Lighting and air conditioner control method based on intelligent thin glass and related equipment

Abstract

The invention discloses an illumination and air conditioner control method based on intelligent thin glass and related equipment. The method comprises the following steps that outdoor temperature data, indoor temperature data, a first electricity price at the current control moment and performance parameters of the thin glass installed on a building are obtained; the performance parameters comprise visible light transmittance, a solar heat gain coefficient and a heat transfer coefficient; based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient and the heat transfer coefficient, a building heat model is constructed, discretization processing of the building heat model is carried out, and a building heat target model at the current control moment is obtained; based on the first electricity price and the visible light transmittance, determining an action constraint of the building thermal target model; and based on a preset comfort constraint, a preset equipment constraint and an action constraint, the building heat target model is operated, and the illumination brightness and the air conditioner power at the current control moment are obtained. The method can be widely applied to the technical field of glass control.

Description

Technical Field

[0001] This invention relates to the field of glass control technology, and in particular to a lighting and air conditioning control method and related equipment based on smart thin glass. Background Technology

[0002] Currently, energy conservation in building exterior windows is a crucial aspect of building energy conservation. Existing energy-saving solutions for building exterior windows mainly include controlling energy consumption through low-emissivity coated glass, electrochromic windows, thermochromic materials, and passive transparency control using thermochromic hydrogels.

[0003] Existing thermotropic materials mostly rely on passive switching of temperature thresholds or optical sensing feedback control, but lack in-situ and quantitative perception of the material's internal phase state. Meanwhile, the photothermal state of windows is rarely deeply coupled with predictive optimization of HVAC systems and lighting, resulting in the energy-saving potential not being fully realized. Therefore, there are still technical problems that need to be solved in related technologies. Summary of the Invention

[0004] The purpose of this application is to at least partially solve one of the technical problems existing in the prior art.

[0005] Therefore, one objective of this application is to provide a lighting and air conditioning control method and related equipment based on smart thin glass, which can improve the energy efficiency of HVAC systems and lighting systems.

[0006] To achieve the above-mentioned technical objectives, the technical solution adopted in this application includes: a lighting and air conditioning control method based on smart thin glass, comprising: acquiring outdoor temperature data, indoor temperature data, a first electricity price at the current control time, and performance parameters of the thin glass installed on the building; the performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient; constructing a building thermal model based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient, and discretizing the building thermal model to obtain a building thermal target model at the current control time; determining the action constraints of the building thermal target model based on the first electricity price and the visible light transmittance; and running the building thermal target model based on preset comfort constraints, preset equipment constraints, and the action constraints to obtain the lighting brightness and air conditioning power at the current control time.

[0007] In addition, the lighting and air conditioning control method based on smart thin glass according to the above embodiments of the present invention may also have the following additional technical features:

[0008] Further, in this embodiment of the application, the step of constructing a building thermal model based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient, and discretizing the building thermal model to obtain the building thermal target model at the current control time, includes:

[0009] The outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient are input into a preset building thermal function set to obtain the building thermal model; the building thermal function set is as follows:

[0010]

[0011] The building thermal model is discretized to obtain the building thermal target model at the current control time; the building thermal target model is as follows:

[0012]

[0013] In the above formulas (1), (2), (3), and (4), C represents the building heat capacity, and T represents the building heat capacity. i For indoor temperature data, T o For outdoor temperature data, A w For the window area, I sol Q represents the light intensity. int For indoor heat generation, Q HVAC For cooling capacity, when Q HVAC When P is negative, it represents the heat output. HVAC The power rating is for the air conditioner, and the COP is the energy efficiency ratio of the air conditioner. The heat transfer coefficient is... T is the solar heat gain coefficient. i [k] represents the indoor temperature data at the current moment, T o [k] represents the outdoor temperature data at the current moment, where k is the current moment, I sol [k] represents the light intensity at the current moment, Q int [k] represents the indoor heat production at the current moment, Q HVAC [k] represents the cooling capacity at the current moment, P HVAC [k] represents the current cooling or heating power of the air conditioner. The heat transfer coefficient at the current moment is... T represents the solar heat gain coefficient at the current moment. i [k+1] represents the indoor temperature data for the next time step after the current time, where k+1 represents the time step after the current time.

[0014] Furthermore, in this embodiment of the application, obtaining the performance parameters of the thin glass installed on the building specifically includes:

[0015] Measure the dielectric constant of thin glass installed in buildings;

[0016] The dielectric constant of the glass is corrected to obtain the corrected target dielectric constant.

[0017] The water phase volume fraction is determined based on the target dielectric constant.

