A method for implementing a whole building energy consumption simulation engine based on resistance and capacitance modeling

Abstract

This invention belongs to the field of building simulation technology and provides a method for implementing a holistic building energy consumption simulation engine based on resistance-capacitance modeling. The method includes: building model construction and model parameter input, simulation mode determination, simulation calculation, parameter optimization, and output of target energy consumption simulation results. By setting multiple resistance-capacitance simulation modes, this invention overcomes the limitation of limited applicability in existing methods, achieving reliable simulation of different building structures. Through the DE algorithm, it integrates positive and inverse resistance-capacitance models, improving the engine's simulation efficiency and optimization performance.

Description

Technical Field

[0001] This invention relates to the field of building simulation technology, and in particular to a method for implementing a holistic building energy consumption simulation engine based on resistance and capacitance modeling. Background Technology

[0002] Architectural design and engineering teams must make rapid and effective green and low-carbon building design decisions and apply energy-saving technologies while ensuring a good living environment. Building load and energy consumption simulation calculations are essential methods and tools for assessing energy consumption and carbon emission levels during building operation. Architects and engineers use various models to evaluate and make decisions about building schemes at different stages of design practice, involving various simulation analysis processes, including physical models, full-size and scale models, analytical models, mathematical models, and computational models. Since the building design process is phased, simulation-aided design also needs to intervene in different forms at different stages. The building design process can be divided into conceptual design, preliminary design, detailed design, and post-design stages. During the conceptual design and early design stages, as well as the post-use energy-saving renovation stage, the research, design, and engineering needs related to the comparison and optimization of parametric green building schemes are constantly increasing. Among these, whole-building heat load and energy consumption simulation tools are important evaluation tools in the building performance optimization process. Currently mature energy simulation software such as EnergyPlus requires detailed model input parameters and integrates all heat flow dynamics into the calculation. It employs transient methods to simulate and solve for the cooling and heating loads of buildings, obtaining reliable and detailed models and calculation results. However, its use can become complex and require significant computational power when dealing with buildings in multiple areas. Most existing building heat load simulation engines require substantial computational resources and time for dynamic load simulation, hindering the exhaustive comparison and optimization of numerous design schemes and making it difficult for architects or engineers to conduct rapid and comprehensive scheme evaluations. In building heat load simulation, a method that simulates the heat transfer process by analogy to the resistance and capacitance of circuit components is called a resistance-capacitance model. Because this type of model is less complex and computationally intensive than dynamic white-box models, it can significantly improve computational efficiency and speed.

[0003] However, existing research on building load and energy consumption simulation using resistive-capacitive models mainly relies on inverse methods, with limited research on positive resistive-capacitive models. Most existing related studies are also limited to the development of single thermal zone models, resulting in poor performance for rapid simulation calculations in the early stages of building design and for optimizing existing building renovations. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method for implementing a holistic building energy consumption simulation engine based on resistance and capacitance modeling. By setting multiple resistance and capacitance simulation modes, the invention addresses the limitation of limited applicability of existing methods and enables reliable simulation of different building structures. Through the DE algorithm, the invention achieves the fusion of positive and negative resistance and capacitance models, thereby improving the simulation efficiency and optimization performance of the engine.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for implementing a holistic building energy consumption simulation engine based on resistance and capacitance modeling includes:

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

[0008] The simulation mode is determined based on the simulation model; the simulation modes include: 4R1C mode, 6R1C mode, 7R1C mode and 7R2C mode;

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

[0010] The simulation parameters of the simulation model are optimized using the DE algorithm to obtain optimized parameters, and the optimized parameters are then updated to the simulation model.

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

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

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

[0014]

[0015] Among them, T o Outdoor air temperature; T a For internal air nodes; T m For thermal mass nodes; R v For ventilation thermal resistance; R win For window thermal resistance; R im R is the internal mass thermal resistance; ex For external opaque structure thermal resistance; Q hvacQ is the heat flux density of a building's heating and cooling system. air Q is the internal air heat flux density; int Q is the heat flux density of the building load; sol C is the solar thermal gain heat flux density; m Heat capacity is the heat mass per unit building area.

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

[0017]

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

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

[0020]

[0021] Among them, R ia For internal air thermal resistance; T s The central quality node.

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

[0023]

[0024] Among them, C s The heat capacity of the central thermal mass node of the building area.

