An energy-saving control scheduling method and system oriented to equipment mechanism

Abstract

The present invention discloses an energy-saving control and scheduling method and system for equipment mechanisms. The method includes the following steps: real-time collection of static and operational data of core cold and heat source equipment in a building; establishment of an energy consumption mechanism model to characterize how equipment energy consumption changes with operational status; utilization of the energy consumption mechanism model, historical operational data, environmental data, and building traffic data to predict energy consumption and load demand changes within a target time period using a pre-built prediction model; based on the predicted results of energy consumption and load demand changes and in combination with a set optimization objective function, an optimization algorithm is employed to optimize the start / stop status, operating load, and supply / return water temperature of the core cold and heat source equipment to generate an optimal scheduling solution; and the optimal scheduling solution is converted into control instructions and issued to the corresponding building automation system or equipment control unit. The present invention can improve the prediction accuracy and adaptability of energy-saving control and scheduling.

Description

Technical Field

[0001] The present invention belongs to the technical field of building energy conservation, and in particular relates to an energy conservation control scheduling method and system oriented to equipment mechanism. Background Art

[0002] With building energy consumption becoming increasingly prominent, especially in large commercial complexes, hospitals, and data centers, which increasingly rely on cooling and heating equipment (such as chillers and heat pumps), achieving efficient operation and energy-saving control of these equipment while ensuring load supply has become a critical issue in building energy management. Existing methods for optimizing the energy consumption of cooling and heating equipment primarily rely on empirical settings or simple rule-based strategies. For example, operational control is achieved through fixed start-stop schedules, fixed load distribution ratios, or preset supply and return water temperature parameters.

[0003] This type of method fails to fully utilize real-time operating data and the physical characteristics of equipment, resulting in a disconnect between scheduling strategies and actual operating conditions; there is a lack of in-depth modeling and prediction of energy consumption changes, and it is unable to dynamically adapt to load fluctuations and environmental changes; the optimization goal is single, only considering the lowest energy consumption, ignoring the impact of equipment life and frequent starts and stops on system reliability; the scheduling plan is updated with a lag, making it difficult to timely reflect the impact of equipment status changes or prediction errors.

[0004] The Chinese invention application with publication number CN118761859A discloses a building energy consumption prediction and control system based on machine learning. The data integration and processing module collects the location, design features and surrounding environment data of the building complex, and performs cleaning and normalization processing; the simulation parameter extraction module uses a computational CFD model to simulate the wind duct effect in the building complex, and extracts microenvironment change parameters from the CFD model data through time-frequency analysis. The microenvironment change parameters include local turbulence intensity and pressure fluctuations; the deep feature analysis module uses multidimensional scaling and principal component analysis to perform deep feature extraction on the extracted microenvironment change parameters, and identifies key variables affecting energy consumption; the key variable network module uses a graph neural network to construct an interaction network based on key variables affecting energy consumption, and predicts how key variables affecting energy consumption jointly affect the energy consumption of the building; the energy consumption prediction model module develops an energy consumption prediction model, and uses machine learning methods to optimize the model's hyperparameters and cross-validate.

[0005] The above scheme focuses on modeling the impact of micro-environmental changes on energy consumption at the building complex level, ignoring the direct driving effect of the operating status of the cold and heat source equipment inside the building on energy consumption; and mainly focuses on offline data collection and energy consumption forecast analysis, lacking the ability to collect equipment operation data in real time and dynamically adjust control instructions; resulting in insufficient level of refinement in the scheduling of this scheme, and insufficient adaptability and prediction accuracy of the scheduling scheme. Summary of the Invention

[0006] The present invention provides an energy-saving control scheduling method and system oriented to equipment mechanism, aiming to solve the problems of insufficient refinement level, insufficient adaptability of scheduling schemes and insufficient prediction accuracy in energy-saving control scheduling in the existing technology.

