454 results about "Cooling load" patented technology

The invention discloses a central air-conditioning system optimization control method oriented to building load prediction, which comprises the following steps: building a building load prediction model based on a long short-term memory network, combining real-time acquired building electrical load, indoor and outdoor temperature, humidity and equipment operation power data to accurately predict building cooling load requirements in a future time period, and on this basis, optimizing a central air-conditioning system. According to the method, time-of-use electricity price information is used for dynamically adjusting a cooling capacity distribution strategy of the central air conditioning system, so that the energy consumption and the operation cost of the system are reduced to the maximum extent while the load requirement is met. Compared with the prior art, the method has the advantages that the problems of insufficient load fluctuation response and insufficient energy consumption optimization are effectively solved, and the operation efficiency and economical efficiency of the central air-conditioning system of the building can be remarkably improved.

The invention provides an energy-saving control method and device for a central air-conditioning system and a medium, and the method comprises the steps: constructing a cooling load prediction model of the central air-conditioning system, and predicting the cooling load per hour of the next day; under the conditions that the hourly cooling load of the next day is met and the power utilization cost is the lowest, the cooling capacity in a fixed time period is distributed; and an energy efficiency model and a function model of the central air-conditioning system are constructed, and the operation strategy of the central air-conditioning system corresponding to the given cooling capacity is optimized with the purpose of minimizing the power consumption cost, so that the comprehensive energy consumption of the central air-conditioning system is minimized. According to the method, the cooling load prediction result is improved by comprehensively considering various cooling load influence factors and effectively capturing the long-term dependency relationship in historical cooling load data, the comprehensive energy consumption minimization and the power consumption cost minimization of the central air-conditioning system serve as targets, the operation strategy is optimized through simulation combination and energy efficiency comparison, and the energy efficiency of the central air-conditioning system is improved. Therefore, the requirement of a target environment for an efficient and energy-saving central air conditioning system is met.

The invention discloses a load prediction method and device for a building air conditioning system, a medium and equipment, and relates to the technical field of computers. The method comprises the following steps: firstly, acquiring cold load historical time sequence data of air conditioners at different positions in a building air conditioning system and load influence data of corresponding historical time periods as sample data, and then improving based on a bidirectional time convolutional network to obtain a bidirectional space convolutional network; a cross attention module is connected in series behind a parallel structure formed by the bidirectional space convolution network and the bidirectional long-short-term memory network, a prediction model is constructed, then the prediction model is trained through sample data, hyper-parameters are optimized through a crown porcupine optimization algorithm, and a building air conditioning system load prediction model is obtained. And finally, predicting the cooling load of each air conditioner in the building air-conditioning system at the target moment based on the to-be-predicted data through the building air-conditioning system load prediction model. The cold load prediction accuracy of the air conditioners at different positions in the building air conditioning system is improved.

The invention relates to the technical field of air conditioner cooling load prediction, and discloses an air conditioner cooling load prediction method and system. The method comprises the following steps: acquiring temperature, humidity and CO2 data through an environment sensor network and carrying out standardization processing; establishing a time slice matrix based on the environmental parameters and the personnel density, and obtaining user behavior characteristics; constructing a wall heat storage model by combining the environmental parameters, and calculating instantaneous heat flow; establishing a spatial downscaling model according to the heat flow and meteorological data; time sequence features are extracted based on local weather and heat flow data, and the cooling load is predicted; and a time-phased refrigeration control instruction is generated accordingly. By establishing a data processing and analysis process, factors such as building microclimate characteristics, user behavior modes and wall dynamic thermal characteristics are comprehensively considered, and more accurate prediction of the cooling load of the air conditioner is realized.

The invention relates to the technical field of air conditioner load dispatching, and discloses a regional air conditioner cooling load dispatching system and method. According to the system and the method, external meteorological data and internal environment data are acquired by applying a temperature and humidity sensor, an air quality sensor, a human flow meter and a meteorological station through a data acquisition module, and the acquired data are processed and analyzed by a data processing and analyzing module; the cooling load scheduling strategy module is used for calculating the current cooling load demand, the load prediction value, the energy cost, the environmental impact factor, the regional building thermal load, the system energy efficiency ratio, the temperature and humidity comfort index and the overall energy consumption level, and generating an optimal cooling load scheduling scheme based on a multi-objective optimization algorithm according to the calculated values and the analysis result; and the control and execution module controls the air conditioners according to the scheduling strategy, adjusts the air conditioner output cooling load of each area, controls ventilation and air return, and optimizes air circulation.

