Product
Patsnap Eureka
For R&D, Patsnap Eureka makes reading and utilizing patents & technical documents easy.
Patsnap Eureka AIR
Designed for self-driven R&D workflows. Generate viable solutions, solve complex R&D challenges, empower your innovation with AI.
Patsnap Eureka Materials
Designed for material experts only. Revolutionize your material R&D, from search, analyze, to developing new materials.
Feature
TechResearch
Generate reliable direction feasibility study reports for your R&D in just a few steps.
TechSeek
Discover and master advanced knowledge NOW. Basics, ideas, possibilities, all at once.
TechMind
As an expert in R&D Theories, TechMind can generates customized viable solutions instantly.
TechRisk
Analyze your overall solution with one click, know your potential R&D risks in advance.
TechMonitor
Get weekly tech updates, stay abreast of the latest tech innovations and key insights.
Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
2418 results about "Load forecasting" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
Load forecasting is a technique used by power or energy-providing companies to predict the power/energy needed to meet the demand and supply equilibrium. The accuracy of forecasting is of great significance for the operational and managerial loading of a utility company.
Energy and cost savings calculation system
InactiveUS20060167591A1Accurate and reliable energyAccurate and reliable and cost savingMechanical power/torque controlData processing applicationsEngineeringCost savings
An Energy and Cost Savings Calculation System is provided that automates the determination of energy and cost savings due to energy conservation measures. The system provides Multi-Variant, Non-Linear (MVNL) load forecasting techniques, energy and cost savings calculations, and Weather Ranking. The load forecasting technique may accept numerous external parameters as input. The technique may use multiple Baselines. It may also use multiple Basic Reference Periods to reduce the load forecasting error. The load forecasting technique may utilize external parameters that are updated on a daily basis, such as dry bulb temperature, dew point temperature, solar condition, and interval meter data. The technique may use Baseline Extensions to perform forecasts and Reference Period Modifications to enhance accuracy. The system may calculate energy and cost savings using Complex Rates and time-of-use (TOU) energy data. The system may rank a plurality of sources providing weather data to identify the most accurate weather data.
Owner:SIEMENS IND INC
Intelligent control system for microgrid energy
InactiveCN101630840ARich control functionsHigh control precisionTotal factory controlProgramme total factory controlMicrogridEnergy control
The invention relates to a miniature power supplying system containing a plurality of distributed power supplies, and discloses an intelligent energy control system capable of controlling a microgrid by using forecast information. The intelligent control system for microgrid energy comprises a microgrid system and a control system, wherein the microgrid system comprises a power supply unit, an energy storage unit and a load unit, and the control system comprises an information collecting system and a central processing unit. The central processing unit comprises a load forecasting module, a microgrid online status estimating module, an energy forecasting module of an intermittent power supply energy forecasting and storing unit, a microgrid system analyzing module and a microgrid multiobjective optimization operation and comprehensive coordination control module. The intelligent control system for microgrid energy can control the microgrid according to the forecast information and accomplish the intelligent operation of the microgrid.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Energy and cost savings calculation system
ActiveUS20070244604A1Save energyAccurate and reliable and costLevel controlTemperatue controlEngineeringTime of use
An Energy and Cost Savings Calculation System is provided that automates the determination of energy and cost savings due to energy conservation measures. The system provides Multi-Variant, Non-Linear (MVNL) load forecasting techniques, energy and cost savings calculations, and Weather Ranking. The load forecasting technique may accept numerous external parameters as input. The technique may use multiple Baselines. It may also use multiple Basic Reference Periods to reduce the load forecasting error. The load forecasting technique may utilize external parameters that are updated on a daily basis, such as dry bulb temperature, dew point temperature, solar condition, and interval meter data. The technique may use Baseline Extensions to perform forecasts and Reference Period Modifications to enhance accuracy. The system may calculate energy and cost savings using Complex Rates and time-of-use (TOU) energy data. The system may rank a plurality of sources providing weather data to identify the most accurate weather data.
