Integral building energy consumption simulation engine implementation method based on resistance and capacitance modeling

Abstract

The invention belongs to the technical field of building simulation, and provides an integral building energy consumption simulation engine implementation method based on resistance-capacitance modeling, which comprises the steps of building model construction, model parameter input, simulation mode determination, simulation calculation, parameter optimization and target energy consumption simulation result output. According to the invention, by setting a plurality of resistance-capacitance simulation modes, the defect of single applicability of the existing method is overcome, and reliable analog simulation of different building structures is realized; through the DE algorithm, fusion of the positive resistance-capacitance model and the inverse resistance-capacitance model is realized, and the simulation efficiency and optimization performance of the engine are improved.

Description

Technical Field

[0001] The invention relates to the technical field of building simulation, and in particular to a method for realizing an overall building energy consumption simulation engine based on resistance and capacitance modeling. Background Art

[0002] How can the architectural design and engineering team make fast and effective green and low-carbon building design decisions and apply energy-saving technologies while ensuring a good building living environment? This has become an indispensable part of achieving this goal. Among them, building load and energy consumption simulation calculations are essential methods and means to evaluate the energy consumption and carbon emission levels during the building operation stage. Architects and engineers use various models to evaluate and make decisions on building plans at different stages of design practice, involving various simulation and analysis processes, including physical models, full-size and scale models, analytical models, mathematical models, and computational models. Since the architectural design process is carried out in stages, simulation-assisted design also needs to intervene in different forms at different stages. The architectural design process can be divided into conceptual design, preliminary design, detailed design, and post-design stages. In the conceptual design and early design stages, as well as the energy-saving renovation stage after use, the research, design, and engineering needs related to the comparison and optimization of green building parametric schemes are increasing. Among them, the whole building heat load and energy consumption simulation tool is an important evaluation method in the process of building performance optimization. At present, mature energy consumption simulation software such as EnergyPlus requires detailed model input parameters and dynamically integrates all heat flows into the calculation. It uses transient methods to simulate and solve the cooling and heating loads of buildings to obtain reliable and detailed models and calculation results. However, when it comes to buildings in multiple areas, its use can be complicated and requires a lot of computing power. Most of the existing building heat load simulation engines require a lot of computing power and time to perform dynamic load simulation calculations, which is not conducive to exhaustive, comparing and optimizing a large number of design schemes, making it difficult for architects or engineers to conduct rapid and comprehensive scheme evaluation. In the simulation of building heat load, the method of simulating and calculating the heat transfer process by analogy with the resistance and capacitance of circuit elements is referred to as the resistance-capacitance model. Since this type of model is lower in complexity and computational intensity than the dynamic white box model, it can significantly improve the calculation efficiency and speed.

[0003] However, existing research on building load and energy consumption simulation of resistance-capacitance model mainly relies on inverse method, and there is less research on positive resistance-capacitance model. Existing related research is also mostly limited to the development of single thermal zone model, resulting in poor performance in rapid simulation calculation in the early stage of building design and optimization of existing building renovation. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling. By setting a variety of resistance and capacitance simulation modes, the defect of the single applicability of the existing method is solved, and reliable simulation of different building structures is achieved; through the DE algorithm, the fusion of the positive resistance and capacitance model and the reverse resistance and capacitance model is realized, thereby improving the simulation efficiency and optimization performance of the engine.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling, comprising:

[0007] Construct a building model and input model parameters to obtain a simulation model;

[0008] Determine a simulation mode according to the simulation model; the simulation modes include: 4R1C mode, 6R1C mode, 7R1C mode and 7R2C mode;

[0009] According to the simulation mode, the simulation model is simulated using the Euler method and the Crank-Nicolson method to obtain simulation node calculation results;

[0010] Optimizing the simulation parameters of the simulation model using the DE algorithm to obtain optimized parameters, and updating the optimized parameters to the simulation model;

[0011] When the calculation results of the simulation nodes meet the preset convergence criteria, the target energy consumption simulation results are output.

[0012] Preferably, the model parameters include: meteorological parameters, building geometry parameters, occupancy behavior parameters, building envelope parameters, infiltration or natural ventilation parameters, solar radiation parameters, convective heat transfer parameters, window absorption of solar radiation parameters, window shading parameters, HVAC system performance curve construction parameters, and on-site renewable energy production parameters.