[0018] The phase change progress is determined based on the water phase volume fraction.

[0019] Based on the phase transition progress, the performance parameters of the thin glass are determined.

[0020] Furthermore, in this embodiment of the application, determining the action constraints of the building thermal target model based on the first electricity price and the visible light transmittance includes:

[0021] The first electricity price and the visible light transmittance are input into the action constraint formula to obtain the action constraints of the building thermal target model; wherein the action constraint formula is:

[0022]

[0023] In the above formula (5), P HVAC [k] represents the current cooling or heating power of the air conditioner, or P. HVAC [k] can be any parameter between the compressor frequency and the indoor temperature setpoint. P is the glare penalty factor. light [k] represents the current illumination power, T* represents the temperature setpoint, and p k The electricity price at the current moment. As an indoor thermal comfort penalty factor, It is the visible light transmittance T of the window at the current moment. vis The calculated illumination index.

[0024] Furthermore, in this embodiment of the application, determining the volume fraction of the aqueous phase based on the target dielectric constant specifically includes:

[0025] The target dielectric constant is input into the formula for calculating the volume fraction of the aqueous phase to obtain the volume fraction of the aqueous phase, wherein the formula for calculating the volume fraction of the aqueous phase is:

[0026]

[0027] In formula (6), Where is the dielectric constant of water. The dielectric constant of the hydrogel is The target dielectric constant is... It represents the volume fraction of the aqueous phase.

[0028] Furthermore, in this embodiment of the application, determining the phase change progress based on the volume fraction of the aqueous phase includes:

[0029] The volume fraction of the aqueous phase is input into the first formula to obtain the phase transition progress; wherein the first formula is:

[0030]

[0031] in, For the phase transition progress, clip() is the clipping constraint algorithm. It represents the volume fraction of the aqueous phase. This represents the volume fraction of the aqueous phase in winter, which is a preset value. This represents the volume fraction of the aqueous phase in summer, which is a preset value.

[0032] Furthermore, in this embodiment of the application, determining the performance parameters of the thin glass based on the phase transition progress includes:

[0033] Visible light transmittance, solar heat gain coefficient, and heat transfer coefficient were determined through a single joint calibration, specifically obtained using the first set of formulas:

[0034]

[0035] In the above formulas (7), (8), (9), and (10), a0, a1, a2, b0, b1, b2, g0, U0, and u1 are set values. For the phase transition progress, Visible light transmittance, Weighted transmittance of the sun For solar heat gain coefficient, is the heat transfer coefficient.

[0036] On the other hand, embodiments of this application also provide a lighting and air conditioning control system based on smart thin glass, including:

[0037] The acquisition unit is used to acquire outdoor temperature data, indoor temperature data, the first electricity price at the current control moment, and the performance parameters of the thin glass installed on the building; the performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient.

[0038] The first processing unit is used to construct a building thermal model based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient, and to discretize the building thermal model to obtain the building thermal target model at the current control time.

[0039] The second processing unit is used to determine the action constraints of the building thermal target model based on the first electricity price and the visible light transmittance.

[0040] The third processing unit is used to run the building thermal target model based on preset comfort constraints, preset equipment constraints, and the action constraints to obtain the lighting brightness and air conditioning power at the current control moment.

[0041] On the other hand, this application also provides a lighting and air conditioning control device based on smart thin glass, comprising:

[0042] At least one processor;

[0043] At least one memory for storing at least one program;

[0044] When the at least one program is executed by the at least one processor, the at least one processor implements a lighting and air conditioning control method based on smart thin glass as described in any one of the inventions.

[0045] In addition, this application also provides a computer-readable storage medium storing processor-executable instructions, which, when executed by a processor, are used to perform a lighting and air conditioning control method based on smart thin glass as described in any of the preceding claims.

[0046] The advantages and beneficial effects of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application:

[0047] This application utilizes outdoor temperature data, indoor temperature data, the current electricity price at the time of control, and performance parameters of thin glass installed on the building. These performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient. Based on these data, a building thermal model is constructed and discretized to obtain the building thermal target model at the current time of control. Based on the current electricity price and visible light transmittance, the action constraints of the building thermal target model are determined. Based on preset comfort constraints, preset equipment constraints, and action constraints, the building thermal target model is run to obtain the lighting brightness and air conditioning power at the current time of control. This application uses outdoor temperature data, indoor temperature data, the current electricity price at the time of control, and the performance parameters of thin glass installed on the building as the basis for air conditioning control. Compared to traditional projects, it incorporates parameters such as ambient temperature, thereby improving the energy efficiency of both the HVAC system and the lighting system. Attached Figure Description

[0048] Figure 1This is a schematic diagram illustrating the steps of a lighting and air conditioning control method based on smart thin glass in a specific embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the structure of smart thin glass in a specific embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the installation of electrodes on a window in a specific embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of a lighting and air conditioning control system based on smart thin glass in a specific embodiment of the present invention;

[0052] Figure 5 This is a flowchart illustrating a lighting and air conditioning control method based on smart thin glass in another specific embodiment of the present invention.

