71 results about "Power usage effectiveness" patented technology

The invention relates to the technical field of intelligent power grids, in particular to a virtual power plant multi-level collaborative optimization control method based on digital twinning. According to the invention, data of the power generation end, the power storage end, the power transmission end and the load end are collected and processed in real time, a digital twin model is established, and accurate modeling and real-time monitoring can be carried out on each device of the virtual power plant. Through real-time optimization scheduling and cooperative control, energy loss can be minimized, supply and demand can be balanced, the energy use efficiency is improved to the maximum extent, and the cost is reduced; according to the method, the genetic algorithm and the particle swarm optimization algorithm are combined, scheduling optimization is carried out based on a multi-level collaborative optimization model, a virtual power plant can rapidly respond according to changes of real-time environment and load requirements, fossil energy reduction and clean energy increase are preferentially considered in the power generation equipment scheduling process, and greenhouse gas emission reduction is facilitated.

The invention relates to the technical field of building optimization analysis, in particular to a BIM-based green energy-saving building optimization design method and system and a storage medium, and the method comprises the following steps: collecting user behavior data in a building through an intelligent device, including use data of each region in the building, hot water use data, electric appliance use frequency and time, and temperature setting preference; based on the user behavior data, establishing a user behavior prediction model by using a machine learning algorithm, and predicting the activity intensity of the user in each area in the building; dynamically adjusting an energy distribution scheme of the building according to a prediction result of the user behavior prediction model; and inputting the activity intensity yi, t of the user behavior prediction model and the real-time environment data into the BIM model, and updating the BIM model of the building. According to the invention, the comfort level of the user is improved, the energy use efficiency is also remarkably improved, and the waste of resources is avoided.

The invention relates to the technical field of machine room energy consumption management, and discloses a machine room energy-saving control method and system based on cloud computing, and the method comprises the steps: collecting equipment operation data in real time through a multi-dimensional sensor network, building a power consumption prediction model and an energy distribution optimization model, and extracting key features through principal component analysis; and constructing a multiple regression model to determine an energy efficiency coefficient, and predicting a future power demand by adopting a long-short-term memory neural network. And an energy distribution scheme is optimized based on a genetic algorithm, and accurate power control and dynamic balance are realized. According to the invention, a self-adaptive energy consumption management decision mechanism is also established, the energy consumption trend is analyzed through a sliding window, an emergency adjustment program is started, state estimation is corrected by using a Kalman filtering algorithm, and parameters of a prediction model are dynamically adjusted. According to the method, intelligent management of energy utilization of the data center can be realized, the total energy consumption cost is effectively reduced, the energy utilization efficiency is improved, and technical support is provided for energy conservation and emission reduction of a large machine room.

The invention relates to the technical field of energy storage converters, in particular to a distributed energy storage converter optimization method and system based on big data analysis. According to the method, the load fluctuation can be predicted more accurately by combining meteorological and load historical data, the control parameters of the energy storage converter can be dynamically adjusted according to the obtained load fluctuation influence rate to adapt to the load change, the control parameters can be dynamically adjusted under different operation conditions according to the real-time energy conversion efficiency data, and the load fluctuation can be predicted more accurately. By comprehensively considering the energy conversion efficiency, the error of load prediction can be reduced, the response capability of the system is improved, and by monitoring the load value and the demand response gain in real time and adjusting the charging and discharging strategy of the distributed energy storage converter, the dynamic response capability and stability of the system are improved, and the energy conversion efficiency is improved. And the energy use efficiency can be improved, the use of stored energy can be optimized by dynamically adjusting the charging amount, and the overall economical efficiency and stability of the system can be improved.

The invention discloses a building energy-saving optimization system, method and equipment based on photovoltaic power generation, and relates to the field of energy-saving system management.The building energy-saving optimization system comprises a data acquisition module, a data uploading module, a data processing module and a data response module. The data flow of each edge node is asynchronously uploaded to the data processing module at the maximum uploading rate, the photovoltaic equipment data is disassembled and segmented, the system splits a data processing task into a plurality of sub-tasks, and the sub-tasks are processed by adopting a multi-channel task scheduling architecture. The load state of the channel is monitored in real time, task allocation is adjusted in time, the energy use efficiency is effectively improved, building energy consumption is reduced, and meanwhile the stability of the system is guaranteed.