[0013] Preferably, the calculation formula of the 4R1C mode includes:

[0014]

[0015] Among them, T o is the outdoor air temperature; T a is the internal air node; T m is the thermal mass node; R v is the ventilation thermal resistance; R win is the window thermal resistance; R im is the internal mass thermal resistance; R ex is the external opaque structure thermal resistance; Q hvacis the heat flux density of the building cooling and heating system; Q air is the internal air heat flux density; Q int is the load heat flux density in the building; Q sol is the solar heat gain heat flux density; C m is the heat capacity of thermal mass per unit building area.

[0016] Preferably, the calculation formula of the 6R1C mode includes:

[0017]

[0018] Among them, T az,i is the temperature of the neighboring area of ​​the ith area; R if,i is the thermal resistance of the base plate in the adjacent area of ​​the i-th area; R iw,i is the thermal resistance of the inner wall of the adjacent zone of zone i.

[0019] Preferably, the calculation formula of the 7R1C mode includes:

[0020]

[0021] Among them, R ia is the internal air thermal resistance; T s The central mass node.

[0022] Preferably, the calculation formula of the 7R2C mode includes:

[0023]

[0024] Among them, C s is the heat capacity of the thermal mass node at the center of the building area.

[0025] Preferably, the adaptation function of the DE algorithm is:

[0026]

[0027] Among them, PCE i is the predicted cooling energy consumption of the i-th iteration; OCE i is the observed cooling energy consumption of the i-th iteration; PHL i is the predicted heating energy consumption of the i-th iteration; OHL i is the observed heating energy consumption of the i-th iteration.

[0028] Preferably, the simulation parameters include: mutation rate, recombination rate, population multiplier, maximum iteration and convergence tolerance.

[0029] The present invention discloses the following technical effects:

[0030] The present invention solves the defect of single applicability of existing methods by setting multiple resistance and capacitance simulation modes, and realizes reliable simulation of different building structures; through the DE algorithm, the problem of poor performance of existing models in rapid simulation calculation in the early stage of building design and optimization of existing building renovation is solved, and the fusion of positive resistance and capacitance model and reverse resistance and capacitance model is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 creative labor.

[0032] Figure 1 A schematic diagram of the implementation process of the overall building energy consumption simulation engine based on resistance and capacitance modeling provided by an embodiment of the present invention;

[0033] Figure 2 A hierarchical modeling framework provided for an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a resistor-capacitor mode provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The purpose of the present invention is to provide a method for realizing an overall building energy consumption simulation engine based on resistance and capacitance modeling. By setting a variety of resistance and capacitance simulation modes, the defect of single applicability of the existing method is solved, and reliable simulation of different building structures is realized; through the DE algorithm, the fusion of the positive resistance and capacitance model and the reverse resistance and capacitance model is realized, thereby improving the simulation efficiency and optimization performance of the engine.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Figure 1 The schematic diagram of the implementation process of the overall building energy consumption simulation engine based on resistance and capacitance modeling provided by the embodiment of the present invention is as follows: Figure 1As shown, the present invention provides a method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling, comprising:

[0039] Step 100: construct a building model and input model parameters to obtain a simulation model;

[0040] Step 200: Determine a simulation mode according to the simulation model; the simulation modes include: 4R1C mode, 6R1C mode, 7R1C mode and 7R2C mode;

[0041] Step 300: According to the simulation mode, the simulation model is simulated using the Euler method and the Crank-Nicolson method to obtain simulation node calculation results;

[0042] Step 400: Optimizing simulation parameters of the simulation model using the DE algorithm to obtain optimized parameters, and updating the optimized parameters to the simulation model;

[0043] Step 500: When the simulation node calculation result meets the preset convergence standard, the target energy consumption simulation result is output.

[0044] Specifically, the model parameters include: meteorological parameters, building geometry parameters, occupancy behavior parameters, building envelope parameters, infiltration or natural ventilation parameters, solar radiation parameters, convective heat transfer parameters, window absorption of solar radiation parameters, window shading parameters, HVAC system performance curve construction parameters, and on-site renewable energy production parameters.

[0045] Further, forward modeling and model input sets include several key parameter types, each with a complex contribution to the overall thermal performance and energy use of the building:

[0046] 1) Meteorological parameters: standard weather files, such as the widely used EPW (EnergyPlus Weather) files.

