Energy-saving optimization method for office buildings based on big data

Abstract

The present invention provides an office building energy-saving optimization method based on big data, which belongs to the field of environmental monitoring technology, and includes: judging the activity area of the personnel according to the position of the personnel in the building, and determining the temperature distribution in the activity area. Setting a target temperature value, adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most of the personnel and the temperature value at the edge of the activity area meets the target value. Adjusting the size of the activity area and the wind direction of the air outlet according to the changes in the personnel. The office building energy-saving optimization method based on big data provided by the present invention is based on personnel and adjusts the activity area in a targeted manner, which greatly improves the economic level, reduces energy consumption, and saves usage costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and more specifically, relates to an office building energy-saving optimization method based on big data. Background Art

[0002] A building's energy consumption depends on three factors: first, the thermal insulation performance of the building envelope, including the exterior walls, doors, windows, and roof. Second, the operational efficiency of the equipment systems, including their selection, operation, and management. Finally, the thermal demands of the building's users, including their requirements for indoor thermal environments such as temperature and humidity. The performance of the building envelope and the thermal demands of the users jointly determine the energy consumption required for various building services, including heating, air conditioning, ventilation, and lighting. The efficiency of the equipment systems determines the amount of energy used to meet these demands.

[0003] At present, public buildings have a large number of people and a high mobility of people. In order to ensure comfort, multiple air conditioning equipment usually needs to be installed. These devices often need to run at high power throughout the day, which undoubtedly increases the cost of use. Moreover, when the number of people changes, the equipment often cannot be adjusted in time, resulting in serious waste of resources. Summary of the Invention

[0004] The purpose of the present invention is to provide an office building energy-saving optimization method based on big data, aiming to solve the problems of high cost of air conditioning equipment in buildings, inability to adjust in time, and serious waste of resources.

[0005] To achieve the above objectives, the present invention adopts a technical solution: to provide an office building energy-saving optimization method based on big data, comprising:

[0006] Determining the activity area of a person based on their location within the building, and determining the temperature distribution within the activity area;

[0007] Setting a target temperature value, and adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the largest number of people and the temperature value at the edge of the activity area meets the target value;

[0008] The size of the activity area and the wind direction of the air outlet are adjusted according to the changes in the number of people.

[0009] In a possible implementation, determining the temperature distribution within the active area includes:

[0010] The data collected by thermal sensors at multiple different locations are integrated to determine the temperature distribution of each point in the active area.

[0011] In a possible implementation, determining the temperature distribution of each point in the active area includes:

[0012] The data collected by the thermal sensing probe is calibrated by a distance measuring sensor, and the data measured by the plurality of thermal sensing probes after position calibration are integrated to obtain the temperature distribution of the active area.

[0013] In a possible implementation, the distance measuring sensor performs position calibration on the data collected by the thermal probe:

[0014] Determine the location of each of the persons and draw a boundary line that includes all of the persons;

[0015] A buffer value is set, and the buffer value is extended outward on the boundary line to obtain the active area.

[0016] In a possible implementation, adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value includes:

[0017] According to the specifications and type of the building and in combination with historical data, a plurality of air outlet ducts and air return ducts are installed on the periphery of the activity area, and the air outlet is arranged at the end of the air outlet duct;

[0018] The corresponding air outlet duct and the air return duct are opened or closed by a control valve.

[0019] In a possible implementation, opening or closing the corresponding air outlet duct and the air return duct by controlling the valve includes:

[0020] By opening and closing the corresponding air outlet duct and the air return duct, the gas discharged from the air outlet can be recovered by the nearest air return duct after covering the largest number of people.

[0021] In a possible implementation, opening or closing the corresponding air outlet duct and the air return duct by controlling the valve includes:

[0022] After running for a period of time, the gas is recovered by the air outlet pipe and discharged by the air return pipe.

[0023] In a possible implementation, adjusting the size of the activity area and the wind direction of the air outlet according to the change of the personnel includes:

[0024] Determine the location of each person in real time and adjust the activity area accordingly;

[0025] By adjusting the temperature and wind speed and opening the corresponding air outlet duct and the air return duct, the temperature value at the edge of the activity area meets the target value.

[0026] In a possible implementation, adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value includes:

[0027] Analyze the size of the cone for directly adjusting the gas according to the wind speed and temperature of the air outlet;

[0028] The wind speed and temperature of the air outlet are adjusted so that the cone can cover the most people.