The invention relates to the technical field of air conditioner control, and provides an indoor radiation floor system multi-target regulation and control method and system based on load prediction. The method comprises the following steps: based on indoor environment parameters, outdoor environment parameters, indoor personnel activity information and equipment parameters, adopting a cold and wet load prediction model to obtain a cold load and a wet load; based on the outdoor environment parameters, the cold load, the wet load and the air conditioning system operation information, an indoor environment and energy consumption prediction model is adopted, and the indoor temperature, the condensation temperature difference, the air system energy consumption and the water system energy consumption are obtained; the outdoor environment parameters, the cooling load, the wet load and the air conditioner system operation information serve as variables, target functions of the indoor temperature, the air conditioner PMV, the condensation temperature difference, the air system energy consumption and the water system energy consumption are built, corresponding weights are configured, and a total target function is calculated; and working parameters of the air conditioning system are set as constraint conditions, the total objective function is solved, and optimal regulation and control parameters are obtained.

The invention relates to the technical field of energy and energy conservation, in particular to a ventilation air conditioner control method based on load prediction. The technical problems that in the prior art, a ventilation air conditioner control method has certain hysteresis quality, advanced adjustment cannot be achieved, and autonomous adjustment and control cannot be conducted on the occasion with large flow are solved. According to the technical scheme, the ventilation air conditioner control method based on load prediction comprises the steps of analyzing the relation between the operation parameters of each component device of an air conditioner system and the building cooling load according to a model prediction result, fitting the change of the relation into a curve, and compiling an operation control scheme under typical working conditions according to the fitted relation curve. The building cooling load prediction value within a period of time can be obtained according to the algorithm, the system formulates the air conditioner system control strategy according to the prediction value, the problems of unreasonable building ventilation air conditioner operation mode and control lag can be solved, the operation energy efficiency of the air conditioner system is improved, on-demand cooling is effectively achieved, and the energy consumption is reduced. And intelligent building operation management is powerfully supported.

The invention relates to a chilled water system of a central air conditioner and an energy-saving optimization control method. The energy-saving optimization method comprises the following steps: S1, establishing an energy consumption model of the chilled water system of the central air conditioner; s2, firstly performing correlation analysis on the selected cooling load influence factors by adopting a grey correlation analysis method, screening out strong correlation influence factors, and establishing a central air conditioner cooling load prediction model based on PSO-LSSVM (Particle Swarm Optimization-Least Square Support Vector Machine); s3, optimizing the energy consumption model parameters of the chilled water system of the central air conditioner while predicting the cooling load value of the next point by using an improved white whale algorithm, and enabling the output result of the energy consumption model to reach the minimum value; the system comprises an energy consumption modeling module, a cooling load prediction module and an energy-saving optimization module. According to the method, the energy consumption model and the cooling load prediction model are established, the cooling load value of the next point is predicted, parameter optimization is performed, and an optimization structure is output; the energy consumption model and the cold load prediction model can be updated periodically, and the problem of performance reduction caused by long-time equipment operation is avoided.

According to the non-intrusive identification method suitable for the operation load of the central air conditioner, the central air conditioners of different brands are selected, electrical waveforms of the complete operation process of the central air conditioners are recorded, time-frequency domain characteristic analysis is carried out, common characteristics are extracted, and total daily electrical load data and daily air temperature data of a user in the past one year are selected; analyzing the incidence relation between the daily electrical load curve and the daily temperature curve of the user, obtaining a heating load area, a basic load area and a cooling load area, performing linear data fitting with the temperature to obtain a load curve, and subtracting the basic load curve from the cooling load curve to obtain a load curve; further judging whether the central air conditioner is in a starting / stopping state or not, and obtaining the electric quantity of the central air conditioner equipment in the operation period through steady-state power integration by adopting sub-item electric quantity estimation in the starting event and stopping event period of the user air conditioner; and running state tracking and electricity consumption estimation of the central air conditioner are realized.