[0053] Figure 6 This is a flowchart illustrating the model predictive control algorithm in a specific embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram of a lighting and air conditioning control device based on smart thin glass in another specific embodiment of the present invention;

[0055] Figure 8 This is a flowchart illustrating the dynamic programming algorithm in a specific embodiment of the present invention. Detailed Implementation

[0056] The following detailed description, in conjunction with the accompanying drawings, illustrates the principles and processes of the lighting and air conditioning control method and related equipment based on smart thin glass according to the embodiments of the present invention.

[0057] This application provides a lighting and air conditioning control method based on smart thin glass. (Refer to...) Figure 1 The detection method may include steps S101-S104.

[0058] S101. Acquire outdoor temperature data, indoor temperature data, the first electricity price at the current control moment, and the performance parameters of the thin glass installed on the building; the performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient.

[0059] S102. Based on outdoor temperature data, indoor temperature data, solar heat gain coefficient, and heat transfer coefficient, construct a building thermal model and discretize the building thermal model to obtain the building thermal target model at the current control time.

[0060] S103. Based on the first electricity price and visible light transmittance, determine the action constraints of the building thermal target model.

[0061] S104. Based on preset comfort constraints, preset equipment constraints, and motion constraints, run the building thermal target model to obtain the lighting brightness and air conditioning power at the current control moment.

[0062] Furthermore, in some feasible embodiments of this application, a building thermal model is constructed based on outdoor temperature data, indoor temperature data, solar heat gain coefficient, and heat transfer coefficient, and the building thermal model is discretized to obtain the building thermal target model at the current control time, including:

[0063] The building thermal model is obtained by inputting outdoor temperature data, indoor temperature data, solar heat gain coefficient, and heat transfer coefficient into a preset set of building thermal functions; the building thermal function set is as follows:

[0064]

[0065] The building thermal model is discretized to obtain the building thermal target model at the current control moment; the building thermal target model is as follows:

[0066]

[0067] In the above formulas (1), (2), (3), and (4), C represents the building heat capacity, and T represents the building heat capacity. i For indoor temperature data, T o For outdoor temperature data, A w For the window area, I sol Q represents the light intensity. int For indoor heat generation, Q HVAC For cooling capacity, when Q HVAC When P is negative, it represents the heat output. HVAC The power rating is for the air conditioner, and the COP is the energy efficiency ratio of the air conditioner. The heat transfer coefficient is... T is the solar heat gain coefficient. i [k] represents the indoor temperature data at the current moment, T o [k] represents the outdoor temperature data at the current moment, where k is the current moment, I sol [k] represents the light intensity at the current moment, Q int [k] represents the indoor heat production at the current moment, Q HVAC [k] represents the cooling capacity at the current moment, P HVAC [k] represents the current cooling or heating power of the air conditioner. The heat transfer coefficient at the current moment is... T represents the solar heat gain coefficient at the current moment. i [k+1] represents the indoor temperature data for the next time step after the current time, where k+1 represents the time step after the current time.

[0068] Furthermore, in some feasible embodiments of this application, the step of obtaining the performance parameters of the thin glass installed on the building specifically includes steps S201-S205.

[0069] S201. Measure the dielectric constant of thin glass installed on a building.

[0070] S202. Correct the dielectric constant of the glass to obtain the corrected target dielectric constant.

[0071] S203. Determine the volume fraction of the aqueous phase based on the target dielectric constant.

[0072] S204. Determine the phase change progress based on the volume fraction of the aqueous phase.

[0073] S205. Determine the performance parameters of thin glass based on the phase transition progress.

[0074] Furthermore, in some feasible embodiments of this application, the action constraints of the building thermal target model are determined based on the first electricity price and visible light transmittance; including:

[0075] Inputting the first electricity price and visible light transmittance into the action constraint formula yields the action constraints for the building thermal target model; the action constraint formula is as follows:

[0076]

[0077] In the above formula (5), P HVAC [k] represents the current cooling or heating power of the air conditioner, or P. HVAC [k] can be any parameter between the compressor frequency and the indoor temperature setpoint. P is the glare penalty factor. light [k] represents the current illumination power, T* represents the temperature setpoint, and p k The electricity price at the current moment. As an indoor thermal comfort penalty factor, It is the visible light transmittance T of the window at the current moment. vis The calculated illumination index.