[0007] To solve the above technical problems, the energy-saving control scheduling method proposed in the present invention includes the following steps:

[0008] Real-time collection of static and operational data of core cooling and heating source equipment in buildings;

[0009] Based on the collected data and combined with the physical working mechanism of the equipment, an energy consumption mechanism model is established to describe the law of equipment energy consumption changes with operating status;

[0010] Using the energy consumption mechanism model, historical operation data, environmental data and building traffic data, a pre-built prediction model is used to predict energy consumption and load demand changes within a target period;

[0011] Based on the predicted results of the energy consumption and load demand changes, combined with the set optimization objective function, an optimization algorithm is used to optimize the start and stop status, operating load and supply and return water temperature of the core cold and heat source equipment to generate an optimal scheduling plan;

[0012] The optimal scheduling plan is converted into control instructions and sent to the corresponding building automation system or equipment control unit.

[0013] Preferably, the method also includes a feedback update step, which updates the prediction model parameters and optimizes the model weight coefficient according to the deviation between the actual operation results of the core cold and heat source equipment and the predicted results after issuing control instructions and collecting the actual operation results of the equipment.

[0014] Preferably, the energy consumption mechanism model adopts a mathematical fitting model based on the equipment thermodynamic performance curve, refrigeration efficiency and power curve.

[0015] Preferably, the energy consumption mechanism model includes an input layer, a physical modeling layer, an empirical correction layer and an output layer; wherein, the physical modeling layer calculates the cooling and heating loads according to the thermodynamic equations, and further calculates the preliminary energy consumption based on the calculated cooling and heating loads combined with the energy efficiency ratio of the equipment; the empirical correction layer trains a regression model through actual collected data to correct the theoretical calculation deviation of the preliminary energy consumption.

[0016] Preferably, the prediction model includes a long short-term memory network, an extreme gradient boosting regression model or a lightweight gradient boosting tree.

[0017] Preferably, the optimization objective function is specifically:

[0018]

[0019] Where N is the number of devices, T is the number of device scheduling time periods, is the power consumption of the i-th device at time t, is the total load demand predicted at time t, is the load provided by the i-th device at time t, and are the start and stop states of the i-th device at time t and t-1, and is the weight factor for adjusting the importance of each part.

[0020] Preferably, the optimization objective function satisfies the following conditions:

[0021]

[0022]

[0023]

[0024] Where, is the set small tolerance, is the maximum rated cooling / heating load of the i-th device.

[0025] Preferably, the optimization algorithm includes a mixed integer programming algorithm, a genetic algorithm or a particle swarm optimization algorithm.

[0026] Preferably, the static data includes the model, rated power, cooling / heating capacity, rated supply and return water temperature range and start / stop control parameters of the cold and heat source equipment; the operating data includes the equipment start / stop status, actual supply and return water temperature, instantaneous load rate, energy consumption and environmental parameters.

[0027] Accordingly, the present invention further proposes an energy-saving control scheduling system oriented to equipment mechanisms, which is used to implement the above-mentioned energy-saving control scheduling method, including:

[0028] Data acquisition module, used to collect static data and operating data of core cooling and heating source equipment in the building in real time;

[0029] A mechanism modeling module is used to establish an energy consumption mechanism model based on the static data and operating data in combination with the physical working mechanism of the equipment to describe the law of changes in equipment energy consumption with operating status;

[0030] An energy consumption prediction module, which is used to use the energy consumption mechanism model and historical operation data to predict energy consumption and load demand changes within a target period through machine learning;

[0031] A scheduling optimization module is used to optimize the start and stop status, operating load, and supply and return water temperature of the core cold and heat source equipment using an optimization algorithm based on the predicted results of the energy consumption and load demand changes, combined with a set optimization objective function, to generate an optimal scheduling plan;

[0032] An instruction issuing module is used to convert the optimal scheduling plan into control instructions and issue them to the corresponding building automation system or equipment control unit to achieve energy-saving operation control of the cold and heat source equipment;

[0033] Feedback update module updates the prediction model and optimizes model parameters based on the operation results.

[0034] Compared with the prior art, the present invention has the following technical effects:

[0035] 1. The energy-saving control and scheduling method proposed in this invention establishes an energy consumption mechanism model by combining the physical working mechanism of the cold and heat source equipment with real-time operating data. Compared with the traditional method of relying solely on historical data for energy consumption prediction, it can more accurately reflect the energy consumption variation pattern of the equipment under different operating states and environmental conditions, thereby improving the model's adaptability to actual operating condition changes and prediction accuracy.

