Intelligent hotel energy-saving management method and system
By deploying sensors in hotels, establishing energy consumption models and comfort models, and formulating and implementing intelligent energy saving strategies, the problem of hotel energy waste is solved, and precise energy management and continuous energy saving effects are achieved.
Patent Information
- Application Number
- CN202510546735.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-28
- Publication Date
- 2025-06-13
AI Technical Summary
Due to the lack of accurate monitoring and analysis of real-time energy consumption, the hotel industry is unable to dynamically adjust its energy supply, resulting in the widespread existence of energy waste.
By deploying a variety of sensors in various areas of the hotel, collecting environmental data and equipment energy consumption data in real time, establishing environmental comfort models and equipment energy consumption models, combining real-time data analysis, formulating optimal energy-saving strategies, and implementing these strategies through intelligent control systems.
Accurate control of equipment in various areas of the hotel has been achieved, energy waste caused by manual negligence is reduced, energy saving strategies are optimized through machine learning algorithms, and energy saving effects are continuously improved.
Smart Images
Figure CN120140900A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of hotel energy-saving management, and in particular to an intelligent hotel energy-saving management method and system. Background Art
[0002] At present, the hotel industry consumes a lot of energy and has extensive management. Traditional energy-saving measures are difficult to meet the growing demand for energy conservation. On the one hand, there is a lack of accurate monitoring and analysis of the hotel's real-time energy consumption, and it is impossible to dynamically adjust the energy supply according to the actual environment and guest needs; on the other hand, the failure to effectively integrate various equipment and systems has led to widespread energy waste. For example, when no one is in the guest room, air conditioning, lighting and other equipment continue to operate; the lighting and ventilation systems in public areas are not reasonably adjusted according to passenger flow and environmental conditions.
[0003] Therefore, there is an urgent need for an intelligent energy-saving management method and system to solve these problems. Summary of the invention
[0004] The present invention provides an intelligent hotel energy-saving management method and system, which are used to promote the solution of the problems mentioned in the above background technology.
[0005] The present invention provides the following technical solution: an intelligent hotel energy-saving management method, comprising the following steps:
[0006] Step 1: Deploy a variety of sensors and connect energy-consuming devices in various areas of the hotel to collect environmental data and equipment energy consumption data in real time;
[0007] Step 2: Establish environmental comfort models for different areas, where the room temperature comfort range satisfies the formula Tmin≤Troom≤Tmax, Tmin and Tmax are the lowest and highest comfort values of the room temperature, respectively, and Troom is the real-time room temperature; establish equipment energy consumption model, such as air conditioning energy consumption Eac=Pac*tac*f(Tset-Troom), where Pac is the rated power of the air conditioner, tac is the air conditioner operation time, and f(Tset-Troom) is a coefficient function related to the difference between the set temperature Tset and the real-time temperature Troom;
[0008] Step 3: Collect data in real time, compare the environmental data with the environmental comfort model, and evaluate the energy consumption cost under different energy-saving strategies in combination with the equipment energy consumption model, such as adjusting the energy consumption of the lighting system Elight = Plight*tlight*g(Lset-Lroom), where Plight is the power of the lighting equipment, tlight is the operating time of the lighting equipment, and g(Lset-Lroom) is a coefficient function related to the difference between the set light intensity Lset and the real-time light intensity Lroom; formulate the optimal energy-saving strategy based on the analysis results;
[0009] Step 4: The intelligent control system converts the energy-saving strategy into control instructions, sends them to the corresponding devices for execution, and monitors the operating status of the devices in real time;
[0010] Step 5: Regularly summarize and analyze the energy consumption data, and use machine learning algorithms to optimize the environment and energy consumption models as well as the energy-saving strategies.
[0011] Preferably, the multiple sensors include a human infrared sensor, a light sensor, a temperature sensor, and a humidity sensor.
[0012] Preferably, the environmental comfort model and the device energy consumption model are dynamically adjusted according to the actual situation and historical data of the hotel.
[0013] Preferably, the energy-saving strategy includes adjusting the operating parameters, switching states, etc. of the devices to achieve efficient utilization of energy.
[0014] The present invention also discloses an intelligent hotel energy-saving management system, including:
[0015] A data acquisition module, which is used to collect data of various sensors and devices in the hotel and transmit them to the data processing center;
[0016] A modeling and analysis module, which constructs an environmental comfort model and a device energy consumption model and analyzes the real-time data;
[0017] A strategy decision-making module, which formulates the optimal energy-saving strategy according to the data analysis results;
[0018] A device control module, which receives the instructions from the strategy decision-making module and performs intelligent control on the energy-consuming devices;
[0019] An optimization learning module, which conducts long-term analysis on the energy consumption data and uses machine learning algorithms to optimize the models and strategies.