[0078] Furthermore, in some feasible embodiments of this application, the volume fraction of the aqueous phase is determined based on the target dielectric constant, specifically including:

[0079] Input the target dielectric constant into the formula for calculating the volume fraction of the aqueous phase to obtain the volume fraction of the aqueous phase. The formula for calculating the volume fraction of the aqueous phase is as follows:

[0080]

[0081] In formula (6), Where is the dielectric constant of water. The dielectric constant of the hydrogel is For the target dielectric constant, It represents the volume fraction of the aqueous phase.

[0082] Furthermore, in some feasible embodiments of this application, the phase transition progress is determined based on the volume fraction of the aqueous phase, including:

[0083] Input the volume fraction of the aqueous phase into the first formula to obtain the phase transition progress; where the first formula is:

[0084]

[0085] in, For the phase transition progress, clip() is the clipping constraint algorithm. It represents the volume fraction of the aqueous phase. This represents the volume fraction of the aqueous phase in winter, which is a preset value. This represents the volume fraction of the aqueous phase in summer, which is a preset value.

[0086] Furthermore, in some feasible embodiments of this application, the performance parameters of the thin glass are determined based on the phase transition progress, including:

[0087] Visible light transmittance, solar heat gain coefficient, and heat transfer coefficient were determined through a single joint calibration, specifically obtained using the first set of formulas:

[0088]

[0089] In the above formulas (7), (8), (9), and (10), a0, a1, a2, b0, b1, b2, g0, U0, and u1 are set values. For the phase transition progress, Visible light transmittance, Weighted transmittance of the sun For solar heat gain coefficient, is the heat transfer coefficient.

[0090] The following is in conjunction with the appendix Figure 5 and Figure 6 Explain the specific implementation principle of this application:

[0091] The structural composition and hardware implementation of this embodiment.

[0092] The system structure of this embodiment may include a laminated insulating glass unit, an electrode structure, a signal transmission circuit, an environmental sensing device, and a control execution platform.

[0093] Insulating glass laminated: Refer to Figure 2 ,exist Figure 2 The insulated glass unit consists of, in sequence, thin glass 1, barrier layer 2, thermotropic hydrogel 3, barrier layer 2, and thin glass 1 again. The thermotropic hydrogel 3 can be a composite multilayer hydrogel or a combination of hydrogel and other interlayers (inert gas, vacuum). Due to the large volume and weight of the hydrogel, using thin glass as the light-transmitting structure at both ends helps reduce the weight of the window, improving portability and transportation safety. The strength of the insulated structure can be increased by adding a cylindrical support column between the two thin glass panes.

[0094] Electrode structure: Long finger-shaped electrodes are arranged in the frame or adhesive layer area to improve robustness to geometric / thickness variations and reduce occlusion of the viewing area. (Refer to...) Figure 3 ,exist Figure 3 In the middle, the electrode structure has a pair of finger-shaped electrodes on each of the horizontal directions (left and right sides) of the window. Figure 3 The design of the finger electrode and its installation method on a window are shown.

[0095] Signal transmission circuit: Capacitor-to-digital converter chip (e.g., FDC2214 type, operating frequency band approximately 0.1–2 MHz), with active drive shielding at the front end (using operational amplifier circuit as non-inverting buffer).

[0096] Environmental sensing devices: sensors for indoor / outdoor temperature, relative humidity, incident irradiance, weather forecast data, and time-of-use electricity pricing. These devices are mainly used to collect the above data.

[0097] Control execution platform: Communicates with the building air conditioning automatic control system to dynamically adjust air conditioning and lighting control parameters.

[0098] This embodiment describes dielectric measurement and phase estimation.