[0036] 2. The energy-saving control scheduling method proposed in the present invention adopts a machine learning algorithm based on the energy consumption mechanism model to predict energy consumption and load demand in future time periods, which can realize early perception of load change trends, avoid the problem of passive scheduling based only on current operating conditions in traditional methods, and thus realize a more proactive and forward-looking energy-saving control strategy.

[0037] 3. The energy-saving control and scheduling method proposed in the present invention comprehensively considers multiple control parameters such as equipment start and stop status, operating load, supply and return water temperature, sets multi-objective optimization functions such as energy saving priority, load satisfaction, and equipment safety, and adopts optimization algorithms for global scheduling. It can effectively reduce overall energy consumption while ensuring the internal cooling and heating load requirements of the building and the stable operation of the equipment, solving the problem of limited energy-saving effects under traditional single control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the energy-saving control scheduling method of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0040] Example 1

[0041] An energy-saving control scheduling method oriented to equipment mechanism, such as Figure 1As shown, the following steps are included from step 1 to step 5:

[0042] Step 1: Collect static and operational data of the core cooling and heating source equipment in the building in real time.

[0043] First, static data is collected and entered for the core cooling and heating source equipment inside the building (such as chillers, boilers, heat pump units, etc.), including but not limited to the following information:

[0044] Equipment model, used to identify the specific specifications and models of the equipment, so as to facilitate subsequent query of the equipment's factory parameters and performance curves;

[0045] Rated power refers to the maximum power consumption value of the equipment under rated working conditions;

[0046] Cooling / heating capacity, the cooling or heating capacity of the equipment under standard operating conditions;

[0047] Rated supply and return water temperature range, which is the recommended supply and return water temperature range for the equipment under normal operating conditions (e.g., supply water 7°C, return water 12°C in cooling mode);

[0048] Start-stop control parameters: These include control logic constraints such as the minimum time interval between starts and stops, the minimum operating time, and the minimum downtime. These parameters are used to prevent equipment from frequently starting and stopping, which can lead to reduced efficiency or increased losses.

[0049] This static data typically comes from equipment factory documentation, product manuals, or on-site inspection data. It can be manually entered or imported in batches and stored in a central database. Static data represents basic equipment characteristics. Once collected and imported, it can be reused during subsequent operations, typically eliminating the need for recollection.

[0050] Secondly, the dynamic operation data generated during the operation of the equipment is collected in real time, including but not limited to the following information:

[0051] Start / Stop status: indicates whether the device is currently in operation, usually represented by a switch value (1-running, 0-stopping);

[0052] Actual water supply temperature, the real-time temperature value of the water supply on the output side of the equipment;

[0053] Actual return water temperature, the real-time temperature value of the return water on the input side of the equipment;

[0054] Instantaneous load rate, the percentage of the current actual load of the equipment to its rated load, which can be calculated based on parameters such as flow rate and temperature difference or directly output by the control system;

[0055] Energy consumption power, the actual energy consumption power value of the device in the current unit time;

[0056] Environmental parameters include temperature, humidity, air pressure and other environmental conditions inside and outside the building. These parameters have an important impact on the operating load and energy consumption of cold and heat source equipment.

[0057] Dynamic operation data can be collected in real time through the Building Management System (BMS), energy consumption monitoring system, Internet of Things sensors, etc., and automatically recorded in the data acquisition module or historical database according to the preset sampling period (such as every 5 minutes, every 15 minutes).

[0058] In practical applications, to ensure the accuracy and reliability of data collection, an anomaly detection mechanism can be set up. For example, boundary checking can be performed on the collected data to eliminate invalid data that exceeds a reasonable range; time synchronization processing can be performed on the sampled data to ensure that each data field has a unified timestamp; missing or abnormal values ​​can be filled or marked to avoid affecting subsequent model training and prediction accuracy.

[0059] As an example, Table 1 below shows the static data collected in this step.

[0060]

[0061] Table 1 Static data

[0062] Table 2 below shows the operating data collected in this step.

[0063]

[0064] Table 2 Operation data

[0065] Step 2: Based on the collected data and the physical working mechanisms of the equipment, an energy consumption mechanism model is established to characterize how the equipment's energy consumption changes with its operating state. This energy consumption mechanism model should be a multi-input multi-output (MIMO) data-driven model. Its core functions include estimating the energy consumption level at the corresponding moment based on the equipment's operating status and supporting the load demand and energy consumption forecasts in step 3.