The invention discloses an enterprise energy efficiency evaluation and promotion model construction method based on electric power big data, and belongs to the field of data analysis. Comprising the steps of data collection; preprocessing the data; extracting characteristic indexes; constructing a model; evaluating and optimizing the model; and model interpretation application. Based on multi-dimensional electric power big data, an energy efficiency evaluation and improvement model is constructed, energy efficiency analysis and evaluation of key energy consumption enterprises are realized, the value of the electric power big data is fully mined, the enterprises are helped to understand own energy use efficiency, the energy efficiency improvement potential of the enterprises is mined, the enterprises are supported to formulate energy use strategies and energy saving measures, and the energy cost is reduced. And energy conservation, emission reduction and dual-carbon targets can be achieved. The method can be widely applied to the fields of power supply and consumption operation management and key energy consumption enterprise energy efficiency analysis and evaluation.

The invention discloses a power distribution area load adaptive regulation and control method, device, equipment and system, and belongs to the technical field of electric power, and the method comprises the steps: obtaining the electric power operation data of a power distribution area micro-grid system in a preset time period, and the power distribution area micro-grid system comprises an energy storage system and a distributed photovoltaic power generation system; preprocessing the power operation data to obtain an operation data set; according to the operation data set, determining a load regulation and control strategy by using an adaptive dynamic programming algorithm; and sending a control instruction to the energy storage system and the distributed photovoltaic power generation system, wherein the control instruction carries the load regulation and control strategy. According to the invention, the self-adaptive regulation and control of the load of the power distribution area are realized, and the energy utilization efficiency and the stability of the low-voltage power distribution network in the operation process can be improved.

The invention discloses an intelligent energy-saving control method based on deep learning, and the method comprises the steps: obtaining the load data and environment data of a machine room, building a PUE prediction model, training the model through the obtained load data and environment data, deploying the trained PUE prediction model to a target device, and carrying out the prediction of the machine room. And obtaining an optimal parameter combination through the PUE prediction model in combination with the genetic algorithm model, and implementing the optimal parameter combination. Meanwhile, according to the energy-saving control method, a service guarantee model can be added, and the performability of the genetic algorithm model for generating the parameter combination in the process of iterating the optimal parameter combination is guaranteed through the service guarantee model. Through verification, the intelligent energy-saving control technology based on deep learning provided by the invention can greatly improve the energy use efficiency of the data center machine room.

The invention relates to the technical field of energy management, in particular to a smart park comprehensive energy optimization management and control system and method, and the method comprises the following steps: collecting the energy data of electric power, heating power and natural gas in real time, and employing the algorithm based on a long-short-term memory network, support vector regression and random forest regression; modeling is carried out on demand characteristics of electric power, thermal power and natural gas, and future demands are predicted; the method comprises the steps of establishing an optimization objective function, optimizing park energy configuration by adopting a particle swarm optimization algorithm based on the established optimization objective function, determining an optimal energy scheduling scheme, and finally applying the optimized energy scheduling scheme to a park energy management system to realize dynamic scheduling and real-time control of energy. According to the invention, the energy waste of the park can be effectively reduced, the energy use efficiency is improved, and intelligent and refined management of energy is realized.

The invention is suitable for the technical field of energy consumption control, and provides an energy consumption prediction and energy-saving control method adopting a deep neural network. According to the method, virtual training data is generated by constructing a machine room digital twinborn model including a three-dimensional geometric model and a CFD simulation model, and the electric energy use efficiency and the overheating condition are predicted in combination with a deep neural network optimized by an attention mechanism. And the operation strategy system generates an optimal control suggestion by predicting the energy-saving effect and the safety risk after the air conditioner is turned off or the temperature is increased. The visual interface integrates real-time parameters, historical data and three-dimensional airflow organization dynamic display, and assists an administrator in decision making. The method breaks through the limitation of real data, improves the prediction precision, reduces the PUE on the premise of ensuring that the operation number of air conditioners is greater than or equal to 3, and achieves the efficient and energy-saving management of a machine room. Experiments show that the PUE can be reduced by about 4.3%, and the cabinet overheating probability can be reduced by 60%.