The invention belongs to the technical field of intelligent building energy management, and particularly discloses and provides a dual-mode cold storage air conditioning system based on human flow dynamic cooperative regulation, which comprises the following steps: acquiring dynamic human flow thermodynamic diagram data in real time, and accurately calculating a multi-region linkage cold load demand value based on a density change correlation coefficient and a heat conduction model; the real-time sensing of the flow density fluctuation and the dynamic refrigeration demand matching are realized; the phase change state of the coolant of the cold storage tank and the power consumption data of the refrigerating unit are collected in real time, the cold storage release proportion, the refrigerating power increment and other key parameters are dynamically adjusted, and the energy utilization rate is increased; the inherent defect of non-uniform cold and heat in zone control is overcome by calculating inter-zone cooling capacity migration parameters; temperature feedback data and a people flow thermodynamic diagram are collected in real time, the proportionality coefficient of the fan rotating speed and the air valve opening degree is dynamically adjusted, a closed-loop learning mechanism is formed, and the matching precision of a temperature control strategy and people flow distribution is continuously optimized.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses a second-level regulation and control method and system for a refrigeration system of a data center, and the method comprises the steps: firstly carrying out the validity verification, type recognition and scale evaluation of a data request, outputting a request type and a task scale, distributing a hardware number, predicting the pre-refrigeration capacity through a pre-refrigeration model, and regulating and controlling the equipment combination of the refrigeration system to complete the pre-refrigeration; a load-energy consumption prediction model is utilized to predict a load curve of each component of a server and convert the load curve into a thermal power consumption curve, a cold load conversion model is utilized to predict a cold load of a data center, a real-time cold quantity difference value is combined to dynamically supplement and adjust a refrigeration system, and multi-stage verification and correction are performed according to temperature, cold quantity and energy efficiency data of a measuring point to generate an optimal control strategy. Finally, the optimal strategy is fed back to the pre-refrigeration model, the load-energy consumption prediction model and the cold load conversion model, and iterative optimization of the models is achieved. The response speed, the cooling capacity distribution precision and the overall energy efficiency of the refrigeration system are improved through task perception preposed pre-cooling, component-level precise regulation and control and closed-loop feedback optimization.

The invention relates to the technical field of central air conditioners, and discloses a central air conditioner water system energy-saving and consumption-reducing method which comprises the following steps: S1, acquiring operation data of a central air conditioner water system, and preprocessing the operation data; and S2, a cold load prediction model is established based on the operation data, and the model combines wavelet threshold denoising, a convolutional neural network and a long and short term memory network to accurately predict the future cold load demand. According to the energy-saving and consumption-reducing method for the water system of the central air conditioner, through the synergistic effect of a cooling load prediction model and a parameter optimization strategy, the system can dynamically match real-time load requirements, the model is combined with a convolutional neural network to extract local features and capture time correlation of a long-short-term memory network, and high-precision prediction of cooling loads in the next two hours is achieved; and compared with a traditional fixed parameter operation mode, invalid energy consumption of equipment such as a water pump and a fan is reduced, and energy conservation is achieved.

The invention discloses an air conditioner resource allocation method and system based on a cold accumulation prediction algorithm, and relates to the field of air conditioner cold accumulation, and the method comprises the steps: collecting historical load operation data of an air conditioner, analyzing the historical load operation data through employing a grey relational analysis algorithm, and constructing a cold load prediction model in combination with the cold accumulation prediction algorithm; training the cold load prediction model by using a back propagation neural network algorithm, inputting load operation data collected in real time into the trained cold load prediction model, and outputting a cold load prediction result; and in combination with the cooling load prediction result and the electricity price information, an air conditioner resource allocation strategy is formulated, and a model prediction control algorithm is used for optimizing the allocation strategy. Through integration of the load prediction technology and the real-time electricity price information, the cold load demand of the whole day can be accurately predicted, energy waste is reduced, the energy utilization efficiency is improved, the dynamic electricity price market can be responded, and economic maximization is achieved.