[0047] 2) Occupancy behavior parameters: reflect the heterogeneity of user behavior habits in the model, including: space occupancy habits, HVAC system usage patterns, home appliance usage and lighting habits.

[0048] 3) Infiltration and natural ventilation parameters:

[0049] Infiltration is the leakage of air through the building envelope due to temperature differences (stack effect) and wind pressure differences. "True" Infiltration Method: The "True" Infiltration method combines the stack effect and wind-driven infiltration models. Stack Effect: The temperature difference between indoor and outdoor air causes buoyancy-driven airflow. It follows the orifice plate equation derived from Bernoulli's principle:

[0050] q v,stack =max(0.0146·F r ·finf ·(0.7·h·|T e -T set |) 0.667 ,0.001)

[0051] In the formula, q v,stack is the volume flow rate due to the chimney effect [m 3 / s],F r is the reference flow rate [m 3 / s],f inf is the infiltration fraction [-], h is the regional height [m], T e is the outdoor temperature [K], T set is the set temperature of the room [K]. The constant 0.7 is the average height of the building for the neutral pressure level.

[0052] The wind pressure difference also conforms to the orifice plate equation:

[0053]

[0054] In the formula, q v,wind is the volume flow rate under wind action [m 3 / s], ΔC p is the pressure coefficient difference [-], v site is the site specific factor [-], w s is the wind speed [m / s]. Pressure coefficient difference ΔC p Characterizes the average wind pressure distribution on the building facade.

[0055] The combined effects are constructed using the orthogonal sum method:

[0056]

[0057] The final permeation rate takes into account the additional pressure difference and ensures a non-negative value:

[0058] q v,inf =max(0,-q v,diff )+q v,sw

[0059] Among them, q v,inf Represents the differential pressure term.

[0060] The constant infiltration method uses a simplified approach based on a reference flow rate and an infiltration fraction:

[0061] q v,inf =F r ·f inf

[0062] When the ventilation method is not mechanical, activate the natural ventilation mode. Ventilation speed:

[0063]

[0064] Environmental factors: The model includes factors that consider the effects of wind and temperature on occupant window opening behavior. Wind factors:

[0065] Y wind =min(max(1-0.1·w s ,0),1)

[0066] Temperature factors:

[0067] Y temp =min(max(T e / 25.0+0.2,0),1)

[0068] Open factors:

[0069] R opw =Y wind ·Y temp

[0070] Natural ventilation rate: The natural ventilation rate is based on the orifice plate equation, taking into account the opening area and ventilation rate:

[0071]

[0072] In the formula, A ow is the opening area [m 2 ], A f is the building area [m 2 ].

[0073] Natural ventilation control: Includes a simple control strategy based on cooling demand and outdoor temperature:

[0074]

[0075] In the formula, q C For cooling needs, NV hrs is the number of hours of natural ventilation.

[0076] Total ventilation heat exchange: Total ventilation combines mechanical air supply, infiltration, and natural ventilation:

[0077] q v,tot =q v,supp +q v,inf +q v,NV

[0078] Ventilation heat transfer coefficient H ve Calculated based on air heat capacity and total ventilation volume:

[0079]

[0080] Where 1.2 is the volume heat capacity of air [kJ / (m 3 ·K)].

[0081] 4) Solar radiation parameters: After obtaining the hourly climate parameters, calculate the hourly direct, diffuse and reflected radiation, as well as the total radiation in all directions of the building, and combine the outdoor temperature, relative humidity, solar azimuth and altitude to calculate the solar radiation heat gain of the building in accordance with the ISO13790 standard. Sun position calculation:

[0082]

[0083] Among them, Y date : Indicates the cumulative date value; Y date,i-1 : Y date The previous value of , where the subscript i-1 indicates the previous iteration or time step; day i : The day of the current month; day i-1 : The previous day of the month; month i : Current month; month i-1 :last month.

[0084] The calculation of the solar time variable τ is as follows:

[0085]

[0086] Time equation ET:

[0087] ET=2.2918(0.0075+0.1868cosτ-3.2077sinτ-1.4615cos 2τ

[0088] -4.089sin 2τ)

[0089] Apparent solar time AST:

[0090]

[0091] Where LSM is the local standard time meridian, longitude is longitude, and hour is the current time. Sun angle: Solar declination δ:

[0092]

[0093] Hour angle θ h :

[0094] θ h =15(AST-12)

[0095] Sun altitude angle β:

[0096] sinβ=cosφcosδcosθh +sinφsinδ

[0097] where φ is the latitude;

[0098] Solar azimuth ψ:

[0099]

[0100] Surface solar radiation calculation, surface solar position γ:

[0101] γ=|ψ-ψ surface

[0102] Angle of incidence θ:

[0103] cosθ=cosβcosγsinσ+sinβcosσ

[0104] Here, σ is the surface tilt angle.

