247 results about "Energy demand" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention belongs to the technical field of intelligent energy control, and particularly relates to an energy equipment control method based on regional people number distribution, which comprises the following steps: acquiring real-time people number distribution data, environmental parameter data, historical energy consumption data and historical people flow characteristic data, and predicting regional people number distribution data in different time periods; based on historical energy consumption data, historical people flow characteristic data, real-time people number distribution data and environmental parameter data, a dynamic energy demand model is constructed, regional people number distribution data in different time periods are combined, a people number density thermodynamic diagram is generated, regional energy equipment load change is predicted, and a regional energy demand model is established according to the load change and the people number density thermodynamic diagram. A differential control strategy is generated, and operation parameters of the energy equipment are dynamically adjusted; and regulating and controlling the energy equipment according to the differentiated control strategy, synchronously collecting regulated and controlled regional environment feedback data and equipment energy consumption data, and generating an energy efficiency evaluation report. Therefore, the problems of high response delay, limited parameter matching precision and the like in the prior art are solved.

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention relates to the technical field of power system regulation and control, in particular to a park multi-resource intelligent flexible control method and device considering energy and carbon consumption collaboration, and the method comprises the steps: obtaining historical energy consumption data and historical carbon emission data in a target park; training the historical energy consumption data and the historical carbon emission data based on a feature-level constraint preference optimization algorithm to obtain a park multi-energy prediction model based on feature-level constraint preference optimization so as to predict energy demands in a future time period; constructing a park multi-resource optimization scheduling model according to the energy demand in the future time period; and based on a preset energy storage equipment charging and discharging strategy and a preset flexible load control strategy of the target park, generating a multi-resource energy-carbon cooperative flexible control strategy of the target park according to the park multi-resource optimization scheduling model. Therefore, the problem that an existing cooperative control method cannot meet the comprehensive requirements of the modern park in the aspects of energy conservation, carbon reduction, cost optimization and system stability is solved.

The embodiment of the invention discloses an electricity-hydrogen-heat energy system construction method for a multi-type energy demand scene. The method comprises the following steps: constructing a system simulation model corresponding to an electricity-hydrogen-heat energy system, and determining a target limiting condition corresponding to the system simulation model in a target energy demand scene based on a target cascade analysis method; and finally, determining system target equipment parameters of the electricity-hydrogen-heat energy system under the target limiting condition according to the annual total construction resource function, and constructing a target electricity-hydrogen-heat energy system according to the system target equipment parameters. Through the construction method of the target electricity-hydrogen-heat energy system, the optimal system equipment parameters are automatically and correspondingly determined while the multi-type energy requirements in different scenes are met, the system construction cost is reduced, heat recovery of the hydrogen fuel cell is achieved by introducing the heat storage tank, the renewable energy utilization efficiency of the system is improved, and the energy utilization efficiency of the system is improved. And the use satisfaction of the user is improved.

The invention relates to the technical field of energy and power engineering, and discloses an efficient thermoelectric decoupling system based on phase change energy storage, which comprises an energy storage unit module, a regulation and control algorithm module and a scheduling optimization module. The energy storage unit module adopts a nanotechnology novel phase change material, so that the energy storage density and stability are improved; the regulation and control algorithm module realizes accurate control of charging and discharging through a multi-stage regulation and control strategy; and the scheduling optimization module predicts energy demand and production by using a machine learning algorithm, and dynamically adjusts an energy distribution strategy. The problems that a traditional heat storage material is low in energy storage density and poor in stability and lacks an intelligent scheduling mechanism can be solved, the energy utilization efficiency and operation reliability of a thermoelectric system are improved, and the energy cost is reduced.