[0099] Determine the relationship between capacitance and dielectric constant. Measure capacitance C0 in a glass cavity filled with vacuum or dry air, and then measure capacitance C1 after filling it with water (assuming its relative dielectric constant is known to be 76). The measured capacitance C and dielectric constant are then compared. The relationship is:

[0100]

[0101] Simultaneous measurement at the selected frequency point f (recommended 0.5–2 MHz) Figure 3 The capacitance between the first electrode 11 and the second electrode 12, between the third electrode 13 and the fourth electrode 14, and between the first electrode 11 and the second electrode 12 and the third electrode 13 and the fourth electrode 14 (when the first electrode 11 and the second electrode 12 are short-circuited, and the third electrode 13 and the fourth electrode 14 are short-circuited) is used to obtain the dielectric constant between the first electrode 11 and the second electrode 12. The dielectric constant between the third electrode 13 and the fourth electrode 14 The dielectric constants of the first electrode 11 and the second electrode 12, and the third electrode 13 and the fourth electrode 14, after short-circuiting are determined. Then, the total phase change volume fraction of the hydrogel is determined by the volume fraction of the water phase. The evaluation satisfies the following:

[0102]

[0103] The volume fraction of the aqueous phase can be calculated using the above formula. In the formula, the dielectric constant of water is... The dielectric constant is 70–80, with the specific value depending on the excitation frequency and the current temperature; the dielectric constant of a fully atomized hydrogel is... The value is 3–5, and the specific value can be adjusted.

[0104]

[0105] in, For the phase transition progress, clip() is the clipping constraint algorithm. It represents the volume fraction of the aqueous phase. This represents the volume fraction of the aqueous phase in winter, which is a preset value. This represents the volume fraction of the aqueous phase in summer, which is a preset value.

[0106] In reality, hydrogels are prone to uneven heat transfer during atomization, as well as the effects of border effects and dynamic heat transfer. Hydrogels are also prone to uneven phase transition progress between the periphery and the center, so the actual effective dielectric constant needs to be corrected.

[0107]

[0108] in, The actual effective dielectric constant is given, and after correction, it can be substituted back into the above formula for solution. E... eff E 12 and E 34 These three parameters are fitting parameters. They can be fitted experimentally or through simulation. If experimental data is unavailable, they can also be set. Specifically, they can be set to E. eff =0.86, E 12 =E 34 =0.07, using these three values ​​for the fitting parameters can achieve good accuracy. It should be noted that the Efit in experiments or simulations... eff E 12 and E 34 The sum of the three can be anything other than 1.

[0109] This embodiment can perform optical-thermal mapping and calibration to obtain visible light transmittance. Solar weighted transmittance Solar heat gain coefficient Heat transfer coefficient Parameters such as these.

[0110] This embodiment can establish visible light transmittance through a single joint calibration. Solar weighted transmittance Solar heat gain coefficient Heat transfer coefficient The empirical relationships are transformed into the following set of formulas, which can be used to determine the specific visible light transmittance. Solar weighted transmittance Solar heat gain coefficient Heat transfer coefficient The formula set is as follows:

[0111]

[0112] (If the impact is small, it can be approximated as U()=U0)

[0113] Typically, coefficients a0, a1, a2, b0, b1, b2, g1, U0, and u1 can be fitted experimentally, where g0 is the system coefficient. This is used in calculating visible light transmittance. Solar weighted transmittance Solar heat gain coefficient Heat transfer coefficient At this time, g0, a0, a1, a2, b0, b1, b2, g1, U0, and u1 can be preset values.

[0114] This embodiment can establish a building thermal model (2R1C simplification).

[0115] The initial model for the building thermal model is:

[0116]

[0117] Discretize the initial model described above with a sampling period of Δt to obtain the following discretized model:

[0118]

[0119] In the above formulas (11), (12), (13), and (14), C represents the building heat capacity, and T represents the building heat capacity. i For indoor temperature data, T o For outdoor temperature data, A w For the window area, I sol Q represents the light intensity. int For indoor heat generation, Q HVACFor cooling capacity, when Q HVAC When P is negative, it represents the heat output. HVAC The power rating is for the air conditioner, and the COP is the energy efficiency ratio of the air conditioner. The heat transfer coefficient is... T is the solar heat gain coefficient. i [k] represents the indoor temperature data at the current moment, T o [k] represents the outdoor temperature data at the current moment, where k is the current moment, I sol [k] represents the light intensity at the current moment, Q int [k] represents the indoor heat production at the current moment, Q HVAC [k] represents the cooling capacity at the current moment, P HVAC [k] represents the current cooling or heating power of the air conditioner. The heat transfer coefficient at the current moment is... T represents the solar heat gain coefficient at the current moment. i [k+1] represents the indoor temperature data for the next time step after the current time, where k+1 is the next time step after the current time, and T... i [k+1] and T i The time difference between [k] is Δt.

[0120] This embodiment can execute the Model Predictive Control (MPC) algorithm.