[0029] In a possible implementation, adjusting the wind speed and temperature of the air outlet so that the cone can cover the most people includes:

[0030] determining the temperature and wind speed at a location where the cone contacts the person based on the position of the cone relative to the person;

[0031] Based on the principles of economy and comfort, the wind speed and temperature of the air outlet are adjusted to adjust the cone.

[0032] The beneficial effect of the big data-based office building energy-saving optimization method provided by the present invention is that, compared with existing technologies, the present method first determines the activity zone of personnel based on their location within the building and then determines the temperature distribution within the activity zone. To achieve the desired temperature, a target temperature value is set, and the outlet temperature and wind speed are adjusted to ensure that the exhaust gas from the outlet reaches the maximum number of people in the activity zone and that the temperature at the edge of the activity zone meets the target value.

[0033] The activity area will change according to the changes in the position of the personnel. After the activity area changes, the wind direction of the air outlet is adjusted to ensure the comfort of the activity area. In this application, the activity area is adjusted based on the personnel, which greatly improves the economic level, reduces energy consumption, and saves usage costs. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0035] Figure 1 A flowchart of an office building energy-saving optimization method based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0037] See also Figure 1 The office building energy-saving optimization method based on big data provided by the present invention is now described. The office building energy-saving optimization method based on big data includes:

[0038] The activity area of personnel is determined according to their positions in the building, and the temperature distribution in the activity area is determined.

[0039] Set the target temperature value, and adjust the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value.

[0040] Adjust the size of the activity area and the wind direction of the air outlet according to the changes in personnel.

[0041] The beneficial effect of the big data-based office building energy-saving optimization method provided by the present invention is that, compared with existing technologies, the present method first determines the activity zone of personnel based on their location within the building and then determines the temperature distribution within the activity zone. To achieve the desired temperature, a target temperature value is set, and the outlet temperature and wind speed are adjusted to ensure that the exhaust gas from the outlet reaches the maximum number of people in the activity zone and that the temperature at the edge of the activity zone meets the target value.

[0042] The activity area will change according to the changes in the position of the personnel. After the activity area changes, the wind direction of the air outlet is adjusted to ensure the comfort of the activity area. In this application, the activity area is adjusted based on the personnel, which greatly improves the economic level, reduces energy consumption, and saves usage costs.

[0043] To meet energy efficiency requirements, the aforementioned design standards must specify and control the performance of the building envelope according to different climate zones. The "Public Building Energy Efficiency Design Standard" stipulates energy-efficient design requirements for heating, ventilation, and air conditioning. These mandatory provisions specify the performance parameters of each component of the building envelope, such as the building's form factor, window-to-wall area ratio, heat transfer coefficient of the building envelope, and the sealing performance of exterior windows and open balconies in residential buildings. When a designed building fully meets these mandatory performance parameters, it is considered energy-efficient.

[0044] When calculating the energy consumption of a designed building and a reference building, in addition to the building envelope, information regarding the building's occupants' lifestyles and equipment systems is also required as a basis for calculation. Under these conditions, if the designed building's energy consumption index is lower than that of the reference building, the building can be considered to meet the design standard energy efficiency requirements.

[0045] Because human needs for a living environment often differ from the external climate, humans require spaces that offer shelter from the elements, warmth from cold, and protection from heat to mitigate the adverse effects of the external climate system. However, buildings alone cannot fully provide a comfortable indoor thermal environment; they must rely on other equipment and systems to meet human comfort needs, which leads to building energy consumption. This is the energy consumption attribute of buildings. Therefore, in addition to mitigating the external climate to provide a good living environment, buildings should also be designed to minimize energy consumption. Therefore, good building energy-saving design can effectively narrow the gap between climate and human comfort needs, thereby reducing building energy consumption.

[0046] From the perspective of architectural design, this requires architects to fully utilize design standards to improve the energy-saving performance of buildings. To accurately judge the quality of a building's energy-saving design, it is necessary to evaluate its energy-saving performance. This requires clarifying the building's contribution to the overall energy efficiency of the building, that is, the building's own energy-saving rate.

[0047] In this application, it is necessary to calculate the heat level input into the building and the heat transfer between the environment and the building. By calculating the change in heat, it is determined how much heat needs to be added or removed from the building to achieve heat balance. The ultimate goal is to maintain heat balance, because after heat balance, comfort can be guaranteed and, more importantly, economic efficiency can be improved.