The invention relates to the technical field of logistics management, in particular to an intelligent supervision control system for operation and maintenance of a cold chain system, which comprises the steps of collecting path environment data to screen an operation and maintenance optimal section, then identifying an energy consumption over-limit risk in the section, and executing main-standby switching on equipment with temperature rise exceeding the standard, meanwhile, power supply sufficiency is judged according to load requirements so as to start a redundant power supply, finally, a priority scheduling object is determined according to resource and task urgency by integrating a power supply state and energy consumption data, and a cold chain operation and maintenance centralized supervision control set is generated. According to the method, path selection is optimized by monitoring path environment fluctuation in real time to filter unstable road sections, energy consumption data and load standards are compared to identify and early warn overload risks, equipment switching and power supply scheduling are automatically executed to avoid operation and maintenance interruption when equipment temperature rise is too high or power supply is insufficient, and finally each link is intelligently scheduled through linkage management and control. The system operates stably and efficiently, the energy use efficiency is improved, and the sudden failure influence is reduced.

The invention relates to a method and device for dynamically adjusting machine room environment energy consumption, and belongs to the technical field of energy consumption adjusting.The method comprises the steps that machine room environment energy consumption data are collected, and a current monitoring energy consumption initial threshold value is set; performing early warning according to the energy consumption data and generating a visual report; if the current actual energy consumption in the visual report is higher than the historical average baseline and the predicted rising and falling probability trend values are consistent, calculating to obtain an energy use efficiency value, an energy use efficiency predicted value and a difference value, and optimizing the current monitoring energy consumption initial threshold value according to the difference value; when the machine room environment energy consumption data is abnormal, a current sub-module is positioned, association relation mining is carried out to generate a data set, and the data set is marked; calculating the similarity of the data sets, and setting a next monitoring energy consumption initial threshold value; determining an optimal threshold value; summarizing and storing the machine room environment energy consumption data generated under the optimal configuration threshold value, and comparing the machine room environment energy consumption data generated under the optimal threshold value with the machine room environment energy consumption data generated under the monitoring energy consumption initial threshold value.

The invention relates to a multi-demand response resource day-ahead market optimal bidding method and device, and the method comprises the steps: defining the structure and operation mechanism of a day-ahead electricity market, considering various demand response resources, and building demand response bidding models corresponding to the various demand response resources; on the basis of the demand response bidding model and contracts of various demand response resources of the signed customer, constructing a load aggregator decision optimization model by taking the maximum total income of a demand response load aggregator as a target; and solving the load aggregator decision optimization model to obtain an optimal bidding strategy. According to the method, the energy use efficiency can be improved, and meanwhile, the demand response utilization efficiency of large-scale data processing facilities with special demand characteristics is improved.

The invention relates to the technical field of special valve manufacturing, and discloses a special valve material heat treatment energy-saving process which comprises the following steps: S1, simulating and predicting the structure change of a material in the heat treatment process through a computer; s4, carrying out heat preservation on the material heated to the target temperature; S5, selecting a cooling mode according to the characteristics of the material; S6, improving the energy utilization rate through waste heat recovery equipment; and S7, carrying out heat treatment on the material by an intelligent control system through monitoring the running state of the equipment in real time in the heat treatment process. S8, detecting the quality of the material; and S9, feeding back according to data in the production process. Through the synergistic effect of electromagnetic induction heating, waste heat recovery and an intelligent control system, energy consumption can be greatly reduced, dependence on external energy is reduced, and the energy use efficiency of the whole heat treatment process is optimized.