The invention discloses a hospital energy resource intelligent management method and related equipment. The method comprises the following steps: predicting the output of renewable energy and the energy demand of each department in a future time period by using an LSTM-Attention model; on the basis of a dynamic mixed AHP analytic hierarchy process, the department energy consumption priority is evaluated by comprehensively considering the emergency state, the number of patients, the equipment operation state, historical energy consumption data and multi-dimensional factors of special medical tasks; and formulating an energy scheduling strategy through a multi-objective optimization model by combining the real-time energy consumption demand and the renewable energy source yield of the hospital according to the department energy consumption priority evaluated by the dynamic hybrid AHP method. The problems that due to insufficient application of clean energy and energy storage technology in hospitals, hospital operation cost is high, carbon emission is increased, a traditional system is difficult to dynamically adapt to complex energy consumption requirements, and stable energy supply of key areas cannot be guaranteed can be solved.

A port collaborative scheduling method based on an information gap decision theory comprises the following steps: S1, establishing a port integrated energy system framework model for a port, and finally constructing a berth logistics system; s2, collecting equipment parameters in the port integrated energy system framework model; s3, establishing a logistics side minimum energy consumption cost target under berth-logistics-energy collaborative scheduling; s4, establishing a minimum energy consumption cost target of a berth side under berth-logistics-energy collaborative scheduling; s5, establishing a common coupling electric energy balance relation; s6, establishing and solving an overall target under berth-logistics cooperative scheduling; s7, performing modeling and solving under uncertainty based on an information gap decision theory; according to the method, the ship energy demand, the container throughput dynamic and the logistics demand are integrated, and meanwhile, the operation limitation of the port comprehensive energy system is considered. And an electricity-hydrogen coupling transportation method is adopted to dynamically optimize hydrogenation, battery replacement time windows, logistics operation and berth allocation under time-of-use electricity price.

The invention discloses a multi-element flexible load feature extraction method based on hydrogen energy demand response, and relates to the technical field of hydrogen energy demand resource planning. In order to construct an optimization control model responding to a power grid signal, hydrogen production efficiency maximization and power utilization planning rationalization are realized. The method specifically comprises the following steps: obtaining new energy power generation power characteristic data, predicting daily average generating capacity data according to the data, obtaining all-day time-of-use electricity price characteristic data of a commercial power grid, and dividing the data into peak / flat / valley time periods; according to the multi-element flexible load feature extraction method and the control strategy, the electric energy use strategy and the hydrogen production and hydrogen supply equipment operation strategy suitable for the whole hydrogen energy supply project can be formulated according to the stirring filtering of the new energy generating capacity, so that the energy consumption is reduced on the premise that the stable and sufficient hydrogen supply amount is ensured. The power consumption of the hydrogen energy supply project in the peak period and the flat period of the power grid is effectively reduced, and the comprehensive energy efficiency and the green power consumption rate of the hydrogen energy supply project are effectively improved.

The invention discloses an air conditioner energy demand control method and system, and the method comprises the steps: obtaining a start-stop signal, carrying out the initialization processing of a compressor based on the start-stop signal, and obtaining the nominal performance of an indoor unit and the single rotation speed of the compressor; the real-time environment temperature is obtained, and a first indoor unit function demand value is determined based on the real-time environment temperature, indoor unit nominal performance, the compressor single rotating speed and a preset mapping rule; obtaining the running state of the compressor, and obtaining a second indoor unit energy demand value based on a preset energy demand prediction model, the real-time environment temperature and the running state of the compressor; the energy demand prediction model is constructed based on a machine learning algorithm and historical operation data; performing weighted fusion on the first indoor function demand value and the second indoor function demand value to determine a target indoor function demand value; and the target rotating speed is calculated based on the target indoor unit energy demand value, a control instruction is generated based on the target rotating speed, and air conditioner energy demand control is completed based on the control instruction, so that the air conditioner system dynamically responds to the indoor thermal load and accurately adjusts the indoor thermal load.

The invention discloses a direct-current off-grid renewable energy hydrogen production energy management method and system, and relates to the technical field of renewable energy management, and the method comprises the steps: constructing a hydrogen production energy management module of a direct-current voltage network; carrying out load characteristic analysis, obtaining the energy demand of the hydrogen production facility, and identifying a high demand time period and a low demand time period; collecting wind power and sunlight data, and evaluating potential output capability of wind power generation and photovoltaic power generation; and determining a wind turbine, a photovoltaic panel and energy storage equipment based on an analysis result of the energy demand and the output capability, determining a hydrogen production operation mode, and performing energy management. According to the direct-current off-grid type renewable energy hydrogen production energy management method, the energy utilization efficiency is improved, energy waste in off-peak periods is reduced, adaptability and stability are enhanced, and different situations of energy peak and valley are dealt with. Energy storage and output strategies are optimized, operation cost is reduced, economic benefits are improved, and energy requirements are met.