[0048] However, for larger buildings, the temperature may vary throughout the building, and even if the heat balance can be maintained, the uneven heat distribution may cause people to have a weak sense of heat and poor comfort.

[0049] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, determining the temperature distribution within the activity area includes:

[0050] The data collected by thermal sensors at multiple locations are integrated to determine the temperature distribution of each point in the active area.

[0051] Traditional temperature sensors can only detect the temperature at a specific location. However, for public buildings of a certain size, or even large ones, the large spatial span inevitably leads to large temperature differences throughout the space. When the external ambient temperature is high, the temperature at the window will also be high, while the temperature at the air conditioner outlet will be low. If the indoor environmental parameters are adjusted based solely on heat balance, the transfer of heat takes a certain amount of time, and the current environmental conditions are constantly changing. This will ultimately cause the temperature inside the building to deviate from the target temperature. Furthermore, the corresponding temperature will also vary depending on the location within the building. If the temperature of a single location is used as the basis for regulating the entire building, due to this one-sidedness, it will not be able to effectively guarantee comfort and economic efficiency.

[0052] In order to accurately judge the temperature conditions in the entire building space, multiple thermal sensors can be installed at the top, bottom and circumference of the building. Each thermal sensor is used to determine the temperature conditions within a certain range. However, since the thermal sensor can only measure the temperature conditions within a certain distance and a certain range, in order to analyze the overall temperature conditions of the building, in this application, the data collected by the thermal sensors at different positions are integrated, and the ultimate goal is to obtain the temperature conditions of all areas in the building space.

[0053] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, determining the temperature distribution of each point in the activity area includes:

[0054] The data collected by the thermal probe is calibrated by the distance measuring sensor, and the data measured by multiple thermal probes after position calibration are integrated to obtain the temperature distribution of the active area.

[0055] Once the thermal sensor is installed and fixed, the range it can detect is determined. In order to determine the temperature conditions at various locations within the building space, the depth information of the same range can be determined based on the range detected by the thermal sensor.

[0056] For more detailed explanation, a ranging sensor can be bound to each thermal probe. The range captured by the ranging sensor is the same as the range detected by the thermal probe. The ranging sensor can obtain the depth information of each area within the current range, and the thermal probe can obtain a thermal map of the same range as the ranging sensor. By integrating the depth information and the thermal map, the temperature values at different positions at the current angle can be determined.

[0057] By integrating the thermal maps measured by thermal sensors installed at different locations and the depth information measured at different locations, the temperature conditions at various locations in the building can be roughly determined. Based on the above-determined temperature conditions, intuitive data is provided for subsequent environmental adjustments.

[0058] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, the data collected by the thermal probe is calibrated using a ranging sensor:

[0059] Determine the location of each person and draw a boundary line that includes all people.

[0060] Set the buffer value and extend the buffer value outward on the boundary line to obtain the active area.

[0061] The buffer value is a distance value. By setting the buffer value, people are prevented from leaving the activity area after walking a short distance, thus ensuring the effectiveness of the adjustment.

[0062] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value includes:

[0063] Based on the specifications and type of the building and combined with historical data, multiple outlet ducts and return air ducts are installed around the periphery of the activity area, and the air outlets are set at the ends of the outlet ducts.

[0064] Open or close the corresponding air outlet and return ducts through the control valve.

[0065] In existing technologies, heat loss and other aspects of a building can be determined through relevant measurements and calculations. To ensure that the building's environmental parameters meet comfort requirements, the operating conditions of the environmental conditioning equipment must be set to corresponding levels based on the target ideal environmental parameters and daily experience. The environmental conditioning equipment then adjusts the building's environment to the set standards. However, it should be noted that existing equipment is equipped with a temperature sensor that can only detect the temperature at a specific location. More importantly, the equipment is typically placed in a corner of a building, while people tend to spend most of their time in the middle of the building. Consequently, the environmental conditioning effect of the equipment cannot be quickly perceived, or the equipment may over-regulate the environment.

[0066] In order to solve the above problems, in this application, multiple air outlet ducts are installed at different locations of the building, and multiple air outlet ducts are connected to the air conditioning equipment. A control valve is installed on each air outlet duct. According to the situation of people in the building, the corresponding air outlet duct is closed and specific air outlet ducts are opened, so as to achieve targeted adjustment of specific environmental areas.

