319 results about "Air conditioning load" patented technology

The invention discloses an intelligent regulation and control method and system for a photovoltaic energy storage air conditioner. The method comprises the steps that photovoltaic related data, energy storage related data and air conditioner operation related data are collected; inputting the photovoltaic related data into the photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction result; inputting the air conditioner operation related data into the air conditioner load prediction model to obtain an air conditioner load prediction result; inputting the photovoltaic generating capacity prediction result, the air conditioning load prediction result, the energy storage related data and the photovoltaic related data into an energy storage optimization scheduling model to obtain an energy storage charging and discharging decision; constructing a power supply strategy core rule base based on the energy storage related data, the electricity price peak time period and output results of the photovoltaic power generation prediction model and the air conditioner load prediction model; and optimizing an output result of the energy storage optimization scheduling model based on the power supply strategy core rule base. The method has the outstanding advantages of high reliability, high photovoltaic utilization rate, low dependence degree on a power grid and the like.

An air conditioner load monitoring and anomaly detection method and system. The method comprises: acquiring user power consumption data and meteorological data, the user power consumption data and the meteorological data comprising the total power of an electricity meter, the power of an air conditioner, and the outdoor temperature, preprocessing the data, and computing electrical characteristics of the user power consumption data; on the basis of the electrical characteristics, learning by using a two-tower neural network so as to obtain operating rules of the air conditioner, and extracting an air conditioner operation curve; on the basis of the air conditioner operation curve, computing electrical characteristics and energy consumption characteristics of the air conditioner, and performing cluster analysis by using a K-means clustering algorithm; and, on the basis of clustering results, comparing the clustering results with clustering results of collected electrical data of the air conditioner under normal and abnormal operating conditions to determine whether the air conditioner is in an abnormal state, so as to implement detection of anomalies in the operation of the air conditioner, thereby improving the precision of load decomposition of the air conditioner, reducing application costs, providing a decision-making reference for equipment maintenance personnel, and prolonging the service life of the equipment.

The invention relates to the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention relates to the technical field of air conditioner load prediction, and discloses an air conditioner load prediction and energy saving method based on machine learning, and the method comprises the following steps: collecting and preprocessing the historical load, multi-dimensional meteorological data and equipment operation parameters of a building air conditioner; historical loads and meteorological data are input into a GBDT model, meteorological feature importance is calculated, and key features are screened; taking the key meteorological characteristics and the historical load as input, and performing load prediction by using an LSTM model; based on a particle swarm optimization algorithm, operating parameters of the air conditioning system are dynamically optimized; and updating the GBDT and LSTM model when the prediction error exceeds a threshold value or the period arrives. According to the method, key meteorological characteristics are screened through GBDT to improve the load prediction precision, adaptive modeling of different climate areas is achieved in combination with LSTM, the air conditioner operation parameters are dynamically adjusted through the particle swarm optimization algorithm, the comprehensive energy consumption of the air conditioner system is effectively reduced, and the overall energy-saving efficiency and the intelligent level of the air conditioner system are improved.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

The invention discloses an air conditioner load response optimization method based on multi-dimensional data fusion, relates to the technical field of air conditioner control, and solves the problem that the number of personnel may suddenly increase or suddenly decrease in an air conditioner refrigeration area. The load capacity predicted only from the air conditioning equipment is difficult to adapt to the complex scene; the accuracy of predicting the load capacity in the future time interval is reduced, and the comfort level of the user is correspondingly reduced. Comprising the following steps: 1, acquiring environment data and image data of each monitoring area; 2, importing the image data into a personnel distribution intelligent model for analysis to obtain a personnel distribution diagram; performing analysis based on the personnel distribution map to obtain a personnel density value used for representing the personnel density of the shopping mall; performing analysis based on the personnel density value and the environment data to obtain a load demand quantity; 3, performing dynamic adjustment based on the load demand quantity to obtain a corresponding optimization adjustment report; and the energy-saving effect of the air conditioner load and the comfort degree of a user are improved.