The invention discloses a power distribution network new energy storage and demand side joint response system and method, and belongs to the technical field of energy distribution, and the system comprises a new energy storage module which is used for providing an energy storage place and can carry out charging and discharging modes; the demand side response module is used for acquiring associated information related to the energy demand; and the cooperative control platform is internally provided with a processing end, an analysis end and a regulation and control end, the analysis end is used for carrying out preprocessing operation on the obtained information, the analysis end is used for analyzing the processed information and judging whether energy regulation and control need to be carried out on a demand side, and the regulation and control end carries out corresponding energy regulation and control based on a result of the analysis end. According to the invention, comprehensive analysis can be carried out according to the frequency and voltage change of the power grid and the output power of the new energy output system to more accurately judge the energy supply and demand state condition of the whole power grid, so that a key basis is provided for an energy scheduling decision and reasonable energy distribution is ensured.

An energy conversion device enclosure system and method that, in certain implementations, may include an expandable system for enclosing one or more generators having a first set of mounting members positioned along a support surface, and a second set of mounting members positioned parallel to the first set. Removable enclosure panels may be coupled between the mounting members, with a first panel featuring a fuel access opening and a second panel having an air intake opening with louvers to provide air flow from one side of the energy conversion device enclosure to the other. Telemetry subsystems may be integrated into a third panel, while a fourth panel is substantially perpendicular to the first and second panels. The fourth panel may be removable to facilitate the expansion of the system in response to future increased energy demand.

The invention relates to the technical field of industrial intelligent control, in particular to an electric discharge machine intelligent scheduling and production regulation and control system which comprises a staggered analysis module, a station mapping module, a task distribution module, a trend prediction module and an instruction regulation and control module. According to the method, the processing task parameters and the station bearing parameters of different dimensions are standardized and uniformly mapped, and then the spatial distance is calculated to accurately quantify the matching degree between the tasks and the stations, so that the limitation of static distribution can be eliminated, and redundant production resources are identified and eliminated by analyzing the change of the matching degree; according to the energy demand and time urgency dynamic remapping of a task, simplification and efficiency improvement of production configuration are achieved, meanwhile, the future abrasion risk can be foreseen by collecting key component contour data in real time and converting the key component contour data into a quantitative integrity index, establishing a state dynamic change track and conducting trend extrapolation, and the method has the advantages of being high in practicability and the like. And finally, carrying out comprehensive decision making according to the prediction result and the real-time load margin.

The invention discloses a comprehensive energy supply system reliability detection method considering state reverse analysis. The method comprises the following steps: firstly, constructing an energy conversion model of a multi-energy coupling element in a comprehensive energy supply system; thirdly, constructing an output power model of the multi-energy coupling element based on the energy conversion model of the multi-energy coupling element and initial input energy of the comprehensive energy supply system; and finally, the reliability of the comprehensive energy supply system is rapidly detected by using the output power model of the multi-energy coupling element. The method provided by the invention focuses on the component combination of which the output power is smaller than the energy demand to realize the rapid evaluation of the reliability of the comprehensive energy supply system.

The embodiment of the invention provides a multi-split air conditioner control method, a controller, a multi-split air conditioner and a medium. The multi-split air conditioner control method comprises the steps that the indoor temperature, the maximum rated energy requirement and the minimum rated energy requirement of all indoor units in the running state are obtained; the temperature difference value between the indoor temperature and the set temperature and the energy demand difference value between the maximum rated energy demand and the minimum rated energy demand are determined; when the temperature difference value and the energy demand difference value meet the preset correction condition, a target indoor unit with the difference value between the maximum rated energy demand and the target indoor unit reaching the preset energy demand difference value is determined, the target superheat degree of the target indoor unit is corrected, and the opening degree of a throttling assembly corresponding to the target indoor unit is adjusted according to the target superheat degree; and the target temperature of an indoor unit coil of the multi-split air conditioner is determined according to the temperature difference value of the indoor units, and the operation frequency of a compressor is adjusted according to the received actual temperature of the indoor unit coil and the target temperature of the indoor unit coil. According to the embodiment of the invention, temperature fluctuation can be reduced, and the thermal comfort of a user is improved.