According to the intelligent stadium energy efficiency management system provided by the invention, activity data in the stadium are accurately captured through the real-time monitoring module, air conditioning and illumination are automatically adjusted by using the environment self-adaptive adjustment module, and optimization of energy consumption is realized. The system adopts an advanced sensor network, a data processing algorithm and a machine learning model, including ARIMA and LSTM, to predict an energy consumption trend and formulate an intelligent scheduling strategy. In addition, the system comprises functions of dynamic data acquisition frequency adjustment and energy consumption monitoring and recording, and a user-friendly interface control and intelligent energy-saving unit. Green energy integration and carbon footprint estimation enhance the environmental protection characteristics of the system. The system not only improves the energy use efficiency, but also supports the goals of sustainability and environmental protection.

Aspects and embodiments disclosed herein include a method of performing an adaptive calculation of power usage efficiency (PUE) of a data center. The method comprises receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

The invention belongs to the technical field of electric power, and discloses a micro-grid quasi-periodic energy management method based on time sequence prediction and linear programming, and a micro-grid system comprises an energy storage system, renewable energy sources, a load and an external power grid. The invention provides an economic energy exchange method between a micro-grid and a power grid. Based on the quasi-periodic hypothesis, the load power, the internal source power and the electricity price are accurately predicted, the energy distribution is optimized by using the linear programming method, the model training calculation complexity is low, and the possibility is provided for the use of edge intelligent equipment. The energy utilization efficiency is effectively improved, dependence on traditional energy is reduced, and sustainability and economic benefits are achieved.

The invention discloses a network transmission dynamic power distribution method and system for a hydrogen-electricity hybrid unmanned aerial vehicle, and belongs to the technical field of hydrogen-electricity hybrid unmanned aerial vehicles, and the method comprises the steps: carrying out the collection and preprocessing of the state monitoring real-time data of the hydrogen-electricity hybrid unmanned aerial vehicle; analyzing the state monitoring characteristic data of the hydrogen-electricity hybrid unmanned aerial vehicle, and determining a network transmission dynamic power distribution scheme of the hydrogen-electricity hybrid unmanned aerial vehicle; and adjusting and distributing the network transmission dynamic power of the hydrogen-electricity hybrid unmanned aerial vehicle to optimize energy utilization and communication performance. The problems that the network transmission dynamic power of the hydrogen-electricity hybrid unmanned aerial vehicle cannot be effectively distributed in the prior art, the energy use efficiency is low, and the stability and safety of data transmission cannot be ensured are solved. According to the invention, the network transmission dynamic power of the hydrogen-electricity hybrid unmanned aerial vehicle can be effectively distributed, the energy use efficiency of the hydrogen-electricity hybrid unmanned aerial vehicle is improved, the stability and safety of data transmission can be ensured, and the communication quality is improved.

This invention belongs to the field of power system control technology. It discloses a system and method for the linkage between sub-core load adaptation and main core power replenishment in a dual-core fused power supply. The system includes: calculating the main core's instantaneous power replenishment margin based on the current state of charge of the main core management unit and the real-time available output capacity of the main core converter; sending a low-voltage detection pulse sequence to the sub-cores and simultaneously acquiring the returned damping attenuation waveform and instantaneous leakage current value; determining a dynamic access threshold, comparing the current real-time load power with the dynamic access threshold, and generating a continuous load adaptation tag and a sub-core locking identifier; establishing a sub-core priority power replenishment sequence, and immediately tightening the access conditions of the sub-core priority power replenishment sequence when the instantaneous power replenishment margin is lower than a preset warning value; activating a fast power replenishment channel for sub-cores that meet the preset power replenishment triggering conditions, while simultaneously performing dynamic power backtracking on unlocked sub-cores until the main core's instantaneous power replenishment margin recovers to or above the safety threshold; significantly improving energy efficiency.