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

[0026]

[0027] Among them, PCE i OCE is the predicted cooling energy consumption for the i-th iteration. i The observed cooling energy consumption for the i-th iteration; PHL i For the i-th iteration, the predicted heating energy consumption is: OHL i Let be the observed heating energy consumption for 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] This invention provides a solution that addresses the limitation of existing methods in terms of single applicability by setting multiple resistance and capacitance simulation modes, thereby achieving reliable simulation of different building structures. Through the DE algorithm, it solves the problems of poor performance of existing models in rapid simulation calculations in the early stages of building design and in optimizing existing building renovations, and achieves the fusion of positive resistance and capacitance models and inverse resistance and capacitance models. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram illustrating the implementation process of the overall building energy consumption simulation engine based on resistance and capacitance modeling provided in an embodiment of the present invention.

[0033] Figure 2 The layered modeling architecture provided in the embodiments of the present invention;

[0034] Figure 3 This is a schematic diagram of the resistor-capacitor mode provided in an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] The purpose of this invention is to provide a method for implementing a holistic building energy consumption simulation engine based on resistance and capacitance modeling. By setting multiple resistance and capacitance simulation modes, the invention addresses the limitation of limited applicability of existing methods and enables reliable simulation of different building structures. Through the DE algorithm, the invention achieves the fusion of positive resistance and capacitance models and inverse resistance and capacitance models, thereby improving the simulation efficiency and optimization performance of the engine.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Figure 1 This is a schematic diagram illustrating the implementation process of the overall building energy consumption simulation engine based on resistance and capacitance modeling provided in an embodiment of the present invention, as shown below. Figure 1As shown, this invention provides a method for implementing a holistic building energy consumption simulation engine based on resistance and capacitance modeling, including:

[0039] Step 100: Construct the building model and input the model parameters to obtain the simulation model;

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

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

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

[0043] Step 500: When the simulation node calculation results meet the preset convergence criteria, output the target energy consumption simulation results.

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

[0045] Furthermore, the forward modeling and model input set includes several key parameter types, each module making a complex contribution to the building's overall thermal performance and energy use:

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

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

[0048] 3) Infiltration and natural ventilation parameters:

[0049] Infiltration is the leakage of air through a building envelope caused by temperature differences (stack effect) and wind pressure differences. The "real" infiltration method combines the chimney effect and a wind-driven infiltration model. The chimney effect: The temperature difference between indoor and outdoor air causes buoyancy that drives 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 The volumetric flow rate [m] due to the chimney effect 3 / s],F r Reference flow rate [m 3 / s],f inf Where is the infiltration fraction [-], h is the area height [m], and T is the infiltration fraction. e Outdoor temperature [K], T set Set the indoor temperature [K]. The constant 0.7 represents the average height of the building's neutral pressure level.

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

[0053]

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

[0055] The combined effect was constructed using the orthogonal summation method:

[0056]

[0057] The final permeation rate takes into account the additional pressure differential and is ensured to be non-negative:

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

[0059] Where, q v,inf This represents the pressure difference term.

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

[0061] q v,inf =F r ·f inf

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

[0063]

[0064] Environmental factors: This model includes factors that consider the impact of wind and temperature on occupant window-opening behavior. Wind factor:

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

[0066] Temperature factor:

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

[0068] Openness factors:

[0069] R opw =Y wind ·Y temp

[0070] Natural ventilation rate: Natural ventilation volume is based on the orifice plate equation and takes into account the orifice area and ventilation volume.

[0071]

[0072] In the formula, A ow The area of ​​the opening [m 2 A f Building area [m 2 ].

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

[0074]

[0075] In the formula, q C To meet cooling requirements, NV hrs This refers to the number of hours of natural ventilation.

[0076] Total ventilation and heat exchange: Total ventilation volume 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] In the formula, 1.2 represents the volumetric heat capacity of air [kJ / (m³)]. 3 ·K)).

