268 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.

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 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.

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 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 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.

The invention discloses an air conditioner adjustable potential prediction method based on a cloud-side collaborative architecture, electronic equipment, a storage medium and a product, comprising a cloud end deployed on a server and a side end deployed on user side computing equipment, the cloud end shares side end data, and the cloud end performs prediction on the adjustable potential of an air conditioner based on historical air conditioner load time sequence data. Obtaining a bidirectional LSTM prediction model capable of predicting an air conditioner load value at a specific time in the future, and carrying out iterative optimization on the bidirectional LSTM prediction model based on air conditioner load time sequence data collected by an edge end in real time; and the side end carries out lightweight processing on the bidirectional LSTM prediction model after iterative optimization, deploys the bidirectional LSTM prediction model on user side computing equipment, and carries out air conditioner load value prediction based on air conditioner load time sequence data collected in real time to further obtain an air conditioner adjustable potential prediction result. According to the method, for the problems that an existing prediction model is insufficient in generalization ability and difficult in edge deployment, the cross-scene adaptability is improved through MMD transfer learning, ONNX quantization and Docker containerization are combined, and edge lightweight and standardized efficient deployment is achieved.

The invention discloses a DLC hierarchical optimization scheduling method considering air conditioner load state difference, and relates to the field of power system optimization scheduling, and the method comprises the steps: building a double-layer interaction model of a power supply company, a load aggregator and a user; establishing a load aggregator output model, and determining the controllable capacity of the load aggregator output model; establishing a clustering grouping method considering air conditioner load state differences; establishing an air conditioner load double-layer scheduling optimization model; and solving based on a multi-strategy improved whale optimization algorithm to obtain the start-stop state of each group of air conditioner load. The method mainly solves the problems of low regulation and control precision and non-ideal response effect caused by uncertainty of the initial state of the air conditioner load and parameter difference, improves the response speed and execution reliability through central coordination of the load aggregator, also considers the comfort demand of a user, and has important value for enhancing the dispatching efficiency of a power grid and stable operation.

The invention relates to an air conditioner load prediction method based on a self-adaptive double-flow graph attention network, and belongs to the technical field of building energy conservation and intelligent control. The method comprises the following steps: collecting historical power and environmental data of an air conditioner, and after preprocessing and normalization, constructing an input sequence through a sliding window and dividing a data set according to time; a prediction model is constructed, and a causal graph learning module, a multi-scale graph structure learning module, a self-adaptive space-time attention module, an uncertainty quantization module and a self-adaptive sampling module are integrated; a training set and a joint loss function training model are adopted, and a load prediction result and uncertainty estimation are output through Monte Carlo Dropout during testing. According to the method, the dynamic causal relationship between variables and multi-scale space-time dependence can be adaptively learned, reliable uncertainty quantification is provided while the prediction precision is improved, and the method is suitable for intelligent regulation and control and energy efficiency optimization of the air conditioning system.

The invention provides an airport terminal building heating, ventilation and air conditioning load prediction system and method based on a micro-service architecture. The method comprises the following steps: collecting multi-source heterogeneous original data and extracting a key feature set; mining space-time relevance between people flow and equipment operation, and outputting a space-time characteristic matrix; and constructing a multi-algorithm fusion framework, carrying out cross-terminal distributed cooperative training and dynamic weight aggregation through federal learning, introducing a lion group optimization algorithm to optimize global parameters, and outputting an airport terminal heating ventilation air conditioning load prediction result. Fusion data are standardized, key features are extracted in combination with automatic feature engineering, and the data utilization rate and feature relevance are improved; then capturing a space-time association mode by using a space-time diagram convolutional network, constructing a multi-algorithm fusion framework and dynamically allocating weights; then realizing distributed cooperative training by adopting federal learning, and optimizing parameters in combination with a lion group optimization algorithm to enhance the generalization ability of the model; and finally, through incremental learning dynamic iterative optimization, the prediction precision is continuously improved.

The invention discloses a commercial air conditioner load dynamic influence factor prediction method and device. The method comprises the following steps: firstly, collecting historical data according to a power load four-level classification system, and constructing a historical impact factor time sequence; then decomposing the sequence into four components including a reference value, a medium and short term value, a short term value and a disturbance value through multi-scale decomposition; on the basis of the reference value sequence and the prediction day features, similar days are screened through grey correlation analysis, and a training data set is constructed; aiming at the characteristics of different time scale components, a plurality of machine learning models are adopted to train prediction sub-models respectively; and finally, fusing prediction results of the components by optimizing weights to generate a dynamic impact factor time sequence. According to the method, through a multi-scale decomposition and model cooperation mechanism, prediction precision and adaptability are effectively improved, and a reliable basis is provided for fine scheduling of a power system.