The invention relates to a central air conditioner energy consumption optimization control method and system, electronic equipment and a storage medium, and the method comprises the steps that characteristic parameters of load calculation are obtained, and according to the characteristic parameters, the cooling load requirements of a central air conditioner under different working conditions are calculated through a dynamic load calculation model; acquiring real-time operation data of the central air conditioner; establishing a digital simulation model of the central air conditioner based on the cooling load demand and the operation data, and calculating the energy consumption distribution of each device of the central air conditioner under different working conditions through the digital simulation model; with the overall energy efficiency optimization of the central air conditioner as the target, the optimal operation parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm; and synchronously displaying the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executing an optimized operation strategy according to a user instruction based on the optimal operation parameter combination. According to the method, the accuracy of energy consumption calculation is improved, and the overall energy consumption of the central air conditioner is reduced.

The invention is suitable for the technical field of intelligent control, and discloses a water cooling control method, system and device and a medium. Based on the preprocessed data, constructing a thermal load trend prediction model based on LSTM to obtain a cold load prediction value; based on the cold load prediction value, a PID adjusting signal is generated in combination with the temperature error value; based on the PID adjusting signal, a water pump target frequency instruction is obtained in combination with the cooling load predicted value; based on the water pump target frequency instruction, a valve target opening instruction is obtained in combination with the actually-measured water flow; based on the valve target opening instruction, the fault alarm and emergency instruction is generated, the preset emergency measures are executed, the PID control parameters are updated, the performance evaluation report and the feedback information are obtained, and the response speed, the operation stability and the autonomous decision-making capacity of the water cooling system under the dynamic load and abnormal working conditions are improved.

The invention discloses a regional cooling system supply and demand side collaborative optimization method based on a master-slave game, and the method comprises the following steps: obtaining meteorological data and date attribute data of a climate region where a selected city is located, determining a typical building type, and collecting building information; constructing a building energy consumption agent model of a user side; calculating a cooling load demand based on the building energy consumption agent model, and constructing a target function of the user side cold purchase cost based on the cooling load demand; constructing a cold supply station operation model; a supply and demand side collaborative optimization model between the cold supply station and the user side building is constructed, and the building energy consumption agent model meets the thermal comfort degree constraint; taking the maximum cold supply benefit of the cold supply station and the minimum building cold purchase cost of the user side as optimization objectives, and performing a master-slave game between the cold supply station and the cold users; and solving to obtain a supply-demand side collaborative optimization result. According to the method, the demand side model is incorporated into the optimization problem to form global optimization, the flexibility of the system can be effectively improved, and the operation stability of the regional cooling system in an extreme scene is improved.

A composite radiative cooling material includes a first layer including a reflective material, a second layer including a porous material, and a third layer including an emissive material. The optical properties of the respective materials and the arrangement of the respective layers cause the radiative cooling material to exhibit a total solar reflectance greater than 85%, and a thermal emissivity greater than 85% in a wavelength range of 8 to 13 μm. The layers may be in a vertically stacked arrangement, with the third layer capable of directly facing the sky when the composite radiative cooling material is installed for cooling a load, the second layer arranged under the third layer, and the first layer arranged under the second layer. The composite radiative cooling material may be thermally coupled to a cooling load to provide radiative cooling to the cooling load.

The invention provides a power transformer electromagnetic loss control method and device in an intelligent power grid environment, and relates to the field of intelligent power grids, and the method comprises the steps: carrying out the cooling demand prediction of a transformer according to a predicted power load sequence and a space temperature sequence, and outputting a predicted cooling load sequence; and with meeting of the predicted cooling load sequence as a constraint and minimization of energy consumption and oil temperature fluctuation as optimization targets, cooling parameters of the forced oil cooling device are optimized, an optimal cooling parameter sequence is output, and the forced oil cooling device is controlled to dissipate heat of the transformer. The method aims to solve the technical problems that a traditional control method lacks cooling strategy dynamic adjustment capacity, temperature rise abnormity is prone to occurring in a high-load or high-temperature environment, transformer iron core loss and winding loss are remarkably increased, and electromagnetic loss is high, accurate and efficient cooling control can be achieved, the electromagnetic loss is effectively reduced, and the service life of a transformer is prolonged. And the operation efficiency and stability of the transformer are improved.