[0105] Direct irradiation E tb :

[0106]

[0107] In the formula, E d is the diffuse horizontal irradiance.

[0108] Diffuse component E t,d :

[0109] Y=max(0.45,0.55+0.437cosθ+0.313cos 2 θ

[0110]

[0111] Among them, E d is the diffuse horizontal irradiance.

[0112] Ground reflection component E t,r :

[0113]

[0114] In the formula, r g is the ground reflectivity.

[0115] Total solar radiation:

[0116] GSR=E t,b +E t,d +E t,r

[0117] Solar radiation on inclined surfaces:

[0118]

[0119] Among them, E gh is the global horizontal irradiance.

[0120] 5) Convective heat transfer parameters: These parameters significantly affect the prediction accuracy of the model. The common method for determining these parameters is an empirical formula, which can use a fixed value or a basic linear variation function.

[0121] 6) Window shading parameters: The heat of solar radiation inside the building is closely related to the shading parameters of the windows. This parameter is affected by the window material and the solar incidence angle. Most studies regard windows as pure resistance without fully considering the shading parameters. Therefore, this embodiment considers the indoor solar radiation heat gain separately and integrates the shading parameters similar to the heat exchange parameters into the energy consumption simulation algorithm. This embodiment calculates the solar reduction coefficient of various shading devices, excess:

[0122]

[0123] Vertical shading:

[0124]

[0125] Horizontal shading:

[0126]

[0127] Where i is the angle of the shading device.

[0128] 7) HVAC system performance curve construction parameters: The core of this embodiment is the simplified performance curve method of the HVAC (heating, ventilation and air conditioning) system. Regarding the primary energy consumption of heating and cooling, the performance curve method is adopted. The user needs to input the energy efficiency of the heating and cooling sources at intervals of partial load conditions. This method simulates the energy performance of the heating and cooling system under different load conditions. After receiving the energy efficiency input for each partial load stage, linear interpolation is used to simulate the performance curve of the system. This method is intended to simplify the model input. In addition, if the HVAC size details are not provided, it is assumed that the system meets the heating and cooling needs of all areas. If the heating or cooling load exceeds the system capacity, the indoor temperature is calculated by the energy-saving control equation. This embodiment calculates the energy consumption of heating, cooling, ventilation and domestic hot water (DHW) for each thermal zone. Supply air flow, supply air flow for heating and cooling:

[0129]

[0130] Where V h,supply and V c,supply are respectively heating and cooling air flow [m 3 / h / m 2 ], qH and q C Heating air demand [W / m 2 ], ρ is the air density [kg / m 3 ], T supply,H and T supply,C is the heating air supply temperature [℃], T air,hc is the regional air temperature [℃], ΔT reheat is the reheat temperature difference [℃].

[0131] Fan energy consumption:

[0132] V fan =max(V h,supply ,V c,supply )+V exh

[0133] E fan =max(V fan ) fan f control f BAC,e

[0134] Where V exh is the exhaust air flow rate, P fan is the fan power [W / (m 3 / s)],f control is the fan flow control factor, f BAC,e is the energy consumption factor of building automation and control (BAC).

[0135] Pump energy consumption, water flow rate and pump volume for heating and cooling:

[0136]

[0137] Where V w,h and V w,c Heating and cooling water flow [m 3 / h],ρ w is the density of water, c p,w is the specific heat capacity of water, ΔT w,H and ΔT w,C is the temperature difference between the heated and cooled water. The pump volume is then calculated based on the pump control strategy.

[0138] Calculate the efficiency of your heating and cooling systems using performance curves:

[0139] eta heat =PLV h (q H,HVAC / q H,max )·η heat,nom

[0140] COP cool=PLV c (q C,HVAC / q C,max )·COP cool,nom

[0141] Among them, PLV h and PLV c is the partial load value curve for heating and cooling, η heat,nom and COP cool,nom is the nominal efficiency.