[0121] Reference Figure 6 The power cost and discomfort are optimized in N-step rolling time domain, and glare and equipment motion constraints can be added; the glare and equipment motion constraints are as shown in equation (15):

[0122]

[0123] In equation (15), P HVAC [k] represents the current cooling or heating power of the air conditioner, or P. HVAC [k] can be any parameter between the compressor frequency and the indoor temperature setpoint. P is the glare penalty factor. light [k] represents the current illumination power, T* represents the temperature setpoint, and p k The electricity price at the current moment. As an indoor thermal comfort penalty factor, It is the visible light transmittance T of the window at the current moment. vis The calculated illumination parameters. Specific constraints may also include comfort constraints and equipment constraints.

[0124] Comfort constraints are:

[0125]

[0126] In summer, Tmin can be 22 degrees Celsius and Tmax can be 30 degrees Celsius. In winter, Tmin can be 14 degrees Celsius and Tmax can be 22 degrees Celsius.

[0127] Equipment constraints may include equipment heat generation and inverter frequency. Specifically, equipment heat generation can be less than a first preset value and greater than a second preset value; the specific values ​​of the first and second preset values ​​can be adjusted according to different users and environments. The upper limit of the inverter frequency can be the preset frequency or 120% of the rated frequency, and the lower limit of the inverter frequency can be the preset frequency or 40% of the rated frequency. Additionally, the compressor ramp-up speed can be constrained to be less than 1000 RPM / s, and the minimum start-up / stop time of the air conditioner can be 180 seconds. Glare or illuminance constraints can satisfy the following formula:

[0128]

[0129] Subject to visible light transmittance T vis With light intensity I sol Impact of natural daylight With light E light Meets the set value .

[0130] Solver: The first air conditioning control quantity and the lighting control quantity are executed in a rolling manner through quadratic programming (QP) or nonlinear programming (NLP), and the air conditioning control quantity and the lighting control quantity for future time moments are updated. This is an existing technology and will not be described in detail here.

[0131] In some embodiments, refer to Figure 8 This embodiment can also execute the dynamic programming (DP) algorithm.

[0132] Given a day-ahead weather forecast and time-of-use electricity pricing constraints, time is discretized into stages t=1..T, and all discretized states are: The control function is The stage cost is the same as MPC, which is: The transfer function is derived from discrete RC. DP recursion:

[0133]

[0134] It is a solution state generator. For the set of all control possibilities, This is the stage cost function. The cost may include electricity prices, which can include the electricity prices for air conditioning and lighting.

[0135] To avoid excessive computation, this embodiment can adopt the following methods: ① coarse temperature grid (e.g., spacing 0.2℃), ② separate analytical compensation for lighting control (discrete the lighting into 3 values: 0, minimum power to satisfy illuminance constraints, and maximum power), ③ embed the COP curve in a lookup table manner, and ④ use a rule-based control (e.g., a threshold strategy based on marginal electricity price and solar heat gain) as the initial value, and then discretize the set air conditioning operating state into 3 values ​​(0, minimum satisfaction value, and maximum value) to accelerate the search.

[0136] The calibration, filtering, and robustness adjustment in this embodiment.

[0137] • Frequency selection: Use an LCR detector to sweep the frequency (10 kHz–5 MHz) to select the maximum frequency point, which can be 0.5–2 MHz. The excitation frequency is a parameter related to circuit design, electrode length, wire length, and dielectric selection, and must be selected through experimental determination.

[0138] • Long-term drift: Establish a quarterly recalibration process, or introduce online calibration (using the nighttime stable period as a reference to automatically calibrate stray capacitance and parasitic capacitance daily).

[0139] • Active shielding design. This embodiment demonstrates a design method that uses a 1x operational amplifier to track and buffer the measurement signal, and attaches a shielding plate to the back of the finger electrode. This confines the electric field lines within the glass, thereby enhancing the signal. This embodiment provides a design and control parameter reference. The specific parameters of this embodiment are:

[0140] The window dimensions are 1.2 m × 1.5 m; the hydrogel thickness is 10 mm.

[0141] The excitation frequency of the circuit is f = 2MHz.

[0142] The finger spacing of the wide electrode is 0.2 mm and the length is ≥0.8 m.

[0143] The data sampling period is Δt = 5 min.

[0144] Meteorological data forecast time domain N=24 (1-hour step); T* = 24℃, T min =23℃, T max =26 ℃.

[0145] Glare penalty factor =0.0065, thermal comfort penalty =0.25.

[0146] In equation (15), p kTime-of-use pricing is adopted. For example, the electricity price is RMB 1.2 per kilowatt-hour during peak hours, RMB 0.5 per kilowatt-hour during off-peak hours, and RMB 0.8 per kilowatt-hour during normal hours.