The invention relates to the technical field of data processing, in particular to a computer-aided air conditioner load prediction model simulation construction method, which comprises the following steps of: acquiring data of each environmental parameter in a building at all moments in each period in real time to form a monitoring sequence; the method comprises the following steps: performing analogue simulation on energy consumption of an air conditioner to obtain load data of an air conditioner load at all moments in each period, and forming a load sequence; acquiring a lagging time length; extracting a time delay subsequence of each environmental parameter in each period; the trend synchronization degree between each environment parameter and the air conditioner load in each period is determined; determining a coupling coefficient of any two environmental parameters in each period; determining a reference evaluation value between the current period and each of the rest periods; obtaining each reference period; and constructing a load prediction model. According to the method, prediction deviation caused by neglecting interaction influence and lag influence among various environmental parameters can be avoided, and the prediction accuracy of the air conditioner load is improved.

The invention relates to a multivariable multi-step air conditioner load prediction model based on time sequence convolution and a double attention mechanism, and belongs to the technical field of air conditioner load prediction. The model adopts a parallel encoding structure, in an encoder, a time sequence convolution module is responsible for modeling local dependence and long-term trend in a time dimension, and a double attention mechanism module is used for modeling a dynamic dependence structure among multiple variables and a coupling relation between the variables and a target load from a variable dimension. The two structures are respectively subjected to feature extraction from a time dimension and a variable dimension, and are complementary to each other. In a decoder, a decoding module with a memory ability and a teacher mandatory strategy is designed, and continuous prediction from a historical multivariable sequence to a future target load is realized.

The invention discloses an air conditioner load aggregator demand response optimization method and device based on user credit, and the method comprises the steps: building a user credit evaluation model according to a user subscription performance index, carrying out the evaluation processing of the response behavior performance of a user, and obtaining a user credit evaluation index; based on the thermal resistance-thermal capacity network model, constructing a building room temperature thermal dynamic model; describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model; based on the user credit evaluation indexes, constructing a load aggregator demand response double-layer optimization model; solving the optimization model by setting a solving strategy to obtain an optimal scheduling strategy of the load aggregator; constructing a credit updating model; and after the power grid demand response, updating the user credit evaluation index through the credit updating model according to the user response performance index, and obtaining the updated credit evaluation index of the user. According to the invention, the demand response effect can be improved and fine scheduling can be realized.

The invention provides a demand side resource mutual aid potential quantitative evaluation method considering space-time collaboration, relates to the technical field of power resource management, and aims to optimize a collaborative mutual aid potential quantitative evaluation method of demand side resources, comprising: determining a plurality of evaluation dimensions including space-time collaboration degree, peak clipping potential and economic applicability; obtaining a space-time collaboration degree; obtaining peak clipping potential; economic applicability is obtained; and obtaining demand side resource mutual aid potential quantitative evaluation by endowing different weights to different index parameters. The method has the advantages that the charging and discharging strategy is reasonably formulated, the adjustable potential of electric vehicle and air conditioner loads is stimulated, the load pressure of a power grid is relieved, and a scientific and accurate quantitative basis is provided.

The invention discloses a virtual power plant energy management method and system based on an air conditioning load, and relates to the field of energy management, and the management method comprises the steps: obtaining power consumption data and integrated operation information data in a virtual power plant management range, carrying out the statistical division of the operation information data according to an operation rule and a potential distribution model, and carrying out the statistical division of the operation information data; generating a batch monitoring management set; constructing a virtual power plant electric energy transaction framework based on the batch monitoring management set, analyzing an electric power balance relationship between an electric quantity supply and demand difference and a virtual power plant, and outputting an energy scheduling result in a virtual power plant management range; and the air conditioner load data serve as demand response resources, the energy dispatching result is optimized, an air conditioner control issuing instruction is generated, the air conditioner control issuing instruction is recorded and decomposed, and the control state of the air conditioner is output. Through optimal scheduling of the air conditioner load, the demand of the air conditioner load can be reduced in the peak period of power demand, so that the load pressure of the system is effectively reduced, and excessive energy consumption is avoided.