The embodiment of the invention provides a baking waste heat recovery method and system based on multistage heat exchange, and belongs to the technical field of waste heat recovery and utilization. The method comprises the steps that exhaust operation parameters in the baking process are obtained, and a plurality of thermotechnical stages and stage identifiers corresponding to the thermotechnical stages are obtained; determining a heat grade index and a dew point difference of each thermal stage based on the stage identifier, and generating a cascade sequence and a valve position control parameter corresponding to each thermal stage; calling the corresponding cascading sequence and the valve position control parameters according to the stage identifiers to execute sequential heat exchange, and calculating to obtain a heat recovery result of each thermal stage; and according to the stage identifier and the production line rhythm, the heat recovery result is distributed to the heat utilization link according to the priority matrix, and the priority matrix and the distribution quantity are dynamically corrected based on the energy demand during stage switching. According to the scheme, dynamic matching of heat energy and self-adaptive control of condensation risks in the baking process are achieved, so that the waste heat utilization rate is increased, and the temperature field in the furnace is kept stable.

The invention relates to an energy storage optimization configuration method based on a green power direct connection project. The method comprises the following steps: step 1, determining an optimization configuration limiting condition and a price mechanism, wherein the core of source, load and storage configuration is a green power direct connection system so as to meet the green energy demand of an enterprise and improve the nearby local consumption level of new energy; 2, green power direct connection project planning configuration: exploring an optimal scheme under a differentiated scene by applying a production operation simulation method and taking system economy optimization as a target, analyzing the influence of a source storage configuration scale on project economy, and considering surrounding actual resource conditions to obtain a final feasible scheme; 3, analyzing a green electricity direct connection project planning scheme based on examples; the method has the advantages that the new energy utilization rate is increased, the cost is reduced, and reasonable distribution and storage are realized based on a production simulation method.

The multi-mode ultra-low energy consumption building kitchen makes-up air exhaust air coupling multi-stage heat recovery system relates to the kitchen ventilation technical field.The present application solves the problems of high operation cost and maintenance workload of the existing kitchen heat recovery system, increases the complexity and installation difficulty of the system, and affects the heat recovery efficiency of the system. In the normal working mode, the make-up air and exhaust air system runs simultaneously, fresh air enters the kitchen through the make-up air channel, and waste air is discharged through the exhaust air channel. When the temperature in the exhaust air exceeds the set threshold, the control system will trigger the heat energy recovery mode. In this mode, the excess heat energy will be introduced into the heat energy storage tank to store the excess heat. During the energy demand peak period, the control system will start the energy supply mode. At this time, the heat energy in the heat energy storage tank can be released to supply the additional heat energy demand. The present application realizes heat energy recovery and efficient use of energy by coupling the kitchen make-up air and exhaust air system, heat exchange and energy storage device.

The invention provides a Java-based diesel generator intelligent start-stop and power dynamic distribution dynamic adaptive method, and belongs to the technical field of hybrid energy management, and the method comprises the steps: S1, importing photovoltaic data and load data, and processing the photovoltaic data and the load data to the same granularity; s2, establishing a dynamic power gap calculation model, and providing an accurate data basis for diesel generator intervention based on photovoltaic and load prediction data; s3, starting a time window fusion intelligent decision algorithm, analyzing the time sequence characteristics of a power gap, replacing multiple times of short-time start and stop with single-time long-time operation, and improving the load rate of each time of operation; and S4, deriving a result through a start-stop time reverse derivation mechanism based on the total energy, firstly determining the moment when the diesel engine needs to stop power supply, and then reversely calculating the latest start moment according to the total energy gap and the rated power of the diesel engine in the window, thereby ensuring that the operation duration of the diesel engine is accurately matched with the actual energy demand, avoiding the waste of fuel oil, and reducing the energy consumption of the diesel engine. And high efficiency and economical efficiency of the operation process are realized.