[0147] In addition, refer to Figure 4 ,and Figure 1 Corresponding to the method described above, this application also provides a lighting and air conditioning control system based on smart thin glass. The system may include: an acquisition unit 1001, a first processing unit 1002, a second processing unit 1003, and a third processing unit 1004. The acquisition unit 1001 can acquire outdoor temperature data, indoor temperature data, the first electricity price at the current control time, and performance parameters of the thin glass installed on the building; the performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient. The first processing unit 1002 can construct a building thermal model based on the outdoor temperature data, indoor temperature data, solar heat gain coefficient, and heat transfer coefficient, and perform discretization processing on the building thermal model to obtain the building thermal target model at the current control time. The second processing unit 1003 can determine the action constraints of the building thermal target model based on the first electricity price and visible light transmittance. The third processing unit 1004 can run the building thermal target model based on preset comfort constraints, preset equipment constraints, and action constraints to obtain the lighting brightness and air conditioning power at the current control time.

[0148] It should be noted that the acquisition unit can be any integrated circuit unit or microprocessor unit obtained by integrating a chip with processing functions and its peripheral circuits using existing integration technology. Similarly, the first processing unit and the second processing unit can also be any integrated circuit module or microprocessor module obtained by integrating a chip with processing functions and its peripheral circuits using existing integration technology. Furthermore, the first processing unit and the second processing unit may include one or more memories.

[0149] It should be noted that the content of the above-described embodiments of the lighting and air conditioning control method based on smart thin glass is applicable to this embodiment of the lighting and air conditioning control system based on smart thin glass. The specific functions implemented by this embodiment of the lighting and air conditioning control system based on smart thin glass are the same as those of the above-described embodiments of the lighting and air conditioning control method based on smart thin glass, and the beneficial effects achieved are also the same as those achieved by the above-described embodiments of the lighting and air conditioning control method based on smart thin glass.

[0150] and Figure 1 Corresponding to the method described above, this application also provides a lighting and air conditioning control device based on smart thin glass, the specific structure of which can be referred to... Figure 7 ,include:

[0151] At least one processor 1011;

[0152] At least one memory 1012 is used to store at least one program;

[0153] When the at least one program is executed by the at least one processor, the at least one processor implements the lighting and air conditioning control method based on smart thin glass.

[0154] The content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0155] and Figure 1 Corresponding to the method described above, embodiments of this application also provide a computer-readable storage medium storing processor-executable instructions, which, when executed by a processor, are used to perform the aforementioned lighting and air conditioning control method based on smart thin glass.

[0156] The contents of the above embodiments of the lighting and air conditioning control method based on smart thin glass are all applicable to this storage medium embodiment. The specific functions implemented by this storage medium embodiment are the same as those of the above embodiments of the lighting and air conditioning control method based on smart thin glass, and the beneficial effects achieved are also the same as those achieved by the above embodiments of the lighting and air conditioning control method based on smart thin glass.

[0157] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0158] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional technology for an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0159] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0161] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0162] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0163] In the foregoing description of this specification, the references to terms such as "one embodiment," "another embodiment," or "some embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0164] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0165] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for controlling lighting and air conditioning based on smart thin glass, characterized by, Includes the following steps: Acquire outdoor temperature data, indoor temperature data, the first electricity price at the current control moment, and performance parameters of the thin glass installed on the building; the performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient; Based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient, a building thermal model is constructed and the building thermal model is discretized to obtain the building thermal target model at the current control time. Based on the first electricity price and the visible light transmittance, determine the action constraints of the building thermal target model; Based on preset comfort constraints, preset equipment constraints, and the aforementioned action constraints, the building thermal target model is run to obtain the lighting brightness and air conditioning power at the current control moment.

2. The smart thin glass based lighting and air conditioning control method as claimed in claim 1, wherein, The process involves constructing a building thermal model based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient, and then discretizing the building thermal model to obtain the building thermal target model at the current control time, including: The outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient are input into a preset building thermal function set to obtain the building thermal model; the building thermal function set is as follows: The building thermal model is discretized to obtain the building thermal target model at the current control time; the building thermal target model is as follows: In the above formulas (1), (2), (3), and (4), C represents the building heat capacity, and T represents the building heat capacity. i For indoor temperature data, T o For outdoor temperature data, A w For the window area, I sol Q represents the light intensity. int For indoor heat generation, Q HVAC For cooling capacity, when Q HVAC When P is negative, it represents the heat output. HVAC The power rating is for the air conditioner, and the COP is the energy efficiency ratio of the air conditioner. The heat transfer coefficient is... T is the solar heat gain coefficient. i [k] represents the indoor temperature data at the current moment, T o [k] represents the outdoor temperature data at the current moment, where k is the current moment, I sol [k] represents the light intensity at the current moment, Q int [k] represents the indoor heat production at the current moment, Q HVAC [k] represents the cooling capacity at the current moment, P HVAC [k] represents the current cooling or heating power of the air conditioner. The heat transfer coefficient at the current moment is... T represents the solar heat gain coefficient at the current moment. i [k+1] represents the indoor temperature data for the next time step after the current time, where k+1 represents the time step after the current time.