[0081] 4) Solar Radiation Parameters: After obtaining hourly climate parameters, calculate the hourly direct, diffuse, and reflected radiation, as well as the total radiation from all directions of the building. Combined with outdoor temperature, relative humidity, solar azimuth angle, and altitude, calculate the building's solar radiation heat gain according to ISO 13790 standard. Solar Position Calculation:

[0082]

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

[0084] The solar time variable τ is calculated 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 the longitude, and hour is the current time. Solar angle: Solar declination δ:

[0092]

[0093] Hour angle θ h :

[0094] θ h =15(AST-12)

[0095] Solar altitude angle β:

[0096] sinβ=cosφcosδcosθh +sinφsinδ

[0097] Where φ is latitude;

[0098] Solar azimuth ψ:

[0099]

[0100] Calculation of Earth's surface solar radiation, Earth's surface solar azimuth γ:

[0101] γ=|ψ-ψ surface

[0102] Angle of incidence θ:

[0103] cosθ=cosβcosγsinσ+sinβcosσ

[0104] Where σ is the surface tilt angle.

[0105] Direct radiation E tb :

[0106]

[0107] In the formula, E d This refers to 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 This refers to diffuse horizontal irradiance.

[0112] Ground reflection component E t,r :

[0113]

[0114] In the formula, r g This refers to the ground reflectivity.

[0115] Total solar radiation:

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

[0117] Solar radiation on an inclined surface:

[0118]

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

[0120] 5) Convection heat transfer parameters: These parameters significantly affect the prediction accuracy of the model. A common method for determining these parameters is through empirical formulas, which can be fixed values ​​or basic linear variational functions.

[0121] 6) Window Shading Parameters: The heat generated by solar radiation inside a building is closely related to the shading parameters of its windows. These parameters are affected by the window material and the angle of solar incidence. Most studies treat windows as pure drag without adequately considering shading parameters. Therefore, this embodiment considers the indoor solar radiation heat gain separately, integrating shading parameters, similar to heat transfer parameters, into the energy consumption simulation algorithm. This embodiment calculates the solar reduction coefficients for various shading devices, with excess:

[0122]

[0123] Vertical shading:

[0124]

[0125] Horizontal shading:

[0126]

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

[0128] 7) HVAC System Performance Curve Construction Parameters: The core of this embodiment is a simplified performance curve method for HVAC (Heating, Ventilation, and Air Conditioning) systems. The performance curve method is used for primary energy consumption in heating and cooling. Users need to input the energy efficiency of the heating and cooling sources at intervals under 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 system's performance curve. This method aims to simplify model input. Furthermore, if HVAC size details are not provided, it is assumed that the system meets the heating and cooling needs of all zones. If the heating or cooling load exceeds the system capacity, the indoor temperature is calculated using energy-saving control equations. This embodiment calculates the energy consumption for heating, cooling, ventilation, and domestic hot water (DHW) in each hot zone. Supply air flow rate, supply air flow rate for heating and cooling:

[0129]

[0130] In the formula, V h,supply and V c,supply The air flow rates for heating and cooling are respectively [m] 3 / h / m 2 ], qH and q C For heating and air supply requirements [W / m 2 ], ρ is the air density [kg / m³] 3 ], T supply,H and T supply,C T represents the heating supply air temperature [°C]. air,hc Let ΔT be the regional air temperature [°C]. reheat The reheat temperature difference is [°C].

[0131] Fan energy consumption:

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

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

[0134] In the formula, V exh For exhaust airflow, P fan The power of the wind turbine [W / (m²)] 3 / s)],f control f is the fan flow control factor. BAC,e Energy consumption factor for Building Automation and Control (BAC).

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

[0136]

[0137] In the formula, V w,h and V w,c For heating and cooling water flow rates [m] 3 / h],ρ w c is the density of water. p,w Let ΔT be the specific heat capacity of water. w,H and ΔT w,C The temperature difference between the heating and cooling water is used. Then, the pump volume is calculated based on the pump's control strategy.

[0138] Calculate the efficiency of 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 For the partial load curves of heating and cooling, η heat,nom and COP cool,nom This refers to the nominal efficiency.

[0142] Distribution losses and final energy consumption, taking into account allocation losses:

[0143]

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

[0145]

[0146] Among them, f dem,cool Indicates the proportion of cooling demand; f dem,heat It is the ratio of heating demand; η dist,heat and η dist,cool It refers to the efficiency of heating and cooling distribution; a cool and a heat It is the total loss coefficient for heating and cooling; f waste This indicates the rate of energy waste.

[0147] The final energy consumption for heating and cooling:

[0148]

[0149] Where, q H,loss and q C,loss is the power 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] In the formula, DHW represents the daily hot water consumption [m³]. 3 / m 2 / day], A f Building area [m 2 ], f occ This refers to the usage rate of domestic hot water.