[0020] Preferably, the data acquisition module is communicatively connected to multiple sensors and energy-consuming devices to ensure real-time and accurate data acquisition.
[0021] Preferably, the modeling and analysis module updates the environmental comfort model and the device energy consumption model according to the real-time data.
[0022] Preferably, the strategy decision-making module formulates the energy-saving strategy by combining the preset rules and goals and comprehensively considering the energy consumption cost and environmental comfort.
[0023] Preferably, the device control module communicates with the energy-consuming devices through the network to achieve remote control of the devices.
[0024] Preferably, the optimization learning module adopts machine learning algorithms to continuously improve the effectiveness and adaptability of the energy-saving strategies.
[0025] The present invention has the following beneficial effects:
[0026] 1. For the intelligent hotel energy-saving management method and system of the present invention, by constructing an environment and energy consumption model and analyzing it in combination with real-time data, it can accurately control the equipment in various areas of the hotel; various sensors are used to monitor the hotel environment and personnel activities in real time. When there is no one in the guest room, the system automatically turns off equipment such as air conditioners and lights, avoiding energy waste caused by human negligence in traditional hotels.
[0027] 2. For the intelligent hotel energy-saving management method and system of the present invention, the optimization and learning module of the system uses machine learning algorithms to regularly analyze energy consumption data, and continuously optimizes the environment and energy consumption model as well as energy-saving strategies. Over time, the system can better adapt to the operational changes of the hotel and continuously improve the energy-saving effect. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 is a schematic flow chart of the method of the present invention;
[0029] Figure 2 is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0031] Example 1, referring to Figure 1 , an intelligent hotel energy-saving management method, includes the following steps:
[0032] Step 1: Deploy a variety of sensors and connect energy consumption devices in various areas of the hotel to collect environmental data and equipment energy consumption data in real time; the variety of sensors include passive infrared sensors, light sensors, temperature sensors, and humidity sensors.
[0033] Step 2: Establish an environmental comfort model for different areas. The temperature comfort range in the guest room satisfies the formula Tmin ≤ Troom ≤ Tmax, where Tmin and Tmax are the minimum and maximum comfort values of the guest room temperature respectively, and Troom is the real-time guest room temperature. Establish an equipment energy consumption model. For example, the air-conditioning energy consumption Eac = Pac * tac * f(Tset - Troom), where Pac is the rated power of the air conditioner, tac is the operating time of the air conditioner, and f(Tset - Troom) is a coefficient function related to the difference between the set temperature Tset and the real-time temperature Troom. The environmental comfort model and the equipment energy consumption model are dynamically adjusted according to the actual situation and historical data of the hotel. The energy-saving strategy includes adjusting the operating parameters, switch states, etc. of the equipment to achieve efficient energy utilization.
[0034] Step 3: Collect data in real time, compare the environmental data with the environmental comfort model, and evaluate the energy consumption cost under different energy-saving strategies in combination with the equipment energy consumption model. For example, the energy consumption of the lighting system adjustment Elight = Plight * tlight * g(Lset - Lroom), where Plight is the power of the lighting equipment, tlight is the operating time of the lighting equipment, and g(Lset - Lroom) is a coefficient function related to the difference between the set lighting intensity Lset and the real-time lighting intensity Lroom. Formulate the optimal energy-saving strategy according to the analysis results.
[0035] Step 4: Convert the energy-saving strategy into a control instruction through the intelligent control system, send it to the corresponding equipment for execution, and monitor the operating status of the equipment in real time.
[0036] Step 5: Regularly summarize and analyze the energy consumption data, and use machine learning algorithms to optimize the environment and energy consumption models as well as the energy-saving strategies.
[0037] Example 2, referring to Figure 2 , an intelligent hotel energy-saving management system, including:
[0038] A data acquisition module for collecting data of various sensors and equipment in the hotel and transmitting it to the data processing center. The data acquisition module is communicatively connected to a variety of sensors and energy consumption equipment to ensure real-time and accurate data acquisition.
[0039] A modeling and analysis module for constructing an environmental comfort model and an equipment energy consumption model and analyzing the real-time data. The modeling and analysis module updates the environmental comfort model and the equipment energy consumption model according to the real-time data.
[0040] A strategy decision-making module for formulating the optimal energy-saving strategy according to the data analysis results. The strategy decision-making module formulates the energy-saving strategy by combining preset rules and goals and comprehensively considering the energy consumption cost and environmental comfort.
[0041] The device control module receives instructions from the policy decision-making module and intelligently controls the energy-consuming devices; the device control module communicates with the energy-consuming devices through a network to achieve remote control of the devices.
[0042] The optimization learning module conducts long-term analysis on the energy consumption data and optimizes the models and strategies using machine learning algorithms. The optimization learning module adopts machine learning algorithms to continuously improve the effectiveness and adaptability of the energy-saving strategies.