Disclosed in the present invention are a photovoltaic air conditioning system, and a control method and control apparatus therefor. The control method comprises: obtaining air conditioner load prediction result data on the basis of known air conditioner load data and an established air conditioner load prediction model; obtaining photovoltaic power generation prediction result data on the basis of known photovoltaic data and an established photovoltaic power generation prediction model; obtaining power grid load prediction result data on the basis of power grid data and an established power grid load prediction model; determining a first energy storage battery charging and discharging strategy on the basis of the air conditioner load prediction result data, the photovoltaic power generation prediction result data and the power grid load prediction result data; and determining a second energy storage battery charging and discharging strategy on the basis of the first energy storage battery charging and discharging strategy and a state-of-charge threshold adjustment strategy, and controlling the charging and discharging process of an energy storage battery assembly on the basis of the second energy storage battery charging and discharging strategy. The present invention can improve the efficient operation performance of a photovoltaic air conditioning system.

The invention discloses a data center dynamic air-conditioning load rapid calculation method, which comprises the following steps of: 1, summarizing data center cold load influence factors as an input condition of a calculation model; 2, constructing a standardized data center reference model based on building energy consumption simulation software; 3, based on the building geometrical characteristics and thermal performance of the target data center, correcting the enclosing structure load of the reference model by adopting an area correction method, and constructing an enclosing structure load rapid calculation model; 4, automatically resetting an internal heat source load calculation result, and constructing an internal heat source load rapid calculation model; 5, correcting the fresh air load of the reference model by adopting a volume correction method based on the geometric characteristics of the target data center building, and constructing a fresh air load rapid calculation model; and step 6, forming a target data center dynamic air-conditioning load rapid calculation method, and obtaining a hourly cooling load prediction result required for covering the whole year or a design stage.

The invention provides an energy-storage-free dynamic power supply method and system for a photovoltaic air conditioner. The method belongs to the technical field of building energy conservation and photovoltaic air conditioner control and comprises the steps that a photovoltaic output prediction value and an air conditioner load prediction value are obtained; time-of-use electricity price data serves as input, a linear programming model with the operation cost minimization as the target is constructed, and power is dynamically supplied to the air conditioner according to the power supply strategy and the time-of-use electricity price response strategy based on the linear programming model; the system can dynamically supply power to the air conditioner through photovoltaic in a non-energy-storage scene, so that the operation cost is minimized, the heat energy storage potential of a building envelope structure is developed, load cross-time transfer is achieved through pre-cooling / pre-heating, and the operation cost is further reduced.

The invention discloses a subway station air conditioning load adjusting system and method, and the system comprises a load simulation module which is used for simulating the time sequence change of cold loads and heat loads of different types of stations under different meteorological conditions and operation conditions, and generating a system training data set; the passenger travel behavior modeling module is used for establishing a time-period passenger distribution model by using subway historical passenger flow data and urban public transport survey data; outputting passenger flow densities of different areas by using a time period passenger flow distribution model; the machine learning prediction model is used for extracting multi-dimensional features as input features; a system training data set is utilized to learn the mapping relation between the input features and the target cold / heat load, and a load preset value is output; the intelligent load adjusting control module dynamically adjusts the output capacity of the air conditioning system through the building automation system according to the load preset value, and closed-loop control is achieved. According to the invention, TRNSYS simulation, behavior modeling and intelligent control are integrated, and accurate load prediction and energy-saving regulation and control are realized.

The invention discloses a virtual power plant combined dispatching method, device and equipment and a medium, and relates to the technical field of virtual power plant combined dispatching, the combined dispatching method comprises the steps that a combined dispatching model of a virtual power plant is constructed, and the combined dispatching model is used for taking the minimum dispatching cost as an optimization target; and scheduling the cooperative operation process among the photovoltaic power generation equipment, the energy storage equipment, the electric vehicle charging station, the electric vehicle battery swap station, the air conditioning load and the power grid under the constraint of the constraint condition. The constraint conditions comprise an electric power balance constraint, an energy storage equipment constraint, an electric vehicle charging station constraint, an electric vehicle battery swap station constraint, an air conditioner load constraint and a power grid interaction power constraint, and then solving the combined scheduling model to obtain a combined scheduling scheme of the virtual power plant. The operation risk of the virtual power plant can be reduced, and the operation efficiency, stability, flexibility and reliability of the virtual power plant can be improved.