[0142] Considering the distribution losses and final energy consumption:

[0143]

[0144] f dem,cool =max(1-f dem,heat ,0.1

[0145]

[0146] Among them, f dem,cool Indicates the cooling demand ratio; f dem,heat is the heating demand ratio; η dist,heat and η dist,cool is the heating and cooling distribution efficiency; a cool and a heat is the overall heating and cooling loss coefficient; f waste Indicates the energy waste ratio.

[0147] Final energy consumption for heating and cooling:

[0148]

[0149] Among them, q H,loss and q C,loss is the distribution loss, and is the BAC coefficient for heating and cooling.

[0150] Domestic hot water demand:

[0151] Q DHW =12 DHW 4.18 1000 45 A f

[0152]

[0153] Where DHW is the daily hot water consumption [m 3 / m 2 / day], A f is the building area [m 2 ], f occ It is the utilization rate of domestic hot water.

[0154] 8) On-site renewable energy production parameters: Photovoltaic (PV) system power generation calculates the amount of solar radiation incident on the PV panels and the resulting energy. sol,PV The area, angle and orientation of the photovoltaic panel determine the solar irradiance:

[0155]

[0156] Among them, E gh is the global horizontal irradiance [W / m 2 ], E sol,θ,ψ is the solar irradiance on the inclined surface.

[0157] The energy generated by the photovoltaic system is calculated as:

[0158] E gen,PV =E sol,PV ·P pk ·f perf 3600000

[0159] Among them, E gen,PV Energy generated by photovoltaic systems[J], E sol,PV is the solar irradiance on the photovoltaic panel [kWh / m 2 ], P pk is the peak power of the photovoltaic system [kW], f perf is the performance factor of the photovoltaic system [-], and 3600000 is the conversion factor from kWh to J.

[0160] The solar water heating system calculates the solar irradiance on the SWH collector. The solar irradiance (E sol,SWH ) is determined according to the area, angle and orientation of the collector:

[0161]

[0162] The energy gain of the SWH system is:

[0163] E gain,SWH =E sol,SWH 3600000

[0164] Among them, E gain,SWH is the energy gain of the SWH system[J], E sol,SWH is the solar radiation of the collector of the sewage treatment plant [kWh / m 2 ].

[0165] If the PV or SWH area is zero or there is an invalid input, the respective solar irradiance is set to zero. Using pre-calculated solar irradiance data for tilted surfaces (E sol,θ,ψ ), which is stored in the solar calculation dictionary. The performance factor (fperf ) takes into account various system losses and inefficiencies, which assumes that the energy gain of the SWH is proportional to the incident solar irradiation and does not consider system efficiency or heat losses.

[0166] The wind turbine produces:

[0167]

[0168] Where ρ air is the air density, N is the turbine number, A swept is the swept area, η turbine is the turbine efficiency, v wind is the wind speed.

[0169] refer to Figure 2 ,The hierarchical modeling architecture (region to building) combined with the coupling of heat flow within the thermal zone forms a building heat load simulation method based on the forward resistance-capacitance model. Figure 2 The architecture of the multi-thermal zone building energy simulation model is presented, and its performance in calculating the load and energy consumption of the entire building is demonstrated. In order to provide a user-friendly interface, the engine allows users to input through customized text-based structured modeling files, which are designed to be clear and simple, ensuring that users can effectively define and adjust model parameters without an overly steep learning curve. It simplifies the process of data input and facilitates modification, allowing researchers and building professionals to quickly iterate and modify their models and simulations. In addition, the implementation environment of the model information collection structure of the resistance-capacitance-based simulation engine is Python3.7, which ensures the robustness and adaptability of the model, and it provides direct integration capabilities with other computing tools and python libraries, enhancing the versatility of the engine in various building simulation scenarios.

[0170] Furthermore, the calculation formula of the 4R1C mode includes:

[0171]

[0172] Among them, T o is the outdoor air temperature (K); T a is the internal air node (m 2 K / W); T m is the thermal mass node (m 2 K / W); R v is the ventilation thermal resistance; R win is the window thermal resistance (m 2 K / W); R im is the internal mass thermal resistance (m 2 K / W); R ex is the external opaque structure thermal resistance (m 2 K / W); Q hvacis the heat flux density of the building cooling and heating system (W); Q air is the internal air heat flux density (W); Q int is the heat flux density of the load in the building (W); Q sol is the solar heat gain heat flux density (W); C m is the heat capacity of thermal mass per unit building area (J / m 2 K).