A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

Described is a process for the recycling of polycarbonate from particulate polycarbonate waste comprising a) providing the particulate polycarbonate waste, b) solvent-based purification of the particulate polycarbonate waste, c) partially depolymerizing of the purified polycarbonate, d) repolymerizing of the partially depolymerized polycarbonate. The process of the invention is an improved and adjustable process for the recycling of polycarbonates in high quality and purity, which is applicable on a technical and commercial scale at a relatively low energy demand.

This application relates to the field of power grid optimization dispatching technology, specifically to a multi-objective optimization dispatching method and system for active distribution networks with source-grid-load-storage coordination. The method includes: collecting the energy storage capacity of each renewable energy power station in the active distribution network, and the energy dispatched by each transformer from each renewable energy power station; calculating the priority dispatching characteristics of each renewable energy power station; predicting the future energy demand of each transformer and the future energy storage capacity of the renewable energy power stations supplying them; coordinating the transmission distances between each transformer and the renewable energy power stations supplying them; determining the dispatch matching coefficient between the renewable energy power stations and transformers; based on the dispatch matching coefficient, constructing an objective function with the goal of minimizing the source-grid-load-storage coordinated dispatching cost and maximizing renewable energy consumption; and using a multi-objective optimization algorithm to solve for the optimal solution, which is then used for power dispatching. This improves the rationality of power resource dispatching in the distribution network.

Hardware distribution and energy efficient scheduling using digital emissions data may include determining an emissions load target for an asset, wherein the emissions load target indicates a limit on digital emissions related to operation of the asset; determining, based on energy utilization data, a future energy utilization projection for the asset; generating, based on the future energy utilization projection, a digital emissions forecast for the asset based on digital emissions attributable to the asset; and alleviating an energy demand of a workload executing on the asset in response to determining that the digital emissions forecast exceeds the emissions load target

The invention discloses a whole-house intelligent energy storage system management method based on energy demand prediction, and relates to the technical field of energy management, and the method comprises the following steps: 1, carrying out the statistics of the energy demand of a whole house, carrying out the statistics of electric equipment and power consumption in the statistics process, and carrying out the centralized collection of the data after statistics; 2, after the collection of the data of the electric equipment and the power consumption is completed, the power consumption data of the electric equipment is counted in real time, and the electric equipment is divided after the statistics is completed; and 3, intelligent optimization regulation and control processing is carried out, a real-time regulation and control prediction scheme is formulated for real-time prediction, and an energy-saving control scheme is formulated for regulation and control. In the intelligent management control process of the whole house, the real-time statistical analysis result is more accurate by classifying the electric equipment and counting the electricity consumption in detail, and meanwhile, the collected frequency and error threshold are controlled during real-time counting, so that the implementation efficiency of a regulation and control prediction scheme is higher.

A kind of hydrate method cold accumulation and cooling system based on LNG cold energy, including gas supply system, hydrate method cold accumulation system, cooling system and refrigerating unit auxiliary system.The huge cold energy released in LNG gasification is used in gas supply system by deionized water and resident building return water coolant water in turn.The absorbed LNG cold energy is accumulated in hydrate method cold accumulation system, and the cold accumulation system can separate hydrate and deionized water, and can realize variable rate cold accumulation.The coolant water in cooling system exchanges heat with LNG and hydrate slurry in turn, and is sent to cold storage and resident building user by circulating pump pressurization, when the cooled coolant water cannot meet the requirement of cold energy demand end or cooling system cannot stably cool, refrigerating unit auxiliary system is opened, the opening of distribution valve is adjusted according to the temperature of cold energy demand end, the coolant water through distribution valve and the coolant water without distribution valve are mixed in mixer, to meet the cold energy demand of demand end.The system has the advantages of energy saving, sustainable and stable cooling, and has the value of commercial application.