The invention discloses a target optimization method and device for an air conditioner aggregation type virtual power plant, equipment and a medium, and relates to the technical field of computers. Other loads except air conditioner loads in the air conditioner aggregation type virtual power plant are modeled to obtain a virtual power plant model, user data are input into the virtual power plant model, and a demand price self-elasticity coefficient of a user is output; a central air conditioner black box model is established according to the air conditioner load, thermodynamic model parameters are input into the central air conditioner black box model, and virtual energy storage operation parameters are output; constructing a lower-layer optimization objective function of the air-conditioning aggregation type virtual power plant, and solving the lower-layer optimization objective function; an upper-layer optimization objective function is constructed, objective optimization of the upper-layer optimization objective function of the air-conditioning aggregation type virtual power plant is realized by utilizing a lower-layer solving result, the problem that the optimization result of any layer cannot be clearly expressed through an analytical expression is solved, and the calculation precision and efficiency are improved; and efficient solution of demand response of the air-conditioning aggregation type virtual power plant is realized.

The invention relates to the technical field of air conditioner load prediction, and provides an air conditioner load prediction method and system, and the method comprises the steps: extracting intra-day meteorological data features and intra-day air conditioner load data features, and forming multi-dimensional data features; performing dimension reduction processing on the multi-dimensional data features to form a comprehensive feature curve; clustering the comprehensive characteristic curve, dividing the air conditioner load data in the historical day into a data set according to a clustering result, and dividing the data set into a training set and a test set; a plurality of prediction models corresponding to different meteorological scenes are constructed, the prediction models are trained through the corresponding training sets, hyper-parameter tuning is conducted on the prediction models through an improved sodat swarm optimization algorithm, and a plurality of air conditioner load prediction models are formed; and inputting the test set corresponding to various meteorological scenes into the corresponding air conditioner load prediction model, and outputting an air conditioner load prediction result. According to the invention, air conditioner load curves with obvious boundaries in different meteorological scenes can be effectively separated, and air conditioner power load prediction errors in extreme weather are reduced.

The invention discloses a baseline evaluation method for air conditioner loads under various meteorological conditions. The baseline evaluation method comprises the steps that S1, air conditioner load data are marked, and a data set is constructed based on the air conditioner load data; s2, an evaluation model is constructed, the model comprises a self-encoder and a classifier, the air conditioner load data are imported into the self-encoder, and self-encoding features are obtained; s3, importing the self-encoding features into a classifier, and obtaining a predicted load type; and respectively constructing loss functions in the processes of the step S2 and the step S3, and minimizing the loss functions to optimize the evaluation model. According to the method, real-time monitoring and self-adaptive adjustment of air conditioner load data distribution drift caused by meteorological condition changes are achieved, and it is ensured that feature expression is stable after dimensionality reduction; gradient updating fluctuation in the training process is reduced, the model convergence time is shortened, the classification accuracy of all categories in an air conditioner load classification task is improved, and a quantitative standard is provided for energy consumption monitoring and abnormal load recognition.

The invention relates to the technical field of air conditioner load modeling and parameter identification, in particular to an air conditioner parameter identification and modeling method based on a multi-mechanism fusion grey wolf optimization algorithm. The method comprises the steps that firstly, air conditioner operation data are collected, preprocessed and normalized; then, on the basis of a traditional grey wolf algorithm, a composite chaos initialization strategy, a crisscross optimization strategy and a Cauchy inverse cumulative distribution variation strategy are fused, and a multi-mechanism fusion optimization algorithm is constructed; secondly, establishing a first-order equivalent thermal parameter model, identifying parameters of equivalent thermal resistance R and equivalent thermal capacity C by adopting the algorithm, and taking minimization of a mean square error of a predicted temperature and an actually measured temperature as a target; and finally, performing air conditioner load aggregation modeling based on the identification parameters, and analyzing the influence of diversity of the set temperature and the initial indoor temperature on aggregation power fluctuation. Through algorithm improvement and comprehensive aggregation characteristic analysis, theoretical and technical support is provided for flexible scheduling of integrating the air-conditioning load into a modern power system.