Owner:SIEMENS IND INC
Intelligent heating network dispatching system
ActiveCN104791903ARun smartAddressing Extensive BehaviorLighting and heating apparatusSpace heating and ventilation detailsLoad forecastingEngineering
Owner:北京上庄燃气热电有限公司 +1
Short-term electric power load prediction method considering meteorological factors
InactiveCN106779129AReduce workloadImprove forecast accuracyClimate change adaptationForecastingLoad forecastingPower grid
The invention discloses a short-term electric power load prediction method considering meteorological factors, and belongs to the technical field of electric power load prediction. The method includes: collecting historical load data and meteorological data, and detecting and correcting abnormal data; analyzing the relevance between the load data and the meteorological factors, and determining key meteorological factors; establishing comprehensive meteorological factors according to the relevance between the load and the key meteorological factors; summarizing change characteristics of a daily load curve of a regional power grid, and finding out typical similar days of a prediction day; establishing an Elman neural network short-term load prediction model by employing the selected load and the comprehensive meteorological factors, and training network parameters by employing a firefly algorithm; inputting the comprehensive meteorological factors of a to-be-predicted moment and the corresponding load data to the Elman neural network short-term load prediction model, and outputting a load prediction value of the to-be-predicted moment; and displaying the load prediction value. According to the method, the load data of weekdays, weekends, and official holidays can be accurately predicted, the prediction precision is high, the applicability is high, and reliable basis is provided for making of generation plans for operation personnel of the power grid.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING) +1
Power load forecasting method based on long short term memory neuron network
InactiveCN106960252AImprove predictive performancePrediction is accurateForecastingNeuron networkLoad forecasting
The invention discloses a power load forecasting method based on a long short term memory (LSTM) neuron network. The power load forecasting method comprises inputting power load data and a region feature factor at a historic moment through an input unit; carrying out training and modeling on the power load data and the region feature factor at the historic moment by means of an LSTM network in order to generate a deep neural network load forecasting model which is a single-layer multi-task deep neural network model or a double-layer multi-task deep neural network model used for power supply load forecasting; forecasting the power load in an area needing to be forecasted by means of the deep neural network load forecasting model, and generating a forecasting result of the power load in the area; and outputting the forecasting result of the power load in the area through an output unit. According to the invention, a multi-task learning power load forecasting model is constructed based on the LSTM network in the deep learning field, power consumption loads in multiple areas can be forecasted accurately, and the forecasting effect is improved.
Owner:X TRIP INFORMATION TECH CO LTD
System tools for integrating individual load forecasts into a composite load forecast to present a comprehensive synchronized and harmonized load forecast
ActiveUS20110035071A1Mechanical power/torque controlLevel controlLoad forecastingRelational database
A system tool merges different load forecasts for power grid centers. A plurality of load forecast engines are coupled to a load forecast interface and a relational data base that saves load forecast engine data as an input through the load forecast interface. A comprehensive operating plan is coupled to the load forecast engines and the relational database. The comprehensive operating plan is configured to integrate individual load forecasts into a composite load forecast to present a comprehensive, synchronized and harmonized load forecast. A program interface provides access to the composite load forecasting schedule.
Owner:ALSTOM TECH LTD
System and method for managing energy generation equipment
InactiveUS7127327B1Efficient managementMaximize leverageMechanical power/torque controlLevel controlLoad forecastingEngineering
A system and method for controlling distributed generation equipment based on remotely derived dispatch schemes improves economics and reliability of operation. The system can adapt to variable changing conditions in real-time to provide adaptable, real-time, site-specific load forecasting.
Owner:DTE ENERGY TECH
Dispatching method and dispatching system based on load online forecasting of thermoelectric power system
InactiveCN101619850AImprove production and operation efficiencyState switching is smoothEnergy industryBoiler controlLoad forecastingData acquisition
The invention discloses a dispatching method and a dispatching system based on load online forecasting of a thermoelectric power system. The dispatching method has the main objects of a boiler and a vapor generating set which are the core equipment of a thermoelectric power generation system. The dispatching process comprises the following steps: a. acquiring data; b. creating a real-time database and a historical database; c. analyzing data and making a dispatching decision, creating a decision dispatching knowledge base to obtain a corresponding operation decision in the current optimal state to be reached and in the recent optimal dispatching state, comparing the expectation effect of the dispatching decision with an actual effect, taking the result as the condition of load forecasting, and finally obtaining the optimal dispatching decision through human-computer interaction. The dispatching system comprises a field data acquiring terminal, a field production layer DCS, a management layer ERP, a center data server and a manufacture execution and management layer MES. The invention overcomes the defects existing in the prior art; and based on the production capacity and the distribution forecast of a thermoelectric plant, the dispatching method and the dispatching system facilitate improving the production operation efficiency of enterprises, lowering the source consumption and reducing the pollution discharge.