[0173] Furthermore, the 4R1C model proposed in this embodiment is a simplified representation of the thermal behavior of a building, especially when the thermal interactions between regions are negligible. The model has only two temperature nodes: T a (internal air) and T m (Thermal Mass), the temperature node is used to capture the main thermodynamic interactions in most typical building environments. In this model, the temperature node T a Represents the air temperature inside the building, which is affected by the outdoor temperature T o , the heating and air conditioning heat flow Q provided by the building HVAC system hvac And indoor and outdoor air heat exchange Q air Thermal resistance R v and R im Determines the heat exchange between the external environment and the internal air and between the internal air and the thermal mass of the building. m The temperature representing the thermal mass of a building, used to capture the overall behavior of the building structure and contents, and the equilibrium outdoor temperature T o , internal load Q int , Solar radiation gain Q sol and the relationship between the internal air nodes; its dynamic thermal inertia is given by C m Definition, that is, the heat capacity of the internal mass of a building.

[0174] refer to Figure 3 ,Inter-zone thermal coupling can play a key role in capturing the complex thermal interactions between different zones within a building.,Therefore, this embodiment proposes three different resistance-capacitance models to address these complexities. Figure 3 Including: 4R1C mode without interlayer thermal coupling, 6R1C mode, 7R1C mode and 7R2C mode.

[0175] Specifically, the calculation formula of the 6R1C mode includes:

[0176]

[0177] Among them, T az,i is the temperature of the neighboring area of ​​the ith area (K); R if,i is the thermal resistance of the bottom plate in the adjacent area of ​​the i-th area (m 2K / W); R iw,i is the thermal resistance of the inner wall of the adjacent area of ​​the i-th area (m 2 K / W). The 6R1C model uses the temperature node T a (representing the internal air) and T m (Describing thermal mass), it is able to integrate the heat flux between the current zone and its neighboring zones. Its model structure highlights the importance of the internal zone of the building in shaping its thermal behavior, which is crucial for building types with large structural loads or significant internal heat sources or sinks. By considering both the external environment and the neighboring zones, compared with the 4R1C model, this model provides a more comprehensive resistance-capacitance model structure for the behavior of a single thermal zone of the building and the interaction with its neighboring thermal zones.

[0178] Furthermore, the calculation formula of the 7R1C mode includes:

[0179]

[0180] Among them, R ia is the internal air thermal resistance (m 2 K / W); T s The 7R1C model introduces an additional temperature node T based on the 6R1C configuration. s , to represent the central thermal mass. This central mass is related to T m The thermal mass of the external envelope structure is different. s Insights can be gained into how core building components (e.g. central halls, courtyards or shared corridors) thermally influence the building load calculations. The thermal resistances associated with walls and floors in adjacent zones provide further details on the interactions between different thermal zones. Thus, 7R1C provides a more detailed model approach to capture external environmental influences and complex heat transfer processes between thermal zones. s The resistance associated with the interior walls and floors of the adjacent areas further details this interaction. Thus, 7R1C provides a layered approach to capture external influences and complex inter-layer dynamics.

[0181] Specifically, the calculation formula of the 7R2C mode includes:

[0182]

[0183]

[0184] Among them, C s is the heat capacity of the thermal mass node at the center of the building area (J / m 2 K). The 7R2C model introduces C on the basis of 7R1C. s term, used to capture the central thermal mass mode T sBy using C m Indicates the peripheral mass and C s Representing the central mass enables the 7R2C model to accurately represent the thermodynamic process of heat storage and release. Compared with the 7R1C model, this dual heat capacity mechanism ensures a more detailed description of the heat transfer process inside different buildings, making it particularly suitable for energy consumption simulation of heavy buildings in climate conditions with significant day-night temperature changes.

[0185] Furthermore, the fitness function of the DE algorithm is:

[0186]

[0187] Among them, PCE i is the predicted cooling energy consumption of the i-th iteration; OCE i is the observed cooling energy consumption of the i-th iteration; PHL i is the predicted heating energy consumption of the i-th iteration; OHL i is the observed heating energy consumption of the i-th iteration.

[0188] Specifically, the simulation parameters include: mutation rate, recombination rate, population multiplier, maximum iteration, and convergence tolerance.