[0154] 8) On-site renewable energy production parameters: The photovoltaic (PV) system power generation is calculated based on the solar irradiance incident on the photovoltaic panels and the energy generated therefrom. According to the photovoltaic panel E... sol,PV The area, angle, and orientation of the photovoltaic panel determine its solar irradiance.

[0155]

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

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

[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 Solar irradiance on photovoltaic panels [kWh / m] 2 ], P pk f represents the peak power [kW] of the photovoltaic system. perf 3600000 is the performance factor of the photovoltaic system [-], and 3600000 is the conversion factor from kWh to J.

[0160] The solar water heating system calculated the solar irradiance on the SWH collector. The solar irradiance (E) of the SWH collector... sol,SWH Determined based on the area, angle, and orientation of the solar 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 The energy gain of the SWH system [J], E sol,SWH Solar irradiance of wastewater treatment plant collectors [kWh / m²] 2 ].

[0165] If the PV or SWH area is zero or an invalid input exists, the respective solar irradiance is set to zero. Pre-calculated solar irradiance data (E) for the tilted surface is used. sol,θ,ψ This data is stored in the solar computation dictionary. The performance factor (f) of the photovoltaic system...perf It takes into account various system losses and inefficiencies, and assumes that the energy gain of the SWH is proportional to the incident solar irradiance, without considering system efficiency or heat loss.

[0166] The power generation of the wind turbine is:

[0167]

[0168] In the formula ρ air Where N is the air density, A is the number of turbines, and N is the number of turbines. swept For the swept area, η turbine For turbine efficiency, v wind This refers to wind speed.

[0169] refer to Figure 2 The hierarchical modeling architecture (from 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 for a multi-heat zone building energy simulation model, designed to calculate the load and energy consumption of the entire building, provides a user-friendly interface. The engine allows users to input data through customized text-based structured modeling files. These files are clearly and simply designed, ensuring users can effectively define and adjust model parameters without an overly steep learning curve. This simplifies the data input process, facilitates modification, and enables researchers and building professionals to quickly iterate and modify their models and simulations. Furthermore, the model information collection structure of the resistance-capacitance-based simulation engine is implemented in Python 3.7, ensuring the model's robustness and adaptability. It also provides direct integration with other computational tools and Python libraries, enhancing the engine's versatility in various building simulation scenarios.

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

[0171]

[0172] Among them, T o Outdoor air temperature (K); T a For internal air nodes (m 2 K / W); T m For thermal mass nodes (m) 2 K / W); R v For ventilation thermal resistance; R win For window thermal resistance (m 2 K / W); R im Internal mass thermal resistance (m) 2 K / W); R ex Thermal resistance (m) of the external opaque structure 2 K / W); Q hvacQ represents the heat flux density (W) of a building's heating and cooling system. air Q represents the internal air heat flux density (W); int Q is the heat flux density (W) within the building; sol For solar thermal gain, heat flux density (W); C m Heat capacity per unit building area (J / m²) 2 K).

[0173] Furthermore, the 4R1C model proposed in this embodiment is a simplified representation of the thermal behavior of a building, especially when inter-regional thermal interactions can be neglected. This model has only two temperature nodes: T a (Internal air) and T m (Thermal mass), temperature nodes are used to capture the main thermodynamic interactions in most typical built environments. In this model, the temperature node T a This indicates the air temperature inside the building, which is affected by the outdoor temperature T. o The heating and air conditioning heat flux Q provided by the building's HVAC system hvac and indoor and outdoor air heat exchange Q air The effect of thermal resistance R. v and R im These determine the heat flow exchanged between the external environment and the internal air, and between the internal air and the building's thermal mass, respectively. Node T m Temperature representing the thermal mass of a building, used to capture the overall behavior of the building's structure and contents, and to balance the outdoor temperature T. o Internal load Q int Solar radiation gain Q sol And its relationship with the internal air nodes; its dynamic thermal inertia is determined by C m Definition: Thermal capacity of the internal mass of a building.

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

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

[0176]

[0177] Among them, T az,i R is the temperature (K) of the neighboring zone of the i-th zone; if,i The thermal resistance of the base plate in the adjacent area of ​​region i (m 2K / W); R iw,i The thermal resistance of the inner wall of the adjacent zone of the i-th zone (m) 2 K / W). The 6R1C model uses the temperature node T. a (representing internal air) and T m (Describing thermal mass), this model integrates heat flux between the current region and its adjacent regions. Its model structure highlights the importance of internal regions within a building in shaping its thermal behavior, which is crucial for building types with large structural loads or significant internal heat sources or heat sinks. By simultaneously considering the external environment and adjacent regions, this model provides a more comprehensive resistive-capacitive model structure than the 4R1C model, taking into account the behavior of a single building thermal zone and the interactions between its adjacent thermal zones.