Owner:HANGZHOU PANGU AUTOMATION SYST
Method for establishing virtual reality excavation dynamic smart load prediction models
InactiveCN104008430AHigh precisionBreakthrough improvementBiological neural network modelsForecastingSmart gridSmart power
The invention discloses a method for establishing virtual reality excavation dynamic smart load prediction models. The method includes the steps that the knowledge excavation technology is adopted so that a virtual reality analysis environment can be formed, the influence relation between fixed quantities is explored, and an input variable candidate set is determined; smart load prediction models of a support vector machine of a self-adaptive structure and an Elman neural network and the like are established, wherein input variables are determined by the support vector machine through the attribute screening technology and parameters are optimized by the support vector machine through a flora tendency differential evolutionary algorithm; a region load smart load prediction model based on data slice excavation is established; a load curve prediction model combined with dynamic electrovalence factors, user characteristics and the user response electric quantity is established, so that linked correcting prediction of loads, electrovalence and the response electric quantity is achieved. According to the method, the prediction models suitable for the actual condition of a smart power grid of China are established, the scale of construction of renewable energy sources is reasonably planned, more efficient power utilization of users is facilitated, and reasonable arrangement of power supply resources of power enterprises is facilitated.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)
Method, system and storage medium for load dispatch optimization for residential microgrid
ActiveUS20200161867A1Improve forecast accuracyHigh energyGeneration forecast in ac networkLoad forecast in ac networkLoad forecastingControl engineering
The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.
Owner:HEFEI UNIV OF TECH
Short-term operation optimization method of electric power system including large-scale wind power
InactiveUS20160169202A1Adjust correlationWind motor controlEngine fuctionsElectricityLoad forecasting
The present invention discloses a short-term operation optimization method for a power system including large-scale wind power, comprising modeling the randomness of wind power output, modeling the randomness of the load of electric power system and modeling net load of electric power system. Net load refers that for probability distribution of net load that is too discretized, probability distribution curve of net load is divided into N intervals, the probabilities for each interval are obtained and probability distribution curve of net load is obtained through calculating and weighing each interval. Through calculating randomness of power wind output and standard deviation of load prediction error of the electric power system, net load prediction error of the electric power system is obtained and reasonable coordination is made on the electric power system according to prediction error and prediction amount to better regulate the correlations between randomness, volatility, regionalism, double-circuit peak shaving and load of wind power generation, so as to realize optimization operation of the electric power system.
Owner:STATE GRID CORP OF CHINA +2
Optimized operation control method and system of distributed energy system
ActiveCN104571068AAchieve optimal allocationIncrease speedTotal factory controlProgramme total factory controlElectricityLoad forecasting
The invention discloses an optimized operation control method and system of a distributed energy system. The method include: S1, collecting environmental information and actual operation data of a unit so as to acquire a change rule of cold and hot load of a distributed energy station user with season and moment, and establishing a cold, hot and electric load prediction model; S2, optimizing the cold, hot and electric load prediction model on line by introducing real-time calibration factors and the actual operation data of the unit; S3, on the premise that the energy utilization efficiency is met, establishing a dynamic optimized load distribution model according to the dynamic requirements of the predicated cold, hot and electric load by taking a whole-plant economic benefit optimization as an objective, and outputting dynamic optimized load distribution results; S4, based on the whole-plant economic benefit optimization, establishing an optimal combination model according to the dynamic optimized load distribution results, and outputting a unit operation optimization command. High-precision load prediction information can be acquired, a corresponding optimization command is formed, and online optimization control is performed on the load dynamics and unit operation.
Owner:CHINA HUADIAN SCI & TECH INST
Load forecasting from individual customer to system level based on price
InactiveUS20150046221A1Optimal dispatchImprove abilitiesMarket predictionsLoad forecast in ac networkPersonalizationLoad forecasting
The present invention relates to system and method for providing near real-time DR events and price signals to the customer end-points to optimally manage the available DR resources. The system utilizes bottom up load forecasting for accurate individualized forecasts for customer loads in the presence of dynamic pricing signals. For better efficiency and reliability of grid operation the system utilizes advanced machine learning and robust optimization techniques for real-time and “personalized” DR-offer dispatch.