[0067] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, opening or closing corresponding air outlet ducts and return air ducts by controlling valves includes:

[0068] By opening and closing the corresponding air outlet and return air ducts, the gas discharged from the air outlet can be recovered by the nearest return air duct after covering the most people.

[0069] To regulate building environmental parameters, existing air conditioners are installed in specific locations within the building. Floor-standing or wall-mounted air conditioners typically have only one outlet. Larger buildings require increased cooling and heating capacity to maintain a comfortable temperature. However, this approach increases power consumption, and the temperature difference between areas farther from the outlet and those closer to it can be significant, making comfort difficult to achieve. While existing central air conditioners can have multiple outlets, they lack the ability to achieve directional air flow, resulting in performance similar to that of conventional air conditioners.

[0070] This application improves upon existing air outlet methods by increasing the number of outlet ducts, with multiple outlet ducts primarily located around crowded areas. More importantly, a return duct is also installed on either side of the outlet duct. These two ducts are typically positioned opposite each other. When one outlet duct discharges processed air, the opposite return duct absorbs the air, completing the gas cycle.

[0071] More importantly, in order to ensure that the treated gas can have a good effect on the flow of people, the outlet and return ducts at specific locations are opened and closed so that the most gas is blown toward the flow of people, while the corresponding outlet and return ducts in areas where people have not been are closed. In this way, the load on the equipment is greatly reduced.

[0072] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, opening or closing corresponding air outlet ducts and return air ducts by controlling valves includes:

[0073] After running for a period of time, the gas is recovered by the outlet duct and discharged by the return air duct.

[0074] Taking summer as an example, the outlet duct discharges cooler air, while the return duct absorbs warmer air. The area covered by the air discharged from the outlet duct can be viewed as a gradually increasing cone. The further the air is from the outlet duct, the higher its temperature becomes, and the smaller its effect on temperature regulation becomes. This results in lower comfort the further away from the outlet duct.

[0075] To address this issue, after both the outlet and return ducts have been operating for a period of time, the return duct exhausts air while the outlet duct absorbs it. This reversal lowers the temperature at the return duct. Due to the shift in the position of the cooling cone, the building's internal environmental conditioning effect is amplified, improving comfort. To achieve this, two diverter pipes are connected between the return and outlet ducts. Each diverter pipe is equipped with a control valve. Each diverter pipe has two connection points with the return and outlet ducts, respectively. Auxiliary valves are installed between these two connection points on the outlet duct and the return duct. Normally, the two auxiliary valves are open, and the control valves on the diverter pipes are closed. When a change of direction is required, the auxiliary valves close, and the control valves on the diverter pipes open, redirecting the air.

[0076] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, adjusting the size of the activity area and the wind direction of the air outlet according to the changes in personnel includes:

[0077] Determine the location of each person in real time and adjust the activity area accordingly.

[0078] By adjusting the temperature and wind speed and opening the corresponding outlet and return air ducts, the temperature value at the edge of the activity area meets the target value.

[0079] In this application, multiple thermal sensors are provided, and the location of a person and his or her own temperature can be identified through the thermal sensors and other equipment. More importantly, the thermal sensors can be used to determine the impact of the gas discharged from the air outlet duct on the surrounding environment.

[0080] To explain in more detail, the thermal sensor first determines the current activity area of a person. Then, the temperature at the edge of the activity area and other environmental parameters are used as the standard for adjustment, setting a target value. Next, the environmental parameters of the activity area are adjusted based on the principle that the air discharged from the air duct can cover the activity area. When the parameters at the edge of the activity area reach the target value, the adjustment is completed and subsequent maintenance is carried out.

[0081] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value includes:

[0082] The size of the cone that directly adjusts the gas is determined based on the wind speed and temperature at the air outlet.

[0083] Adjust the wind speed and temperature of the air outlet so that the cone can cover the most people.

[0084] The reality is that people are always in motion, which means that the gas discharged from the air duct may not be able to continuously cover the activity area. The higher the wind speed of the gas discharged from the air duct, the more area the treated gas can cover. Based on the above considerations, it is necessary to dynamically adjust the wind speed of the air duct and the temperature of the discharged gas.

[0085] In order to achieve the above effects, the location information of people is obtained in real time through thermal sensors, and then the activity area is generated based on the location information. It should be pointed out that the number of outlet and return air ducts that need to be opened needs to be determined based on the position of the people relative to the building and the density of the people, as well as the parameters such as the wind speed of the outlet duct.