The invention relates to an off-grid photovoltaic air conditioner self-adaptive control system and method based on rolling self-learning, and aims to solve the problems of low power supply reliability, large battery loss, inflexible temperature control, weak adaptability and the like caused by poor matching between an existing off-grid photovoltaic system and an air conditioner load. The method mainly comprises the steps that parameters are collected in real time, loads are calculated, and air conditioning unit operation frequency bands of the off-grid system are dynamically divided based on the temperature difference; constructing a multi-target model for guaranteeing power supply, prolonging the service life of the battery and optimizing the thermal comfort of the off-grid system, and adaptively adjusting the target by utilizing a dynamic weight coefficient; the optimal sequence is solved through rolling time window optimization, and the off-grid regulation and control strategy is adjusted in real time in combination with temperature gradient feedback. According to the method, the energy dynamic matching efficiency in the off-grid supply and demand system can be improved, power supply is guaranteed, fine adjustment of temperature control is achieved, harmful charging and discharging are limited based on state optimization, loss is reduced, the service life is prolonged, and compared with a traditional regulation and control strategy, the method has remarkable advantages in reliability, comfort and economical efficiency.

The invention discloses a transformer area flexible load robust optimization method and device, electronic equipment and a storage medium, relates to the field of low-voltage transformer area flexible load scheduling, and realizes gradient utilization of flexible loads and improves the distribution transformer capacity utilization rate through a multi-time scale two-stage robust optimization model and considering distributed photovoltaic uncertainty. The method comprises the steps that an air conditioner load model, an electric vehicle charge state model and an energy storage charge state model are adopted to construct a transformer area flexible load model; establishing a multi-time-scale two-stage robust optimization model based on a robust optimization algorithm and the transformer area flexible load model; solving the day-ahead stage of the robust optimization model by adopting a column constraint generation algorithm to obtain an air conditioning load, energy storage station and charging station scheduling plan of the day-ahead stage; and based on the scheduling plan of the air conditioner load, the energy storage station and the charging station in the day-ahead stage, solving the intra-day stage of the robust optimization model by adopting a solver to obtain a real-time charging and discharging power scheduling plan of the energy storage equipment and the electric vehicle.

The present application relates to an electric power regulation and control method and apparatus for an air conditioning load, a computer device, a storage medium, and a computer program product. The method comprises: acquiring temperature parameters of each target space; inputting the temperature parameters into a pre-constructed air conditioner power prediction model to obtain target power curves corresponding to the target spaces, wherein the air conditioner power prediction model is used for representing an association relationship between the temperature parameters and the air conditioner power, and the target power curves are used for indicating the changes of the air conditioner power in a target time period; and determining an electric power regulation and control strategy on the basis of the target power curves corresponding to the plurality of target spaces.

The invention relates to the technical field of air conditioner load regulation and control, in particular to a quantitative evaluation method for air conditioner load regulation and control potential in the field of modern industry, which comprises the following steps: S1, collecting and preprocessing environment data and crop information of an air conditioner system, and generating a high-dimensional feature vector containing derived quantities such as VPD and DLI; s2, predicting the transpiration rate and the disease risk based on the high-dimensional features and the crop growth stage, and achieving the dynamic representation of crop response; and S3, the prediction result and the environment, physiology and energy consumption parameters are fused, a multi-dimensional regulation potential index system is constructed, the adjustable space and depth of the air conditioner are evaluated, and regulation potential indexes and grades are output. According to the method, the multi-dimensional evaluation index system is constructed by fusing the multi-source environmental parameters and the crop response characteristics, and accurate quantification and intelligent evaluation of the air conditioner regulation and control potential are achieved.