The present application relates to a kind of method and device for dynamically adjusting computer room environment energy consumption, belong to adjusting energy consumption technical field, the method includes: collecting computer room environment energy consumption data, set this monitoring energy consumption initial threshold;According to energy consumption data, early warning and generating visual report;If the actual energy consumption in visual report this time is higher than historical average baseline and prediction rise and fall probability trend value is consistent, energy use efficiency value, energy use efficiency prediction value and difference are obtained, according to the size of difference, optimize this monitoring energy consumption initial threshold;When computer room environment energy consumption data is abnormal, locate current submodule, generate data set and mark by correlation relationship mining;The similarity of data set is calculated, set next monitoring energy consumption initial threshold;Determine optimal threshold;Optimal configuration threshold is generated under the computer room environment energy consumption data of optimal configuration threshold is stored, compare the computer room environment energy consumption data generated under optimal threshold and the computer room environment energy consumption data generated under monitoring energy consumption initial threshold.

This invention discloses a server configured with a dual-carbon management mode, comprising: a governance module, a management module, a carbon emission accounting module, a security module, an energy-saving module, and a data analysis module. The governance module is used for dynamically analyzing and evaluating Power Usage Effectiveness (PUE). The management module is used for collecting energy and material flows, performing intelligent analysis, refined management, and system optimization. The carbon emission accounting module is used for performing carbon emission accounting and generating carbon asset management solutions based on the accounting results. The security module is used to improve the underlying IT / OT application infrastructure. The energy-saving module is used to acquire power consumption data of server nodes and generate corresponding energy-saving strategies based on the power consumption data. The data analysis module is used to provide intelligent decision support for the planning, deployment, and operation and maintenance of data center clusters. This addresses carbon emission compliance requirements, ensures improved lifecycles for data center equipment, and meets data center requirements such as fundamentality, continuity, reliability, stability, security, and energy efficiency.

The invention relates to a smart energy digital management system applied to a cloud platform, in particular to the field of energy digital management, and specifically comprises a data acquisition module, a data storage and preprocessing module, a data analysis and optimization module and an energy management decision and scheduling module. According to the system, technologies of Internet of Things, big data analysis, artificial intelligence and the like are combined, comprehensive digital monitoring and intelligent optimization management of enterprise energy are realized through real-time acquisition and processing of various energy data, and the energy management efficiency is improved through integration of advanced data mining, trend prediction and optimization scheduling algorithms. The method helps enterprises to reduce energy consumption, improves energy use efficiency, and optimizes energy management strategies.

The embodiment of the invention provides a temperature control strategy determination method and system, computing equipment and a storage medium, and the temperature control strategy determination method comprises the steps: determining service equipment parameters of service equipment deployed in a target machine room, and temperature control equipment parameters of temperature control equipment; under the condition of determining that the target machine room meets a preset test condition according to the service equipment parameters and the temperature control equipment parameters, performing energy consumption test on the service equipment and the temperature control equipment according to a preset temperature interval to obtain an energy consumption test result corresponding to each temperature test node in the preset temperature interval; and determining a temperature control strategy corresponding to the target machine room according to the energy consumption test result corresponding to each temperature test node. The temperature of the target machine room is subsequently controlled according to the temperature control strategy, the energy consumption of the target machine room is further reduced, and the energy use efficiency of the target machine room is improved.

The invention discloses an intelligent power regulation method and system for an electric vehicle charging operation platform, and the method comprises the steps: carrying out the orderly charging inspection, and updating the state of a charging pile based on the inspection content; real-time power data of the charging pile are counted, the power of the charging pile is limited based on the real-time power data, when control is needed, limitation is specifically carried out based on a charging pile dynamic control algorithm, and the algorithm content at least comprises charging gun number calculation, charging gun priority calculation and single charging gun dynamic power limit value calculation; and when control is not needed, the current limited power is recovered to a normal state, and the state of the charging pile is synchronously updated. By dynamically adjusting the power output of the charging pile, the charging speed can be properly reduced in the peak period of power utilization, so that the load of a power grid is reduced, the energy use efficiency is improved, and the stability of the power grid and the charging activity continuity of the charging pile are guaranteed.