[0066] The energy consumption mechanism model adopts a mathematical fitting model based on the equipment thermodynamic performance curve, refrigeration efficiency and power curve.

[0067] The energy consumption mechanism model includes an input layer, a physical modeling layer, an empirical correction layer and an output layer; wherein, the physical modeling layer calculates the cooling and heating loads according to the thermodynamic equation, and further calculates the preliminary energy consumption based on the calculated cooling and heating loads combined with the energy efficiency ratio of the equipment; the empirical correction layer trains a regression model through actual collected data to correct the theoretical calculation deviation of the preliminary energy consumption.

[0068] In this embodiment, an energy consumption mechanism model is provided to describe how the energy consumption of core cold and heat source equipment in a building changes with its operating state. The structure of the energy consumption mechanism model includes:

[0069] First, the collected static parameters and dynamic operation data of the equipment are received through the input layer. Among them, the static parameters include but are not limited to equipment model, rated power, maximum cooling or heating capacity, energy efficiency ratio, design flow and design head; the dynamic operation data include but are not limited to equipment start and stop status, current load rate, supply and return water temperature, chilled water flow, cooling water flow, inlet and outlet pressure, current, voltage, power and external ambient temperature and humidity.

[0070] Then, we enter the modeling layer, which includes the physical modeling module and the experience correction module. The physical modeling module calculates the current cooling and heating load of the equipment based on the basic principles of thermodynamics using the following formula:

[0071]

[0072] Where Q is the load, m is the flow rate, is the specific heat capacity, and The inlet water temperature and return water temperature are the inlet and outlet water temperature differences respectively; the input power is derived through the performance curve and Q of the equipment.

[0073] After obtaining preliminary energy consumption values ​​through physical modeling, the empirical correction module enters. This module constructs a regression model and uses historically collected equipment operating data to correct the physical model calculation results to eliminate deviations introduced by factors such as equipment aging, environmental changes, and measurement errors. The feature processing unit selects and normalizes input features and trains an error compensation function based on actual operating conditions, thereby improving the accuracy and robustness of energy consumption forecasts.

[0074] Through the output layer, the energy consumption mechanism model can output the following data: the predicted energy consumption per unit time of a single cold and heat source device in the future target period; the aggregated total energy consumption prediction curve of multiple devices; the load rate change trend prediction of a single device and the overall system; and the cooling load or heating load demand prediction of the target building in the future period.

[0075] Step three: using the energy consumption mechanism model, historical operation data, environmental data and building traffic data, a pre-built prediction model is used to predict energy consumption and load demand changes within the target period.

[0076] The prediction model includes a long short-term memory network, an extreme gradient boosting regression model or a lightweight gradient boosting tree.

[0077] Specifically, taking the Extreme Gradient Boosting (XGBoost) model as an example, its inputs include static device feature data, real-time device operation data, device energy consumption mechanism model output, and historical energy consumption load data. It outputs predicted energy consumption values ​​and load values ​​for each time point in the future target period. The output task type setting for XGBoost determines its output type. If the task type is a classification task, the model outputs probabilities; if the task type is set to a regression task, the output is a continuous real value (i.e., the regression prediction value). In this step's energy consumption load prediction scenario, the XGBoost regression task is used.

[0078] When training the XGBoost model, it is necessary to extract fixed features related to energy consumption from the static feature data of the equipment, such as rated power, cooling / heating capacity, etc.; extract time series features from the real-time operation data of the equipment, such as the current load rate, supply and return water temperature, and ambient temperature and humidity; call the energy consumption mechanism model and derive the output of the energy consumption mechanism model based on the current operation data; combine the output of the above energy consumption mechanism model with the historical energy consumption load data to form a unified training sample set.

[0079] The specific training process is as follows: define the XGBoost regression model and set basic hyperparameters such as learning rate, tree depth, and subsampling ratio; use historical data to train the model with the goal of minimizing energy consumption prediction errors; use cross-validation to tune hyperparameters and improve model generalization capabilities; save the optimal model after training is completed.