The present invention relates to a household low-carbon energy optimization method and system that considers green electricity consumption and V2H. The method includes: collecting historical household electricity consumption statistics, establishing a carbon emission baseline scenario for household electricity consumption, and constructing a carbon credit emission reduction accounting model based on the emission reduction scenario; establishing a load flexibility optimization model based on the household's non-temperature-controlled flexible load; constructing an air conditioning load model; constructing an electric vehicle load model based on user commuting characteristics, electric vehicle operating parameters, and charging pile operating parameters; and constructing a household energy economic low-carbon optimization model based on the established model. The model is solved with the goal of minimizing electricity purchase costs and comfort losses and maximizing carbon emission reductions, thereby obtaining an optimized household economic low-carbon energy consumption plan. Compared with the existing technology, the present invention achieves quantification of the carbon reduction potential contained in household low-carbon energy consumption through multiple household green electricity consumption methods, scientifically guides household users to economical, low-carbon and comfortable electricity consumption, and facilitates the achievement of energy conservation and carbon reduction goals.

The application discloses an air conditioner load aggregator demand response optimization method and device based on user credit, which comprises the following steps: constructing a user credit evaluation model according to user subscription performance indicators, evaluating the response behavior performance of users, and obtaining user credit evaluation indicators; constructing a building room temperature thermal dynamic model based on a thermal resistance-thermal capacity network model; describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model; constructing a load aggregator demand response double-layer optimization model based on the user credit evaluation indicators; solving the optimization model through a set solving strategy, and obtaining an optimal load aggregator dispatching strategy; constructing a credit updating model; and updating the user credit evaluation indicators through the credit updating model according to the user response performance indicators after the power grid demand response, and obtaining the updated user credit evaluation indicators. The application can improve the effect of demand response and realize fine dispatching.

The present application provides a method and device for constructing an air-conditioning load aggregated power model and a power scheduling method. By introducing an air-conditioning load aggregated power drop process model that describes the change in the output power of the air conditioner when the preset temperature of the air conditioner changes, and adding the change in the output power of the air conditioner when the preset temperature of the air conditioner changes to the calculation of the air-conditioning load aggregated power, an air-conditioning load aggregated power model is established. Since the change in the output power of the air conditioner when the preset temperature of the air conditioner changes is considered, the accuracy of the air-conditioning load power calculation is improved, and the situation of instantaneous overload of the power system load power caused by inaccurate load power calculation is reduced. At the same time, in the power scheduling method, the demand curve is used as the connection interface between the air-conditioning load and the outside, which can better protect the data privacy of users and improve the security of control.

The present application relates to the electric vehicle charging and discharging resource and air conditioner load mutual benefit potential evaluation technical field, relates to the demand side resource mutual benefit potential evaluation method and system considering the space-time coupling characteristics, including: obtaining the operation data of electric vehicle and air conditioner load;Build economic evaluation index model and feasibility evaluation index model, based on economic evaluation index model and feasibility evaluation index model, analyze the electric vehicle and air conditioner load operation data, and solve the subjective weight and objective weight of the analysis result;The combined weight matrix is obtained by combining weighting method, the combined weight matrix is multiplied by the normalized analysis result, and the demand side resource mutual benefit potential evaluation of electric vehicle and air conditioner load is completed.The present application can not only improve the utilization efficiency of electric vehicle as mobile energy storage, but also effectively alleviate the pressure of air conditioner load on power grid during peak period, so as to enhance the stability and flexibility of power grid.

The invention discloses an intelligent flexible regulation and control method and system. The method comprises the following steps: acquiring a user setting demand; an air conditioner load flexible adjusting operation mode is set according to user setting requirements; collecting air conditioner load data, environment data and operation state data in real time; based on the air conditioner load flexible adjustment operation mode, a flexible adjustment instruction is obtained according to the air conditioner load data, the environment data and the operation state data; and the flexible adjusting instruction is sent to a preset air conditioner control module so that the air conditioner unit can be flexibly adjusted. According to the user requirements and the environment where the air conditioner is located, the load and the environment are comprehensively analyzed, different regulation and control schemes are formulated, and therefore the electric energy loss of the air conditioner is reduced.