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

[0179]

[0180] Among them, R ia Internal air thermal resistance (m) 2 K / W); T s The central mass node is used. The 7R1C model, based on the 6R1C configuration, introduces an additional temperature node T. s , to represent the central thermal mass. This central mass is related to T. m The thermal mass of the outer envelope differs. Introducing T... s It provides insights into how core building components (such as central halls, courtyards, or shared corridors) thermally impact building load calculations. The thermal resistances associated with walls and floors in adjacent areas further explain the interactions between different thermal zones. Therefore, 7R1C offers a more detailed modeling approach to capture the influence of external environments and the heat transfer processes within complex thermal zones. s The resistance associated with the interior walls and floors of adjacent areas further details this interaction. Thus, 7R1C provides a layered approach to capture external influences and complex inter-floor dynamics.

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

[0182]

[0183]

[0184] Among them, C s Heat capacity of the central thermal mass node of the building area (J / m²) 2 K). The 7R2C model introduces C based on the 7R1C model. s Item, used to capture the central thermal mass mode T sThe heat storage capacity. By using C m Indicates the external mass and C s The central mass is represented, enabling the 7R2C model to accurately represent the thermodynamic processes of heat storage and release. Compared to the 7R1C model, this dual-capacity mechanism ensures a more detailed depiction of heat transfer processes within different building structures, making it particularly suitable for energy consumption simulations of heavy buildings under climatic conditions with significant diurnal temperature variations.

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

[0186]

[0187] Among them, PCE i OCE is the predicted cooling energy consumption for the i-th iteration. i The observed cooling energy consumption for the i-th iteration; PHL i For the i-th iteration, the predicted heating energy consumption is: OHL i Let be the observed heating energy consumption for the i-th iteration.

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

[0189] Preferably, the solution method for the mode formula in this embodiment is as follows: In the numerical solution of the ordinary differential equation (ode) of the proposed resistive-capacitive model system, this embodiment employs the Euler method and the Crank-Nicolson method. The Euler method is one of the basic numerical techniques for ode. As a first-order method, it approximates the solution by advancing along the slope increment of the solution. The Crank-Nicolson method is a numerical technique for solving parabolic partial differential equations, commonly found in heat transfer and diffusion scenarios. As a two-step or implicit method implementation, it calculates the solution by averaging the explicit Euler scheme for the current time step with the implicit Euler scheme for the next time step, utilizing the current and subsequent time steps. The Crank-Nicolson method combines the stability of implicit methods with the accuracy of explicit methods. This makes it the method of choice for many types of problems, especially when solving parabolic partial differential equations such as the heat equation.

[0190] Furthermore, for any building energy simulation model to accurately represent reality, identifying and correctly setting parameters that significantly affect the results is crucial. While some parameters, such as building geometry or material properties, can be easily extracted or measured, others, such as thermal capacitors and system efficiency, are difficult to obtain directly. These elusive parameters are essential for 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 capable of handling complex, nonlinear, and multidimensional problems. Essentially, 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 combination of mutation, crossover, and selection processes. Compared to other optimization techniques, DE's main advantage lies in its ability to maintain population diversity, avoiding premature convergence to local minima, making it particularly suitable for calibrating complex building energy models. It exhibits good convergence in handling 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 parameters that are difficult to determine. The performance or "fitness" of each vector (i.e., the degree to which the relevant 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 suitable values ​​for the difficult-to-determine parameters. The fitness function of the DE algorithm can then be described as:

[0191]

[0192] In the formula, PCE i OCE represents the predicted cooling energy consumption for the i-th time. i PHL represents the cooling energy consumption observed in the i-th instance. i OHL represents the predicted heating energy consumption for the i-th time. iThis represents the heating energy consumption observed in the i-th instance. The optimization process in this embodiment provides three different time granularity modes: hourly, daily, and monthly. Users can define the time granularity and calibration scheme under which optimization is performed. It is worth noting that although a DE-based calibration method is used for existing building modeling, it is not used in the pre-design phase due to a lack of real-world observation data. 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. This customization ensures that the calibration process can be finely tuned to meet the specific needs of individual building scenarios or to align with the modeler's preferences.