Owner:AUTOGRID SYST INC
Short period load prediction method based on kernel principle component analysis and random forest
InactiveCN105303262AImprove rationalityImprove forecast accuracyForecastingKernel principal component analysisLoad forecasting
The invention discloses a short period load prediction method based on kernel principle component analysis and a random forest. The a short period load prediction method comprises the following steps of: (1) analyzing and selecting data influencing load prediction precision of a day to be predicted in an operational electric power system, and preliminarily constructing training and prediction sample sets; (2) utilizing kernel principle component analysis to carry out dimensionality reduction on training sample data; (3) utilizing a random forest model to train the training sample data after the dimensionality reduction, and obtaining the random forest model after the training; and (4) inputting prediction sample data into the random forest model after the training, and carrying out short period load prediction of the day to be predicted. The short period load prediction method has the advantages that the kernel principle component analysis and the random forest model are combined for carrying out short period load prediction on the electric power system, the prediction precision, efficiency and data rationality are improved.
Owner:HOHAI UNIV
Wind-power adsorption connected large-power-grid scheduling rolling planning method
ActiveCN102170170AImprove output access abilityRealize economic outputSingle network parallel feeding arrangementsWind energy generationElectricityLoad forecasting
The invention relates to a wind-power adsorption connected large-power-grid scheduling rolling planning method, comprising the following steps: obtaining ahead planned output data of all conventional units from an ahead power-generation planning system; building an intraday rolling model of a wind-power adsorption connected power grid based on the ahead plan; obtaining a current system load predicted value and a total wind-power output predicted value through intraday rolling expansion short-term prediction and refreshing the output data of residual time intervals of all the conventional units in an online manner according to the system loading predicted value and the total wind-power output predicted value; and updating processing data of a single machine in optimization problems in a unit output plan. The method can well solve the effect of wind-power connection on the operation of the power grid, and the safety and economic property for power grid operation are improved while cleanenergy sources are fully utilized.
Owner:TSINGHUA UNIV
Short-term and medium- and long-term electric power load prediction method based on machine learning model
The invention discloses a short-term and medium- and long-term electric power load prediction method based on machine learning model. Firstly, preprocessing is conducted on data, including smootheningabnormal data and filling missing data. Factors of affecting load changes will be analyzed, including historical data, time periodicity, and weather variable characteristics. Domestication will be conducted on all input variables for accelerating learning speed and raising prediction precision. The invention is advantageous in that linear regression is compared, and the performance of the vectorregression and gradient lifting regression in the short-term and medium- and long-term electric power load prediction is supported; with the prolongation of the prediction time, the performance of thegradient lifting regression model is better that that of the other two models; the AdaBoost algorithm which uses the gradient lifting tree as a basic classifier is brought forward, and load prediction is conducted, and the precision of electric power load prediction can be effectively raised.
Owner:FOSHAN SHUNDE SUN YAT SEN UNIV RES INST +2
Power load forecasting method based on customer segmentation in power industry
The invention discloses a power load forecasting method based on customer segmentation in power industry. The power load forecasting method comprises: step 1, extracting customer load data and obtaining load sample data; step 2, pre-processing the load sample data; step 3, performing cluster grouping to customer to obtain a plurality of customer groups; step 4, summarizing the load data of the customer groups; step 5, performing load timing sequence forecasting to a single customer group; step 6, summarizing and calculating load forecasting results of the plurality of customer groups; and step 7, evaluating the load forecasting results obtained in the step 6. The power load forecasting method can accurately forecast power load of the customer groups and provides a basis for development and operation of a power system.
Owner:ELECTRIC POWER RES INST OF GUANGDONG POWER GRID
Dynamic climate compensation method for centralized heating
ActiveCN107120721AStable indoor temperatureEnsure heating qualityLighting and heating apparatusSpace heating and ventilation detailsHysteresisValue set
The invention discloses a dynamic climate compensation method for centralized heating. Firstly, the outdoor temperature is predicted, water supply and return temperatures are adjusted some time ahead according to the predicted value of the outdoor temperature, and the hysteresis of pipe network adjustment in the manner that adjustment is performed while sampling is overcome. The outdoor temperature of the next day is predicted according to local historical meteorological data and weather forecast of the meteorological department, and the value is used as basic data for prediction of a thermal load; and then the thermal load is predicted, that is, the thermal load curve of the next day is calculated according to the outdoor temperature. With the method, the outdoor temperature is reasonably predicted so as to realize advanced dynamic adjustment of a climate compensator; heating medium parameters of a heating system are adjusted by the aid of an adjusting model according to the thermal load value set in advance, a heat source is changed from original wide passive heating into active heating, on the premise that the indoor temperature for the user is stable, the operation adjusting indictor of the heating system within the specified time are given in advance, the heating efficiency is improved, and the heating energy consumption is reduced.