[0189] Preferably, the present embodiment is about the solution method of the mode formula: in the numerical solution of the ordinary differential equation (ode) of the proposed resistor-capacitor model system, the present embodiment adopts the Euler method and the Crank-Nicolson method. The Euler method is one of the basic numerical techniques of ode. As a first-order method, it approximates the solution by advancing incrementally along the slope of the solution. The Crank-Nicolson method is a numerical technique for solving parabolic partial differential equations, which often appears in heat transfer and diffusion scenarios. Implemented as a two-step or implicit method, it calculates the solution by utilizing the current and subsequent time steps, averaging the explicit Euler format of the current time step with the implicit Euler format of the next time step. The Crank-Nicolson method has both the stability of the implicit method and the accuracy of the explicit method. This makes it the method of choice for many types of problems, especially when solving parabolic partial differential equations like the heat equation.

[0190] Furthermore, for any building energy simulation model to accurately represent reality, it is crucial to identify and correctly set the parameters that significantly affect the results. While some parameters, such as building geometry or material properties, can be easily extracted or measured, other parameters, such as thermal capacitors and system efficiency, are difficult to obtain directly. These difficult-to-determine parameters are crucial to capturing the actual dynamics of a building's thermal behavior and the efficiency of its HVAC system. However, their identification often requires a more nuanced approach, especially when the model's design documentation may be outdated or unavailable. To address this challenge, this embodiment employs the differential evolution (DE) algorithm, a robust and efficient global optimization technique that can handle complex, nonlinear, and multidimensional problems. In essence, DE is a population-based optimization algorithm. It starts with a population of randomly generated potential solutions (vectors). In successive iterations, these vectors evolve through a process that combines mutation, crossover, and selection. Compared to other optimization techniques, the main advantage of DE is that it can maintain population diversity and avoid premature convergence to local minima, making it particularly suitable for calibrating complex building energy models. It has good convergence when dealing with model parameter identification when the number of target parameters is not too large. In this embodiment, each vector in the DE algorithm represents a set of potential values ​​for the hard-to-determine parameters. The performance or "fitness" of each vector (i.e., how well the associated parameters enable the simulation model to reproduce actual building behavior) is evaluated using a fitness function based on the root mean square error (RMSE) between the model's predicted heating and cooling energy use and the building's observed data (with equal weights). In this case, the goal of the DE algorithm is to minimize the RMSE value, thereby deriving the most appropriate value for the hard-to-determine parameter. The fitness function of the DE algorithm can then be described as:

[0191]

[0192] Where PCE i represents the refrigeration energy consumption predicted for the i-th time, OCE i represents the refrigeration energy consumption for the i-th observation, PHL i represents the predicted heating energy consumption for the i-th time, OHL iRepresents the heating energy consumption observed for the i-th time. The optimization process in this embodiment provides three different time granularity modes: hourly, daily, and monthly. The user can define at what time granularity and under what calibration scheme to perform optimization. It is worth noting that although the DE-based calibration method is used here for existing building modeling, it is not used in the building pre-design stage due to the lack of real-world observational data. In order to further improve the adaptability and effectiveness of the DE-based calibration method, the engine provides users with the flexibility to adjust the hyperparameters of the DE algorithm. Such customization ensures that the calibration process can be fine-tuned to meet the specific needs of individual building scenarios or to be consistent with the modeler's preferences.

[0193] Further, key hyperparameters that can be tuned include:

[0194] 1) Mutation rate: determines the degree to which the vectors of the population are perturbed. It essentially controls the degree of exploration in the search space. Its default value ranges from 0.7 to 1, which allows for variability in the exploration intensity.

[0195] 2) Recombination rate: The default setting is 0.8. This hyperparameter is used to control the ratio of the mixing of donor vectors and target vectors, which affects the information exchange rate between different potential solutions.

[0196] 3) Population size: Population size is a key factor in the success of the DE algorithm. By default, it is set to around 50 to ensure that the initial solution set of the calibration problem is diverse enough.

[0197] 4) Maximum Iterations: This parameter specifies the number of iterations the engine will run before terminating. Its default value is set to 100, which balances the number of exhaustive searches and computational efficiency.

[0198] 5) Convergence tolerance: The default value is 0.01. This hyperparameter is the convergence threshold. If the change in fitness value between consecutive iterations is less than this threshold, the algorithm considers that the solution has converged. By allowing users to modify these hyperparameters, the calibration process becomes more versatile.