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

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

[0195] 2) Recombination rate: The default setting is 0.8. This hyperparameter controls the proportion of the donor vector and the target vector being mixed, and it affects the information exchange rate between different potential solutions.

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

[0197] 4) Maximum Iterations: This parameter specifies the number of iterations the engine will run before terminating. Its default value is 100, which balances the number of thorough searches with 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 successive iterations is less than this threshold, the algorithm considers the solution to have converged. By allowing users to modify these hyperparameters, the calibration process becomes more general.

[0199] Furthermore, while the provided default values ​​are based on extensive testing and have demonstrated consistent performance across numerous building models, the ability to adjust them ensures 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. Tables 2 (Building Energy Consumption Model Parameters) and 3 (Differential Evolutionary Algorithm Hyperparameters) show the modeling parameters available for calibration in this embodiment.

[0200] Table 2

[0201]

[0202]

[0203] Table 3

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

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

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

[0207] This invention addresses the limitation of existing methods in terms of applicability by setting multiple resistance and capacitance simulation modes, enabling reliable simulation of different building structures. Through the DE algorithm, it achieves the fusion of positive and negative resistance and capacitance models, improving the simulation efficiency and optimization performance of the engine.

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

[0209] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for implementing a holistic building energy consumption simulation engine based on resistance and capacitance modeling, characterized in that, include: A building model is constructed and model parameters are input to obtain a simulation model. The model parameters include: meteorological parameters, construction geometry parameters, occupancy behavior parameters, building envelope parameters, infiltration or natural ventilation parameters, solar radiation parameters, convective heat transfer parameters, window solar radiation absorption parameters, window shading parameters, HVAC system performance curve construction parameters, and on-site renewable energy production parameters. The simulation mode is determined based on the simulation model; the simulation modes include: 4R1C mode, 6R1C mode, 7R1C mode, and 7R2C mode; the calculation formula for the 4R1C mode includes: Among them, T o Outdoor air temperature; T a For internal air nodes; T m For thermal mass nodes; R v For ventilation thermal resistance; R win For window thermal resistance; R im R is the internal mass thermal resistance; ex For external opaque structure thermal resistance; Q hvac Q is the heat flux density of a building's heating and cooling system. air Q represents the internal air heat flux density. int Q is the heat flux density of the building load; sol C is the solar thermal gain heat flux density; m Heat capacity per unit building area; Based on the simulation mode, the simulation model is simulated using the Euler method and the Crank-Nicolson method to obtain the simulation node calculation results; The simulation parameters of the simulation model are optimized using the DE algorithm to obtain optimized parameters, and the optimized parameters are then updated to the simulation model. When the simulation node calculation results meet the preset convergence criteria, the target energy consumption simulation results are output.

2. The method for implementing a building energy consumption simulation engine based on resistance and capacitance modeling according to claim 1, characterized in that, The calculation formula for the 6R1C mode include: Among them, T az,i R is the temperature of the neighboring region of region i; if,i R is the thermal resistance of the base plate in the adjacent area of ​​region i; iw,i Let be the thermal resistance of the inner wall of the adjacent region of region i.

3. The method for implementing a comprehensive building energy consumption simulation engine based on resistance and capacitance modeling according to claim 2, characterized in that, The calculation formula for the 7R1C mode include: Among them, R ia For internal air thermal resistance; T s The central quality node.

4. The method for implementing a comprehensive building energy consumption simulation engine based on resistance and capacitance modeling according to claim 3, characterized in that, The calculation formula for the 7R2C mode includes: Among them, C s The heat capacity of the central thermal mass node of the building area.

5. The method for implementing a comprehensive building energy consumption simulation engine based on resistance and capacitance modeling according to claim 4, characterized in that, The fitness function of the DE algorithm is: Among them, PCE i OCE is the predicted cooling energy consumption for the i-th iteration. i The observed cooling energy consumption for the i-th iteration; PHL i For the predicted heating energy consumption in the i-th iteration; OHL i Let be the observed heating energy consumption for the i-th iteration.

6. The method for implementing a comprehensive building energy consumption simulation engine based on resistance and capacitance modeling according to claim 5, characterized in that, The simulation parameters include: mutation rate, recombination rate, population multiplier, maximum iteration, and convergence tolerance.

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