[0086] In some embodiments of the office building energy-saving optimization method based on big data provided in this application, adjusting the wind speed and temperature of the air outlet so that the cone can cover the most people includes:

[0087] Based on the position of the cone relative to the person, the temperature and wind speed at the point where the cone contacts the person are determined.

[0088] Based on the principles of economy and comfort, the wind speed and temperature of the air outlet are adjusted to adjust the cone.

[0089] The current activity area and position of personnel can be determined through thermal sensors and other equipment. The position of each air outlet duct remains relatively fixed. The direction of the air outlet of the air outlet duct has also been determined. Different wind speeds of the air outlet duct correspond to different cones. The higher the wind speed, the larger the corresponding cone.

[0090] The specific adjustment method is to first set the target value, which is the lowest standard, and then determine the activity area of the personnel. According to the position and distance of the personnel from the corresponding air outlet duct, the exhaust gas temperature and wind speed required by the air outlet duct are judged so that the edge of the activity area meets the target value. At this time, the internal position of the activity area is better than the target value, and the cone can cover the most people.

[0091] In one embodiment, if the number of people is relatively dense, the wind speed of the air outlet pipe can be increased accordingly. If the number of people is relatively dispersed, the wind speed of the air outlet pipe can be adjusted. Finally, in order to make the edge of the activity area meet the requirements, the wind speed and temperature can be adjusted at the same time.

[0092] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The office building energy-saving optimization method based on big data is characterized by: include: Determining the activity area of a person based on their location within the building, and determining the temperature distribution within the activity area; Setting a target temperature value, and adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the largest number of people and the temperature value at the edge of the activity area meets the target value; Adjusting the size of the activity area and the wind direction of the air outlet according to the changes in the personnel; Determining the temperature distribution within the active area includes: Integrate the data collected by thermal sensors at multiple locations to determine the temperature distribution of each point in the active area; Determining the temperature distribution of each point in the active area includes: The data collected by the thermal sensing probe is calibrated by a distance measuring sensor, and the data measured by the plurality of thermal sensing probes after position calibration are integrated to obtain the temperature distribution of the active area; The adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value includes: According to the specifications and type of the building and in combination with historical data, a plurality of air outlet ducts and air return ducts are installed on the periphery of the activity area, and the air outlet is arranged at the end of the air outlet duct; Open or close the corresponding air outlet duct and the air return duct by controlling the valve; The opening or closing of the corresponding air outlet pipe and the air return pipe by controlling the valve includes: By opening and closing the corresponding air outlet duct and the air return duct, the gas discharged from the air outlet can be recovered by the nearest air return duct after covering the largest number of people; The opening or closing of the corresponding air outlet pipe and the air return pipe by controlling the valve includes: After running for a period of time, the gas is recovered by the air outlet pipe and discharged by the air return pipe.

2. The office building energy-saving optimization method based on big data according to claim 1, characterized in that: The distance measuring sensor is used to calibrate the position of the data collected by the thermal probe: Determine the location of each of the persons and draw a boundary line that includes all of the persons; A buffer value is set, and the buffer value is extended outward on the boundary line to obtain the active area.

3. The office building energy-saving optimization method based on big data according to claim 1, characterized in that: The adjusting the size of the activity area and the wind direction of the air outlet according to the change of the personnel includes: Determine the location of each person in real time and adjust the activity area accordingly; By adjusting the temperature and wind speed and opening the corresponding air outlet duct and the air return duct, the temperature value at the edge of the activity area meets the target value.

4. The office building energy-saving optimization method based on big data according to claim 3, characterized in that: The adjusting the temperature and wind speed of the air outlet so that the gas discharged from the air outlet can cover the most people and the temperature value at the edge of the activity area meets the target value includes: Analyze the size of the cone for directly adjusting the gas according to the wind speed and temperature of the air outlet; The wind speed and temperature of the air outlet are adjusted so that the cone can cover the most people.

5. The office building energy-saving optimization method based on big data according to claim 4, characterized in that: The adjusting of the wind speed and temperature of the air outlet so that the cone can cover the most people includes: determining the temperature and wind speed at a location where the cone contacts the person based on the position of the cone relative to the person; Based on the principles of economy and comfort, the wind speed and temperature of the air outlet are adjusted to adjust the cone.

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