[0043] Embodiment III
[0044] Install a human body infrared sensor, a temperature sensor, a light sensor, and a smart socket in the guest room. When the guest leaves the guest room, the human body infrared sensor detects the unoccupied state, and the system automatically turns off non-essential devices such as the air conditioner and lighting according to the environmental comfort model and the device energy consumption model. If the temperature in the guest room exceeds the comfortable range, the system can calculate the optimal operating parameters of the air conditioner according to the energy consumption model to reduce energy consumption on the premise of ensuring the comfort of the environment when the guest returns.
[0045] Install light sensors and human body infrared sensors in public areas such as the hotel lobby and corridors. During the day, when the light sensor detects sufficient ambient light, the system automatically dims or turns off some lighting devices. At night, according to the passenger flow detected by the human body infrared sensor, the operating states of the lighting and ventilation systems are dynamically adjusted. For example, when the passenger flow is small, the operating power of the ventilation equipment and the lighting brightness are reduced.
[0046] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.
[0047] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.
Claims
1. A smart hotel energy-saving management method, characterized in that: The following steps are involved: Step 1: Deploy a variety of sensors and connect energy-consuming devices in various areas of the hotel to collect environmental data and equipment energy consumption data in real time; Step 2: Establish environmental comfort models for different areas, where the room temperature comfort range satisfies the formula Tmin≤Troom≤Tmax, Tmin and Tmax are the lowest and highest comfort values of the room temperature, respectively, and Troom is the real-time room temperature; establish equipment energy consumption model, such as air conditioning energy consumption Eac=Pac*tac*f(Tset-Troom), where Pac is the rated power of the air conditioner, tac is the air conditioner operation time, and f(Tset-Troom) is a coefficient function related to the difference between the set temperature Tset and the real-time temperature Troom; Step 3: Collect data in real time, compare environmental data with the environmental comfort model, and evaluate the energy consumption cost under different energy-saving strategies in combination with the equipment energy consumption model, such as adjusting the energy consumption of the lighting system △Elight = Plight*tlight*g(Lset-Lroom), where Plight is the power of the lighting equipment, tlight is the operating time of the lighting equipment, and g(Lset-Lroom) is a coefficient function related to the difference between the set light intensity Lset and the real-time light intensity Lroom; formulate the optimal energy-saving strategy based on the analysis results; Step 4: The energy-saving strategy is converted into control instructions through the intelligent control system, sent to the corresponding equipment for execution, and the equipment operation status is monitored in real time; Step 5: Regularly summarize and analyze energy consumption data, and use machine learning algorithms to optimize environmental and energy consumption models and energy-saving strategies.
2. A smart hotel energy-saving management method according to claim 1, characterized in that: The multiple sensors include a human infrared sensor, a light sensor, a temperature sensor and a humidity sensor.
3. A smart hotel energy-saving management method according to claim 1, characterized in that: The environmental comfort model and the equipment energy consumption model are dynamically adjusted according to the actual situation and historical data of the hotel.
4. The smart hotel energy saving management method according to claim 1, characterized in that: The energy-saving strategy includes adjusting the operating parameters, switch status, etc. of the equipment to achieve efficient use of energy.
5. An intelligent hotel energy-saving management system, applied to the intelligent hotel energy-saving management method according to any one of claims 1 to 4, characterized in that: include: Data acquisition module, used to collect data from various sensors and devices in the hotel and transmit it to the data processing center; Modeling and analysis module, which builds environmental comfort model and equipment energy consumption model, and analyzes real-time data; Strategy decision module, which formulates the optimal energy-saving strategy based on data analysis results; The equipment control module receives instructions from the strategy decision module and performs intelligent control on energy-consuming equipment; The optimization learning module conducts long-term analysis of energy consumption data and uses machine learning algorithms to optimize models and strategies.
6. The intelligent hotel energy-saving management system according to claim 5, characterized in that: The data acquisition module is connected to various sensors and energy consumption equipment to ensure real-time and accurate data collection.
7. The intelligent hotel energy-saving management system according to claim 5, characterized in that: The modeling and analysis module updates the environmental comfort model and the equipment energy consumption model according to real-time data.
8. The intelligent hotel energy-saving management system according to claim 5, characterized in that: The strategy decision module combines preset rules and goals, and comprehensively considers energy consumption costs and environmental comfort to formulate energy-saving strategies.
9. The intelligent hotel energy-saving management system according to claim 5, characterized in that: The device control module communicates with the energy-consuming device through a network to achieve remote control of the device.
10. The intelligent hotel energy-saving management system according to claim 5, characterized in that: The optimization learning module adopts a machine learning algorithm to continuously improve the effectiveness and adaptability of energy-saving strategies.