The invention provides an air conditioning system energy efficiency optimization method based on load prediction, and the method comprises the steps: collecting air conditioning load related data, building a load prediction model which employs a support vector regression model, and training the support vector regression model through the air conditioning load related data; constructing a model function by adopting a mixed kernel function of a linear kernel and a Gaussian kernel in the support vector regression model, and obtaining an air conditioner load prediction result according to the load prediction model; an energy efficiency optimization model is constructed, the energy efficiency optimization model adopts a mixed integer linear programming model, the mixed integer linear programming model takes the total energy consumption minimization of the air conditioning system as a target function, energy consumption of various devices in the air conditioning system is considered, and an energy consumption function is established; the energy consumption function represents the energy consumption under the given control variable and the air conditioner load prediction result. The operation state and parameters of the air conditioning system are adjusted in real time according to the prediction load and the result of the optimization model, and energy-saving operation of the system is achieved.

The invention discloses a building photovoltaic integration and air conditioning system fusion system comprising a photovoltaic power generation subsystem used for converting solar energy into electric energy; the energy storage subsystem is used for storing and releasing electric energy; the air conditioning subsystem is used for adjusting the indoor environment of the building; the intelligent management and control subsystem is in communication connection with the photovoltaic power generation subsystem, the energy storage subsystem and the air conditioner subsystem and is used for collecting operation data of the subsystems, predicting photovoltaic output and air conditioner loads and executing scene-divided energy scheduling based on a model prediction control algorithm; therefore, the cooperative operation of preferentially supplying photovoltaic electric energy to the air-conditioning subsystem, storing or grid-connecting surplus electric energy, and supplementing energy by the stored energy or the power grid when the energy is insufficient is realized. According to the system, efficient production, storage and intelligent utilization of building energy can be achieved, a novel building energy system with self-generation and self-use, residual electricity storage and coordinated regulation is constructed, and the core problems that an existing building photovoltaic and air conditioning system is poor in collaboration and low in intelligent level are solved.

The invention discloses an electrical load aggregation scheduling method considering thermal dynamic characteristics of a building. The method comprises the following steps: constructing an equivalent thermal resistance-thermal capacity physical model of each single building in an office park; data are collected in real time, model parameters are updated through an online recursion identification algorithm, and a digital twin heat dynamic model is obtained; aggregating the buildings into a virtual aggregation group based on thermal dynamic response characteristics, and constructing an aggregation load response characteristic curve; double-layer optimization scheduling is executed, the upper layer solves a total air conditioner load power reference curve of each virtual aggregation group with the purpose of minimizing the total power utilization cost of the park, and the lower layer decomposes the reference curve to each single building and generates a control instruction; and calibrating the model parameters according to the fed back actual indoor temperature. According to the method, the capacity of the scheduling system for dealing with uncertainty is remarkably improved, and the physical feasibility and the actual execution effect of the scheduling instruction are guaranteed.

The invention provides a mass air conditioner load aggregator user balanced scheduling method and system based on learning optimization and differentiable game, a program product and electronic equipment. The method comprises the following steps: sensing and constructing environment information; constructing and training a deep learning model of the air conditioner load response of the lower-layer user; constructing an upper aggregator income optimization model; and solving aggregated commercial householder-slave game equilibrium. According to the method, the problems of improving the solving efficiency and the large-scale capability, realizing end-to-end differentiable optimization, ensuring the equilibrium solution quality and improving the model prediction precision and the physical rationality in the prior art are effectively solved.

The invention provides a power distribution network two-stage robust game regulation and control method and system considering an air conditioner load aggregator, and the method comprises the steps: building an air conditioner load aggregation model based on a two-parameter thermodynamic model, achieving the reasonable regulation and control of a load through the pre-starting of an air conditioner before a load peak, and meeting the comfort demands of a user; meanwhile, a battery energy storage model considering the influence of the discharge depth, the temperature and the cycle index is adopted, the cycle index of the battery can be reduced, the benefits of an energy storage system operator are improved, and practical application is better fitted; compared with a non-gaming method and a deterministic gaming method, the interactive gaming under two-stage robust optimization can effectively cope with the influence caused by distributed photovoltaic output uncertainty, the benefit balance of each main body is ensured, and the win-win situation of multiple main bodies of the power distribution network in the interactive gaming is realized; the distributed photovoltaic output is enveloped by adopting the diamond-shaped cutting convex hull uncertain set, so that a power distribution system operator not only can obtain higher benefits, but also can realize more stable voltage distribution, reduce the conservative property of the system, and improve the robustness and economical efficiency.