The invention relates to the technical field of power management, in particular to a data center rack power management method and system, and the method comprises the following steps: collecting the CPU utilization rate, the memory utilization rate and the rack temperature of multiple racks of a data center, measuring the power output of each device through a rack power unit, and carrying out the integration to obtain energy consumption basic data. According to the method, the CPU utilization rate, the memory utilization rate, the rack temperature and other data of the multiple racks of the data center are integrated, the power output of each device is accurately measured, the comprehensive energy consumption basic data view is provided, and through data integration and weight analysis, energy distribution is more accurate, dynamic adjustment can be conducted according to the actual running state of the device, and the energy consumption is reduced. The energy utilization efficiency is improved, the neural network model is applied to predict energy demands and optimize power output and cooling system setting, accurate matching and efficiency maximization of power supply are ensured, prediction deviation is effectively corrected through real-time monitoring and application of the sliding window method, and excessive waste of resources is reduced.

The invention discloses a charging pile ultra-low energy consumption control method, system and device based on AI regulation and control and a medium, belongs to the technical field of charging piles, and aims to solve the technical problem of how to adjust charging power and improve the energy use efficiency of the charging pile. According to the technical scheme, the method comprises the steps that a power grid load historical data table, meteorological data and dates are obtained through a charging operation platform, current power configuration and temperature data are collected through a sensor of a charging pile, then data related to the charging pile are collected, the collected data related to the charging pile are circularly processed, and the data are stored in a storage battery. And obtaining the overall network parameters, predicting the charging power load through the overall network parameters, and then adjusting the charging power of the charging pile in real time.

The invention relates to the technical field of camera equipment control, in particular to a camera power consumption control method and system.According to the camera power consumption control method and system, importance evaluation is conducted on the power consumption influence of an image dynamic area through a graph neural network, so that power consumption of all modules in the image collection process is accurately adjusted, and accurate calculation of the scene change rate is achieved; the power consumption of the camera in different scenes is optimized, the change trend and the power consumption demand of future image acquisition are predicted through the long-short-term memory network, more accurate power consumption control and adaptive adjustment capability are provided for the camera equipment, the effects of prolonging the endurance of the equipment, improving the response efficiency and reducing redundant data processing are achieved, and the method is suitable for large-scale popularization and application. Through combination of future power consumption mode prediction and actual requirements of equipment, the camera can be intelligently switched between a high-performance mode and a low-power consumption mode, the energy utilization efficiency of the equipment is optimized, and higher flexibility and energy utilization efficiency are achieved.

The embodiment of the invention discloses a maximum demand control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the real-time gas reserves and real-time demand power of a target power grid system; under the condition that the real-time gas reserves and the real-time demand power meet a preset control condition, determining a corresponding generation power adjustment amount; and based on the generated power adjustment amount, adjusting the real-time generated power of the gas generator in the target power grid system so as to realize the control process of the maximum demand of the target power grid system. According to the technical scheme, the problem that in the prior art, the maximum demand of a power grid system cannot be effectively controlled is solved, the generated power can be dynamically adjusted according to the real-time gas reserves and the real-time demand power so as to control the maximum demand to be within a certain range, the energy use efficiency can be improved, the power utilization cost can be reduced, and the electric charge expenditure can be reduced.

The invention belongs to the technical field of energy management, and particularly relates to a park energy management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining energy use information, inputting the energy use information into a trained LSTM model, obtaining an energy demand prediction result, and obtaining a park energy management result based on the energy demand prediction result. And generating equipment control information, wherein the equipment control information is used for controlling the work of the energy equipment. The method has the effects of improving the energy use efficiency and optimizing the energy management of the park.

The invention provides a smart park power management system based on the Internet, and relates to the technical field of power management, and the system comprises a data collection module which is used for collecting the operation data of power related equipment in a park; the data management module is used for processing and storing the acquired operation data; the data analysis module is used for analyzing the operation data through a machine learning algorithm; the intelligent power dispatching module is used for intelligently adjusting power distribution according to the analysis result; according to the invention, through Internet connection, real-time monitoring of power equipment, environmental factors and power consumption of the park is realized, so that through cooperation of real-time monitoring and intelligent scheduling, unnecessary energy waste is avoided, the energy consumption of the park is reduced, the energy use efficiency is improved, the problem of blind scheduling in a traditional method is avoided, and the energy utilization rate of the park is improved. And the power distribution is more accurate and flexible.