, the system can automatically adjust the brightness of the lighting system to achieve energy savings. Common light sensors include photodiodes and photoresistors, which can provide real-time data on light intensity.

[0106] It should be noted here that the detailed description of each component structure of this embodiment can refer to other embodiments and will not be repeated here.

[0107] The present invention provides an intelligent building energy consumption optimization control system based on the Internet of Things, comprising: a data acquisition module for real-time acquisition of energy consumption data and environmental parameters of a building to be optimized; a strategy generation module for generating an energy consumption optimization control strategy based on the energy consumption data and environmental parameters; a control execution module for controlling equipment in the building to be optimized according to the energy consumption optimization control strategy; an energy management module for centrally managing and scheduling the energy consumption of the building to be optimized according to the energy consumption optimization control strategy; and a communication module for data transmission and communication among the data acquisition module, the strategy generation module, the control execution module, and the energy management module. Compared with the prior art, the embodiment of the present invention realizes real-time monitoring and data analysis of building energy consumption through the Internet of Things technology, can promptly detect abnormal energy consumption, and provide an accurate basis for energy consumption optimization control; generates a scientific and reasonable energy consumption optimization control strategy, realizes intelligent control of equipment in the building, reduces energy waste, and improves energy utilization efficiency; centrally manages and schedules the building's energy consumption, optimizes the allocation of energy resources, ensures the normal operation of the building and the optimized control of energy consumption, and achieves the goal of energy conservation and emission reduction. The embodiment of the present invention can effectively reduce building energy consumption, reduce energy waste, achieve the goal of energy conservation and emission reduction, and has significant economic and social benefits.

[0108] At the same time, the embodiment of the present invention also adds a preprocessing module to preprocess the energy consumption data and environmental parameters, thereby improving the accuracy and reliability of the data and further improving the accuracy and reliability of the energy consumption optimization control strategy.

[0109] In addition, the embodiment of the present invention adds a user interaction module to facilitate users to view energy consumption data and control the operating status of the equipment, while allowing users to manually intervene and set, thereby enhancing the flexibility of the system and user experience.

[0110] Example 3

[0111] The embodiment of the present invention provides an intelligent building energy consumption optimization control method based on the Internet of Things, such as Figure 3 Shown, including:

[0112] S301. Collect energy consumption data and environmental parameters of the building to be optimized in real time.

[0113] S302: Generate an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters.

[0114] S303: Control the equipment in the building to be optimized according to the energy consumption optimization control strategy, and centrally manage and dispatch energy consumption.

[0115] It should be noted here that the detailed description of each step of this embodiment can refer to other embodiments and will not be repeated here.

[0116] An embodiment of the present invention provides an intelligent building energy consumption optimization and control method based on the Internet of Things, including: real-time collection of energy consumption data and environmental parameters of the building to be optimized; generating an energy consumption optimization control strategy based on the energy consumption data and the environmental parameters; controlling the equipment in the building to be optimized according to the energy consumption optimization control strategy, and centrally managing and scheduling energy consumption. Compared with the existing technology, the embodiment of the present invention realizes real-time monitoring and data analysis of building energy consumption through the Internet of Things technology, can timely discover abnormal energy consumption, and provide an accurate basis for energy consumption optimization and control; generates a scientific and reasonable energy consumption optimization and control strategy, realizes intelligent control of equipment in the building, reduces energy waste, and improves energy utilization efficiency; centrally manages and schedules the energy consumption of the building, optimizes the allocation of energy resources, ensures the normal operation of the building and the optimization control of energy consumption, and achieves the goal of energy conservation and emission reduction; can effectively reduce building energy consumption, reduce energy waste, achieve the goal of energy conservation and emission reduction, and has significant economic and social benefits.

[0117] Example 4

[0118] The embodiment of the present invention provides an intelligent building energy consumption optimization control method based on the Internet of Things, such as Figure 4 As shown, including:

[0119] S401. Collect energy consumption data and environmental parameters of the building to be optimized in real time.

[0120] S402: Preprocess the energy consumption data and the environmental parameters.

[0121] S403: Generate an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters.

[0122] S4031. Model the energy consumption data using a machine learning algorithm to obtain an energy consumption model, that is, establish a relationship model between the energy consumption data and time, and between the energy consumption data and the environmental parameters.

[0123] Among them, appropriate machine learning algorithms (such as linear regression, decision tree, random forest, neural network, etc.) are selected to process energy consumption data. These algorithms can automatically learn rules and patterns from the data.

[0124] S4032: Based on the established energy consumption model, predict the energy consumption data change trend within a preset time period in the future to obtain a prediction result.

[0125] S4033. Formulate the energy consumption optimization control strategy according to the prediction result.

[0126] S404: Control the equipment in the building to be optimized according to the energy consumption optimization control strategy, and centrally manage and dispatch energy consumption.

[0127] S405: Check the energy consumption data, the environmental parameters, the energy consumption optimization control strategy, and the operating status of the equipment in the building to be optimized, and perform manual intervention and settings.

[0128] It should be noted here that the detailed description of each step of this embodiment can refer to other embodiments and will not be repeated here.