The invention discloses a wind, light, electricity, heat, cold air and hydrogen optimal scheduling method for an off-grid zero-carbon park, and the method comprises the steps: firstly, building an off-grid zero-carbon park system model, the system comprises energy supply equipment, energy conversion equipment and energy storage equipment, the energy source is renewable energy wind energy and light energy, and the load types comprise an electric load, a heat load and a cold load; establishing a mathematical model and operation constraint conditions according to the system model, and establishing an optimization objective function for the system; describing an optimized scheduling reinforcement learning framework of the off-network zero-carbon park system as a Markov decision process; on the basis of an improved flexible actor-commentator algorithm, advantage learning, transfer learning and an SAC algorithm are combined to solve the model. The advantages of multi-energy complementation are fully utilized, and the economical efficiency and the environmental protection property of the system are improved; and an improved flexible actor-commentator algorithm is adopted, so that the training time is shortened, the algorithm iteration speed is accelerated, and the flexible and efficient scheduling of the integrated energy system is realized.

The invention discloses a new energy building integration energy-saving design analysis method, and relates to the technical field of new energy building integration, and the method comprises the following steps: carrying out the time sequence alignment and unit normalization processing of collected heat flow data, temperature rise data and power output data; according to the heat conduction characteristic of the heat conduction structure and the thermal inertia parameters of the building enclosure structure, a heat flux track and a thermal response profile with a unified thermal resistance reference axis are constructed; and based on the heat flux track and the thermal response profile, extracting a reverse heat flux strength parameter and a heat flux continuous influence duration parameter, and combining the parameters with a temperature control target and a heat conduction structure attribute set in the building to construct a reverse heat conduction interference atlas for judging the heat conduction interference degree of the cold load in the building. The problem of cold load misjudgment caused by the fact that photovoltaic heat is transmitted through a high-heat-conduction structure is solved, and cold and heat conduction accurate recognition and energy consumption dynamic regulation and control are achieved.

The invention discloses a hospital public building temperature refined energy-saving regulation and control method, which comprises the following steps: establishing a refined hierarchical management architecture based on use requirements and energy use purposes of different wards; for a conventional inpatient ward, door and window opening and closing state data and ward heat state data are collected, and air conditioner operation control and door and window opening and closing state adjustment are executed based on a window magnetic control air conditioner operation model and by adopting a multi-step prediction control method; for different types of special wards, regulating and optimizing corresponding thermal equipment by adopting a multi-step prediction control method under an MPC model prediction control framework according to thermal parameters required to be controlled by the current type of ward; for outpatient wards, outpatient air conditioner output operation is optimized by adopting a multi-step prediction control method based on a cooling load prediction model according to the flow density, the temperature and the indoor and outdoor temperature difference; according to the invention, monitoring and accurate prediction of the thermal condition in the ward are realized based on the model prediction control MPC method in combination with the window magnetic switch.

The invention discloses a self-adaptive regulation and control method for an air conditioning system based on information matching. The self-adaptive regulation and control method mainly comprises the following steps that firstly, a data set used for building cold load prediction is divided into a low-completeness data set and a high-completeness data set through a proposed information completeness judgment method; and for the high-completeness data set, regulating and controlling the air conditioning system by adopting a model prediction control method according to the load prediction model and the air conditioning equipment model. And for a low-completeness data set, a proposed migration imitation learning regulation and control method based on deep reinforcement learning is adopted. According to the method, the influence of data quality on the model prediction precision and the regulation and control effect is considered, the influence of fluctuation of historical data quality on the regulation and control effect can be overcome, the relative advantages of the model prediction control method and the reinforcement learning method are integrated, compared with a single regulation and control method, the energy-saving and cost-saving effects can be improved, and good robustness is achieved.

The invention discloses an efficient refrigerating machine room performance optimization method and device, and relates to the technical field of intelligent energy-saving optimizing.The efficient refrigerating machine room performance optimization method comprises the steps that operation data of refrigerating equipment are collected through various sensors and preprocessed; based on the preprocessed operation data, constructing an MT-LSTM model to predict a future cold load demand, and generating a cold load demand prediction value; on the basis of the cold load demand predicted value, generating an optimal scheduling strategy through an NSGA-II optimization algorithm; and according to the optimal scheduling strategy, equipment starting and closing and load proportion distribution are executed through IoT, operation data are monitored in real time, and the operation state of the refrigeration equipment is dynamically adjusted and optimized. According to the method, the future cold load demand is predicted by constructing the MT-LSTM model, and accurate demand prediction is realized.