Owner:石家庄华浩能源科技有限公司
Agile elastic telescoping method in cloud environment
ActiveCN103425535AFast supplyShorten recovery timeEnergy efficient ICTResource allocationMoving averageLoad forecasting
The invention relates to the field of elastic computing of cloud computing, and discloses an agile elastic telescoping method in a cloud environment. The agile elastic telescoping method includes the specific steps: forecasting the load of a next time slice according to historical load data of a data center through an ARIMA (autoregressive integrated moving average) model and an ARMA (autoregressive moving average) model by taking the time slice as a cycle; performing saving operation and restoring operation on a virtual machine, saving the memory state of the virtual machine by the saving operation to hang up the virtual machine, and then restoring the memory state of the virtual machine by the restoring operation to restore use of the virtual machine; hanging up one or a plurality of application-ready virtual machines or rapidly placing the virtual machines into service through the forecasted load of the data center obtained by the load forecasting step and by the aid of the rapid supply step of the virtual machines to dynamically adjust resources of application clusters of the data center. The agile elastic telescoping method has the advantages that the sizes of the clusters are adjusted in real time according to current conditions of the application clusters, and energy consumption of the data center is reduced.
Owner:ZHEJIANG UNIV
Control method of optimized running of combined cooling and power distributed energy supply system of micro gas turbine
ActiveCN101667013AIncrease economic benefitsHigh Utilization MetricsClimate change adaptationGas turbine plantsMathematical modelMicro gas turbine
The invention belongs to the technical field of energy management of distributed generation energy supply systems of electric power systems. The control method comprises the following steps: before running a combined system on every workday, extracting history cooling load data and power load data of a terminal user from a historical data base and obtaining the delay variation curve of the coolingload and the power load of the terminal user during the whole workday by lone-term load predicting; according to load predicting results, working out the optimal generated output plan of the combinedsystem by adopting optimization control mathematical model; during the running of the combined system, carrying out optimization control calculation again by utilizing the terminal user real-time cooling and power load need obtained from the distributed control system, and modifying the generating capacity and the refrigerating capacity of the combined system. The invention utilizes a distributedmonitoring system to monitor the actual cooling and power load need of the terminal user and can modify the load forecasting result in real time and adjusting the respective controlled variable of the combined system.
Owner:TIANJIN UNIV +2
Electric vehicle charging station load forecasting method
An electric vehicle charging station load forecasting method is divided into a simplified method and a dynamic simulation method. The simplifying method comprises the following steps of counting a vehicle entering a station flow in a typical set time interval in a day by a historical statistics data so that a section curve description formula of an electric vehicle entering the station flow curve is obtained; solving the number of the vehicles entering in the station and being charged in the interval [t-TC, t]; and calculating active power at any time. The dynamic simulation method comprises the following steps of describing charging time by a normal distribution probability density function; performing counting and curve fitting to historical charging time to get a mean value and a standard deviation; and obtaining the whole number of the vehicles being charged and the charging power of the vehicles at every time in a day so that overall charging power of the charging station is calculated. The method is simple in arithmetic, definite in a data interface, fast in operation speed and capable of supporting dynamic interactive simulation of the electric vehicles in a large scale so that time and space distribution of charging load of the electric vehicles can be forecasted and the foundation method can be offered for studying influence on an electric power system by the charging load.
Owner:ELECTRIC POWER RES INST OF GUANGDONG POWER GRID
Dispatching method for achieving robust operation of electrical power system
ActiveCN103606967AFlexible schedulingDealing with Stochastic VolatilitySingle network parallel feeding arrangementsWind energy generationOriginal dataNew energy
The invention discloses a dispatching method for achieving robust operation of an electrical power system. The dispatching method comprises the steps that S1, original data information is obtained; S2, under a certain confidence coefficient level, an upper limit and a lower limit of a mean value of day-ahead, intra-day and real-time wind power generation forecast errors, an upper limit and a lower limit of day-ahead, intra-day and real-time photovoltaic power generation forecast errors, and an upper limit and a lower limit of day-ahead, intra-day and real-time load forecast errors are obtained; S3, a day-ahead dispatching plan, a robust safe operation range corresponding to the day-ahead dispatching plan, an intra-day dispatching plan, a robust safe operation range corresponding to the intra-day dispatching plan, a real-time dispatching plan and a robust safe operation range corresponding to the real-time dispatching plan are obtained. According to the method, the rolling coordination technologies of forecast information, current operation information and historical operation information are considered simultaneously, the robust safe operation ranges of the system are obtained, and therefore the dispatching plans are not limited to a unique preset value, and flexible dispatching in the robust ranges can be achieved. The obtained dispatching plans can be used for coping with stochastic volatility of new energy power generation better, and safety and economical efficiency are both considered.