[0199] Further, while the default values ​​provided are based on extensive testing and have demonstrated consistent performance across numerous building models, the ability to adjust them ensures that the engine remains robust even in unique or challenging calibration scenarios. To make the calibration procedure more general and applicable to different building types and scenarios, users can flexibly select modeling parameters. Table 2 (building energy consumption model parameters) and Table 3 (differential evolution algorithm hyperparameters) show the modeling parameters that can be used for calibration in this embodiment.

[0200] Table 2

[0201]

[0202]

[0203] Table 3

[0204] parameter scope Mutation rate 0.7-1 Recombination rate 0.8 Population size 50 Maximum number of iterations 100 Convergence tolerance 0.01

[0205] When the user wishes to calibrate a parameter, they need to provide a reasonable range of values ​​within which the DE algorithm will search for the optimal value. In addition, to maintain clarity, organization, and easy access, all of this calibration information (modeling parameters and DE hyperparameters) is stored in a structured text-based file, specifically named "calibration_parameter.txt". The above table provides an intuitive way to modify the calibration parameters, allowing users to quickly prepare calibration simulations while ensuring the reproducibility and transparency of the calibration process. This design choice ensures seamless integration with other computational tools and provides an effective automatic calibration method for model parameter settings.

[0206] The beneficial effects of the present invention are as follows:

[0207] The present invention solves the defect of single applicability of existing methods by setting multiple resistance and capacitance simulation modes, and realizes reliable simulation of different building structures; through the DE algorithm, the fusion of positive resistance and capacitance model and reverse resistance and capacitance model is realized, which improves the simulation efficiency and optimization performance of the engine.

[0208] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0209] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling, characterized in that: include: Construct a building model and input model parameters to obtain a simulation model; Determining a simulation mode according to the simulation model; The simulation modes include: 4R1C mode, 6R1C mode, 7R1C mode and 7R2C mode; According to the simulation mode, the simulation model is simulated using the Euler method and the Crank-Nicolson method to obtain simulation node calculation results; Optimizing the simulation parameters of the simulation model by using the DE algorithm to obtain optimized parameters, and updating the optimized parameters to the simulation model; When the calculation result of the simulation node meets the preset convergence standard, the target energy consumption simulation result is output.

2. The method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 1 is characterized in that: The model parameters include: meteorological parameters, building geometry parameters, occupancy behavior parameters, building envelope parameters, infiltration or natural ventilation parameters, solar radiation parameters, convective heat transfer parameters, window absorption of solar radiation parameters, window shading parameters, HVAC system performance curve construction parameters, and on-site renewable energy production parameters.

3. The method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 1 is characterized in that: The calculation formula of the 4R1C mode includes: Among them, T o is the outdoor air temperature; T a is the internal air node; T m is the thermal mass node; R v is the ventilation thermal resistance; R win is the window thermal resistance; R im is the internal mass thermal resistance; R ex is the external opaque structure thermal resistance; Q hvac is the heat flux density of the building cooling and heating system; Q air is the internal air heat flux density; Q int is the load heat flux density in the building; Q sol is the solar heat gain heat flux density; C m is the heat capacity of thermal mass per unit building area.

4. The method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 3 is characterized in that: The calculation formula of the 6R1C mode includes: Among them, T az,i is the temperature of the neighboring area of ​​the ith area; R if,i is the thermal resistance of the base plate in the adjacent area of ​​the i-th area; R iw,i is the thermal resistance of the inner wall of the adjacent zone of zone i.

5. The method for implementing the overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 4 is characterized in that: The calculation formula of the 7R1C mode includes: Among them, R ia is the internal air thermal resistance; T s The central mass node.

6. The method for implementing the overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 5 is characterized in that: The calculation formula of the 7R2C model is include: Among them, C s is the heat capacity of the thermal mass node at the center of the building area.

7. The method for implementing an overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 6 is characterized in that: The fitness function of the DE algorithm is: Among them, PCE i is the predicted cooling energy consumption of the i-th iteration; OCE i is the observed cooling energy consumption of the i-th iteration; PHL i is the predicted heating energy consumption of the i-th iteration; OHL i is the observed heating energy consumption of the i-th iteration.

8. The method for implementing the overall building energy consumption simulation engine based on resistance and capacitance modeling according to claim 7 is characterized in that: The simulation parameters include: mutation rate, recombination rate, population multiplier, maximum iteration and convergence tolerance.

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