[0080] The trained XGBoost model can be used to predict energy consumption and load in future time periods: obtain the expected static and dynamic input features within the prediction target period; use the energy consumption mechanism model to perform feature inference on the operating status of the prediction period, generate operating status features, and fuse the static and dynamic input features with the operating status features to form the final prediction feature vector; call the optimal model for prediction and output the predicted energy consumption and predicted load.

[0081] In this embodiment, two types of predictors are trained for each device: predicting the future hourly load rate and predicting the future hourly energy consumption using the load rate. As an example, Table 3 below provides the real-time data of device A used to input the optimal model for prediction.

[0082]

[0083] Table 3 Real-time data of device A

[0084] Step 4: Based on the predicted results of the energy consumption and load demand changes and the set optimization objective function, an optimization algorithm is used to optimize the start and stop status, operating load and supply and return water temperature of the core cold and heat source equipment to generate the optimal scheduling plan.

[0085] The optimization objective function is specifically:

[0086]

[0087] Where N is the number of devices, T is the number of device scheduling time periods, is the power consumption of the i-th device at time t, is the total load demand predicted at time t, is the load provided by the i-th device at time t, and are the start and stop states of the i-th device at time t and t-1, and is the weight factor for adjusting the importance of each part.

[0088] The optimization objective function satisfies the following conditions:

[0089]

[0090]

[0091]

[0092] Where, is the set small tolerance, is the maximum rated cooling / heating load of the i-th device.

[0093] Optimization algorithms include mixed integer programming (MIP), genetic algorithms (GA), or particle swarm optimization (PSO). Mixed integer programming can be solved using the gurobipy library, genetic algorithms can be implemented using libraries such as DEAP and PyGMO, and particle swarm optimization can be implemented using libraries such as PySwarms and SciPy.

[0094] As an example, we obtain the load demand forecast for the next 24 hours through step 3, with one data point per hour, which is recorded as . Build equipment mechanism models for the three chillers in the building (numbered A, B, and C), including: rated cooling capacity , 500kW, 600kW, and 400kW respectively; the load rate and power consumption relationship curve provided by the energy consumption mechanism model; and the minimum tolerance (minimum operating load limit) of 50%. Based on the above functions, a specific optimization objective function is established:

[0095]

[0096] In the above formula, the weight factor and Set them to 100 and 5, respectively, and call the mixed integer programming optimizer. Input the device modeling parameters, load forecast value, optimization objective function, load satisfaction constraints, and load rate limit. After solving, the start and stop status of each device at each time and the corresponding load rate are output.

[0097] Step 5: Convert the optimal scheduling solution into control instructions and send them to the corresponding building automation system or equipment control unit. Based on the example provided in step 4, this step converts the optimized scheduling solution into executable control instructions, such as:

[0098] Unit A runs at 70% load at 0-8;

[0099] Unit B operates at 65% load from 8 to 18 o'clock;

[0100] Unit C is turned on as an auxiliary during peak hours, with a load of 40%.

[0101] In some other embodiments of the present invention, the method also includes step six, which is specifically: a feedback update step. After issuing control instructions and collecting actual equipment operation results, the feedback update step updates the prediction model parameters and optimizes the model weight coefficient according to the deviation between the actual operation results of the core cold and heat source equipment and the predicted results.

[0102] Example 2

[0103] This embodiment is an energy-saving control and scheduling system oriented to equipment mechanisms. The system is used to implement the energy-saving control and scheduling method described in the first embodiment, including:

[0104] Data acquisition module, used to collect static data and operating data of core cooling and heating source equipment in the building in real time;

[0105] A mechanism modeling module is used to establish an energy consumption mechanism model based on the static data and operating data in combination with the physical working mechanism of the equipment to describe the law of changes in equipment energy consumption with operating status;

[0106] An energy consumption prediction module, which is used to use the energy consumption mechanism model and historical operation data to predict energy consumption and load demand changes within a target period through machine learning;

[0107] A scheduling optimization module is used to optimize the start and stop status, operating load, and supply and return water temperature of the core cold and heat source equipment using an optimization algorithm based on the predicted results of the energy consumption and load demand changes, combined with a set optimization objective function, to generate an optimal scheduling plan;

[0108] An instruction issuing module is used to convert the optimal scheduling plan into control instructions and issue them to the corresponding building automation system or equipment control unit to achieve energy-saving operation control of the cold and heat source equipment;

[0109] Feedback update module updates the prediction model and optimizes model parameters based on the operation results.