The invention provides a control method and equipment of a cold storage system and the cold storage system. The control method comprises the steps that the accumulated cold storage capacity of the cold storage tank in the previous day is obtained; acquiring 24-hour hourly electricity price per day; obtaining a cooling load demand hour-by-hour value in the next 24 hours in the previous day through a prediction technology; and according to the accumulated cold storage capacity of the previous day, the 24-hour hourly electricity price and the cooling load demand hourly value of the next 24 hours, cold release control is carried out on the cold storage tank with the goal of the lowest all-day operation electricity price. The dynamic cold release potential based on different electricity prices can be brought into full play, and the operation cost is saved to the maximum extent.

The invention discloses a medical water chilling unit internet-of-things edge control method, which comprises the steps of obtaining water chilling unit operation parameters, cooling tower operation parameters and environmental parameters of different types of areas of a hospital, and inputting the water chilling unit operation parameters, the cooling tower operation parameters and the environmental parameters of the different types of areas into a cooling load prediction model, future cooling load prediction values of different types of regions are obtained; inputting the running parameters of the water chilling unit, the running parameters of the cooling tower and the future cooling load predicted values of the different types of regions into a deep reinforcement learning model to obtain adjustment strategies of the water chilling unit and the cooling tower of the different types of regions; dynamically adjusting distribution paths and distribution priorities of cooling water and chilled water according to different types of regions, future cooling load predicted values and adjustment strategies of a water chilling unit and a cooling tower; and controlling the operation of the water chilling units and the cooling towers in the corresponding regions through the adjustment strategies of the water chilling units and the cooling towers in the different types of regions, the cooling water and chilled water region distribution paths and the distribution priorities thereof.

The invention relates to a data center equipment type selection method based on electrical and heating ventilation load linkage, and belongs to the technical field of data center infrastructure design. The model selection method comprises the steps of setting basic data, obtaining the number of machine rooms and the number of IT devices in a single machine room, obtaining the total power of the IT devices, setting an energy efficiency index set value, obtaining the project power consumption scale, obtaining the total cooling load, selecting models of air conditioners at the tail ends of the machine rooms and substations, selecting models of water chilling units, selecting models of heat exchangers, selecting models of cooling water pumps and chilled water pumps and selecting models of cooling towers. And obtaining the total power of the air conditioner, obtaining an energy efficiency index accounting value, and carrying out PUE verification. According to the method, through closed-loop iteration of electrical load-heating and ventilation load-PUE, model selection and quantity selection of all key equipment are completed at a time; professional splitting, repeated adjustment and modification and capacity redundancy caused by traditional serial design are avoided, the design period is remarkably shortened, and initial investment is reduced.

A method for controlling HVAC equipment includes using a dynamic thermal model that describes heat transfer characteristics of a building to predict a temperature of the building as a function of control inputs to HVAC equipment that operate to affect the temperature of the building. The method includes obtaining time-varying utility prices and a predicted heating or cooling load for the building and performing a predictive control process using the dynamic thermal model, the time-varying utility prices, and the predicted heating or cooling load to generate the control inputs to the HVAC equipment subject to constraints on the temperature of the building.

The invention relates to the technical field of electrical performance testing, in particular to a method and system for measuring loss of an inductance winding, and the method comprises the following steps: applying a direct current to a measured inductance winding, synchronously monitoring the temperature, and multiplying a pre-measured direct current resistance value by a direct current value to obtain a direct current loss power value. The direct current is applied to the inductance winding, the temperature is monitored synchronously, the direct-current loss power is obtained by combining the direct-current resistance value and the current value, the temperature curve in the initial stage is extracted, the temperature rise rate is calculated, and therefore the winding heat capacity parameter is obtained, loss measurement can be closely associated with thermal response, and the measurement accuracy is improved. Introduction of a heat capacity factor is added in loss calculation, dynamic characteristics of a winding material on a thermodynamic level can be reflected, instantaneous current with load step change is applied after an inductor is cooled, an instantaneous loss power curve is generated by collecting a voltage sequence and a current sequence at the same time, and loss calculation is not limited to average value or steady state measurement any more.