3. The smart thin glass based lighting and air conditioning control method as claimed in claim 1, wherein, The acquisition of performance parameters of thin glass installed on buildings specifically includes: Measure the dielectric constant of thin glass installed in buildings; The dielectric constant of the glass is corrected to obtain the corrected target dielectric constant. The water phase volume fraction is determined based on the target dielectric constant. The phase change progress is determined based on the water phase volume fraction. Based on the phase transition progress, the performance parameters of the thin glass are determined.

4. The smart thin glass based lighting and air conditioning control method as claimed in claim 1, wherein, The determination of the action constraints for the building thermal target model based on the first electricity price and the visible light transmittance includes: The first electricity price and the visible light transmittance are input into the action constraint formula to obtain the action constraints of the building thermal target model; wherein the action constraint formula is: wherein in the above equation (5), P HVAC [k] is the power of the air conditioning system for cooling or heating at the current time, or P HVAC [k] is any one of the compressor frequency or the indoor temperature set point, is the glare penalty factor, P light [k] is the current time light power, T* is the temperature set point, p k is the current time electricity price, is the indoor thermal comfort penalty factor, is the light index calculated by the current time window visible light transmittance T vis .

5. The smart thin glass based lighting and air conditioning control method as claimed in claim 3, wherein, The determination of the water phase volume fraction based on the target dielectric constant specifically includes: The target dielectric constant is input into the formula for calculating the volume fraction of the aqueous phase to obtain the volume fraction of the aqueous phase, wherein the formula for calculating the volume fraction of the aqueous phase is: In equation (6), is the dielectric constant of water, is the dielectric constant of the hydrogel, is the target dielectric constant, is the volume fraction of the aqueous phase.

6. The smart thin glass based lighting and air conditioning control method as claimed in claim 3, wherein, The step of determining the phase transition progress based on the aqueous phase volume fraction includes: The volume fraction of the aqueous phase is input into the first formula to obtain the phase transition progress; wherein the first formula is: wherein, is the phase change progress, clip() is a clipping limit algorithm, is the volume fraction of the water phase, is the volume fraction of the water phase in winter, which is a preset value, is the volume fraction of the water phase in summer, which is a preset value.

7. The smart thin glass based lighting and air conditioning control method as claimed in claim 3, wherein, The process of determining the performance parameters of the thin glass based on the phase transition progress includes: Visible light transmittance, solar heat gain coefficient, and heat transfer coefficient were determined through a single joint calibration, specifically obtained using the first set of formulas: In the above equations (7), (8), (9), (10), a0, a1, a2, b0, b1, b2, g0, U0, and u1 are set values, is the progress of phase change, is the visible light transmittance, is the solar heat gain coefficient, is the solar heat gain coefficient, is the heat transfer coefficient.

8. A smart thin glass based lighting and air conditioning control system, characterized in that, include: The acquisition unit is used to acquire outdoor temperature data, indoor temperature data, the first electricity price at the current control moment, and the performance parameters of the thin glass installed on the building; The performance parameters include visible light transmittance, solar heat gain coefficient, and heat transfer coefficient. The first processing unit is configured to construct a building thermal model based on the outdoor temperature data, the indoor temperature data, the solar heat gain coefficient, and the heat transfer coefficient, and to discretize the building thermal model to obtain a building thermal target model at a current control time; The second processing unit is configured to determine an action constraint of the building thermal target model based on the first electricity price and the visible light transmittance. The third processing unit is configured to run the building thermal target model based on a preset comfort constraint, a preset device constraint, and the action constraint to obtain a lighting brightness and an air conditioning power at the current control time.

9. A smart thin glass based lighting and air conditioning control device, characterized by The method comprises: at least one processor; at least one memory configured to store at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the method for controlling lighting and air conditioning based on intelligent thin glass according to any one of claims 1-7.

10. A computer-readable storage medium having stored therein instructions that are executable by a processor, the instructions comprising: The instructions executable by the processor when executed by the processor are used to execute the method for controlling lighting and air conditioning based on intelligent thin glass according to any one of claims 1-7.

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