[0110] The above description is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the creative concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. An energy-saving control scheduling method oriented to equipment mechanism, characterized in that: The following steps are involved: Real-time collection of static and operational data of core cooling and heating source equipment in buildings; Based on the collected data and combined with the physical working mechanism of the equipment, an energy consumption mechanism model is established to describe the law of equipment energy consumption changes with operating status; Using the energy consumption mechanism model, historical operation data, environmental data and building traffic data, a pre-built prediction model is used to predict energy consumption and load demand changes within a target period; Based on the predicted results of the energy consumption and load demand changes, combined with the set optimization objective function, an optimization algorithm is used to optimize the start and stop status, operating load and supply and return water temperature of the core cold and heat source equipment to generate an optimal scheduling plan; Convert the optimal scheduling plan into control instructions and send them to the corresponding building automation system or equipment control unit; The optimization objective function is specifically: Where N is the number of devices, T is the number of device scheduling time periods, is the power consumption of the i-th device at time t, is the total load demand predicted at time t, is the load provided by the i-th device at time t, and are the start and stop states of the i-th device at time t and t-1, and is the weight factor for adjusting the importance of each part.

2. The method according to claim 1, characterized in that The method also includes a feedback update step, which updates the prediction model parameters and optimizes the model weight coefficient according to the deviation between the actual operation results of the core cold and heat source equipment and the predicted results after issuing control instructions and collecting the actual operation results of the equipment.

3. The method according to claim 1, characterized in that The energy consumption mechanism model adopts a mathematical fitting model based on the equipment thermodynamic performance curve, refrigeration efficiency and power curve.

4. The method according to claim 1, wherein The energy consumption mechanism model includes an input layer, a physical modeling layer, an empirical correction layer and an output layer; wherein, the physical modeling layer calculates the cooling and heating loads according to the thermodynamic equation, and further calculates the preliminary energy consumption based on the calculated cooling and heating loads combined with the energy efficiency ratio of the equipment; the empirical correction layer trains a regression model through actual collected data to correct the theoretical calculation deviation of the preliminary energy consumption.

5. The method according to claim 1, wherein The prediction model includes a long short-term memory network, an extreme gradient boosting regression model or a lightweight gradient boosting tree.

6. The method according to claim 1, characterized in that The optimization objective function satisfies the following conditions: Where, is the set small tolerance, is the maximum rated cooling / heating load of the i-th device.

7. The method according to claim 1, characterized in that The optimization algorithm includes a mixed integer programming algorithm, a genetic algorithm or a particle swarm optimization algorithm.

8. The method according to claim 1, characterized in that The static data includes the model, rated power, cooling / heating capacity, rated supply and return water temperature range and start / stop control parameters of the cold and heat source equipment; the operating data includes the equipment start / stop status, actual supply and return water temperature, instantaneous load rate, energy consumption and environmental parameters.

9. An energy-saving control and scheduling system oriented to equipment mechanism, characterized in that: The system is used to implement the energy-saving control scheduling method according to any one of claims 1 to 8, including: Data acquisition module, used to collect static data and operating data of core cooling and heating source equipment in the building in real time; A mechanism modeling module is used to establish an energy consumption mechanism model based on the static data and operating data in combination with the physical working mechanism of the equipment to describe the law of changes in equipment energy consumption with operating status; An energy consumption prediction module, which is used to use the energy consumption mechanism model and historical operation data to predict energy consumption and load demand changes within a target period through machine learning; A scheduling optimization module is used to optimize the start and stop status, operating load, and supply and return water temperature of the core cold and heat source equipment using an optimization algorithm based on the predicted results of the energy consumption and load demand changes, combined with a set optimization objective function, to generate an optimal scheduling plan; An instruction issuing module is used to convert the optimal scheduling plan into control instructions and issue them to the corresponding building automation system or equipment control unit to achieve energy-saving operation control of the cold and heat source equipment; Feedback update module updates the prediction model and optimizes model parameters based on the operation results.

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