[0129] An embodiment of the present invention provides an intelligent building energy consumption optimization and control method based on the Internet of Things, including: real-time collection of energy consumption data and environmental parameters of the building to be optimized; generating an energy consumption optimization control strategy based on the energy consumption data and the environmental parameters; controlling the equipment in the building to be optimized according to the energy consumption optimization control strategy, and centrally managing and scheduling energy consumption. Compared with the existing technology, the embodiment of the present invention realizes real-time monitoring and data analysis of building energy consumption through the Internet of Things technology, can timely discover abnormal energy consumption, and provide an accurate basis for energy consumption optimization and control; generates a scientific and reasonable energy consumption optimization and control strategy, realizes intelligent control of equipment in the building, reduces energy waste, and improves energy utilization efficiency; centrally manages and schedules the energy consumption of the building, optimizes the allocation of energy resources, ensures the normal operation of the building and the optimization control of energy consumption, and achieves the goal of energy conservation and emission reduction; can effectively reduce building energy consumption, reduce energy waste, achieve the goal of energy conservation and emission reduction, and has significant economic and social benefits.

[0130] At the same time, the embodiment of the present invention also pre-processes the energy consumption data and environmental parameters, thereby improving the accuracy and reliability of the data and further improving the accuracy and reliability of the energy consumption optimization control strategy.

[0131] In addition, the embodiment of the present invention can view energy consumption data, environmental parameters, energy consumption optimization control strategies and the operating status of equipment in the building to be optimized, and perform manual intervention and settings, making it convenient for users to view energy consumption data and control the operating status of equipment, while allowing users to perform manual intervention and settings, thereby enhancing the flexibility of the system and user experience.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them. 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 make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. The intelligent building energy consumption optimization control system based on the Internet of Things is characterized by: include: Data acquisition module, strategy generation module, control execution module, energy management module and communication module; The data acquisition module is used to collect energy consumption data and environmental parameters of the building to be optimized in real time; The strategy generation module is connected to the data acquisition module and is used to generate an energy consumption optimization control strategy based on the energy consumption data and the environmental parameters; The control execution module is connected to the strategy generation module and is used to control the equipment in the building to be optimized according to the energy consumption optimization control strategy; The energy management module is connected to the strategy generation module and is used to centrally manage and schedule the energy consumption of the building to be optimized according to the energy consumption optimization control strategy; The communication module is connected to the data acquisition module, the strategy generation module, the control execution module, and the energy management module respectively, and is used for data transmission and communication among the data acquisition module, the strategy generation module, the control execution module, and the energy management module.

2. The intelligent building energy consumption optimization control system based on the Internet of Things according to claim 1 is characterized in that: Also includes: Preprocessing module; The preprocessing module is connected to the data acquisition module, the strategy generation module, and the communication module respectively, and is used to preprocess the energy consumption data and the environmental parameters; the preprocessing includes but is not limited to data cleaning, denoising, and formatting.

3. The intelligent building energy consumption optimization control system based on the Internet of Things according to claim 1 is characterized in that: Also includes: User interaction module; The user interaction module is respectively connected to the data acquisition module, the strategy generation module, the control execution module, the energy management module, and the communication module, and is used for allowing the user to view the energy consumption data, the environmental parameters, the energy consumption optimization control strategy, and the operating status of the equipment in the building to be optimized, and to perform manual intervention and settings.

4. The intelligent building energy consumption optimization control system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module includes: The data acquisition module includes an energy consumption acquisition unit and an environmental parameter acquisition unit; The energy consumption collection unit is used to collect the energy consumption data of the building to be optimized in real time; The environmental parameter acquisition unit is used to acquire the environmental parameters of the building to be optimized in real time.

5. The intelligent building energy consumption optimization control system based on the Internet of Things according to claim 4 is characterized in that: The energy consumption collection unit includes: The energy consumption collection unit includes but is not limited to electricity meters, water meters and gas meters.

6. The intelligent building energy consumption optimization control system based on the Internet of Things according to claim 4 is characterized in that: The environmental parameter acquisition unit includes: The environmental parameter acquisition unit includes but is not limited to a temperature sensor, a humidity sensor, and a light sensor.

7. The energy consumption optimization control method of intelligent buildings based on the Internet of Things is characterized by: include: Real-time collection of energy consumption data and environmental parameters of the building to be optimized; generating an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters; The equipment in the building to be optimized is controlled according to the energy consumption optimization control strategy, and energy consumption is centrally managed and scheduled.

8. The method for optimizing energy consumption of intelligent buildings based on the Internet of Things according to claim 7 is characterized in that: After collecting the energy consumption data and environmental parameters of the building to be optimized in real time, it also includes: The energy consumption data and the environmental parameters are preprocessed; the preprocessing includes but is not limited to data cleaning, denoising and formatting.

9. The method for optimizing energy consumption of smart buildings based on the Internet of Things according to claim 7, characterized in that: After controlling the equipment in the building to be optimized according to the energy consumption optimization control strategy and centrally managing and scheduling energy consumption, the method further includes: Check the energy consumption data, the environmental parameters, the energy consumption optimization control strategy and the operating status of the equipment in the building to be optimized, and perform manual intervention and settings.

10. The method for optimizing energy consumption of intelligent buildings based on the Internet of Things according to claim 7, characterized in that: Generating an energy consumption optimization control strategy according to the energy consumption data and the environmental parameters, including: Modeling the energy consumption data using a machine learning algorithm to obtain an energy consumption model, that is, establishing a relationship model between the energy consumption data and time, and between the energy consumption data and the environmental parameters; Based on the established energy consumption model, predict the energy consumption data change trend within a preset time period in the future to obtain a prediction result; The energy consumption optimization control strategy is formulated according to the prediction results.

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