Owner:HUAZHONG UNIV OF SCI & TECH
Forecasting net load in a distributed utility grid
ActiveUS20170317495A1Generation forecast in ac networkLoad forecast in ac networkLoad forecastingElectrical energy consumption
A method for generating a net load forecast for a utility grid, the grid including intermittent distributed energy resources and loads, comprising: defining two or more load forecast zones, each zone being associated with a load profile type and a climate zone type; assigning each of the loads to one of the zones based on the load profile and climate zone types associated with the load; assigning each of the energy resources to at least one of the zones based on the climate zone type associated with the energy resource; for each zone, generating an electrical energy consumption forecast for loads, an electric power generation forecast for energy resources, and a net load forecast from the electrical energy consumption and electric power generation forecasts; combining the net load forecast for each zone to generate the net load forecast for the grid; and, presenting the net load forecast on a display.
Owner:GREEN POWER LABS INC
Load prediction based on-line and off-line training of neural networks
ActiveUS7552100B2Easy to generate electricityLow costAnalogue computers for electric apparatusDigital computer detailsNerve networkLoad forecasting
A method and system is provided for predicting loads within a power system through the training of on-line and an off-line neural networks. Load data and load increments are used with an on-line load prediction scheme to generate predicted load values to optimize power generation and minimize costs. This objective is achieved by employing a method and system which predicts short term load trends through the use of historical load data and short term load forecast data.
Owner:SIEMENS AG
Short-term load forecasting method
InactiveCN105608512AImprove accuracyImprove efficiencyForecastingCharacter and pattern recognitionLoad forecastingDistributed memory
The invention discloses a short-term load forecasting method, and belongs to the technical field of power systems. Aiming at the problems that mass data have too much noise, are long in training time and are easily trapped in local minimum or over-fitting and the like in the prior art, the method comprises: performing cluster analysis on historical load data to generate a typical load curve, and digging the generality of mass historical load data to achieve the effect of screening and training data for later load forecasting, thus eliminating the noise influence of the mass data; performing strong fitting of a complex nonlinear function by deep learning to solve the problems of over-fitting and local minimum of a conventional neural network, thus realizing accurate and quick short-term load forecasting; and further constructing a forecasting model by using a distributed memory computation framework Spark, thus improving the efficiency and the instantaneity of the whole short-term load forecasting flow. Compared with the prior art, the method can realize accurate and quick short-term load forecasting.
Owner:SOUTHEAST UNIV
Intelligent resource optimization method of container cloud platform based on load prediction
ActiveCN108829494AReduce overheadReduce performanceResource allocationTransmissionLoad forecastingResource utilization
The invention discloses an intelligent resource optimization method of a container cloud platform based on load prediction, and belongs to the field of container cloud platforms. The method comprisesthe following steps of: based on a grayscale model, predicting the load condition of the next time window of each container instance according to the historical load of the container instance; judgingwhether the load of a node is too high or too low according to the load prediction value of all containers on each physical node; then executing the corresponding scheduling algorithm, migrating somecontainers on the node with over high load to other nodes, so that the load of the node is in a normal range; migrating all container instances on the node with over low load to other nodes so that the node is empty. According to the invention, aiming at the problem that the resource utilization is not balanced and the resource scheduling is delayed in a prior data center, load forecasting analysis is introduced, the load of the data center is scheduled and optimized in advance, the performance loss caused by the over high load of the node and the low resource utilization rate caused by the over low load are avoided, thereby improving the resource utilization efficiency of the platform.
Owner:杭州谐云科技有限公司
Regional energy comprehensive coordination management and control system
ActiveCN104616121ARealize rationalityTo achieve consumptionResourcesSystems intergating technologiesLoad forecastingControl system
The invention discloses a regional energy comprehensive coordination management and control system. The system comprises a regional operation monitoring subsystem, a distributed power forecasting subsystem, a load cluster prediction response analysis subsystem, a fault fast processing subsystem, an energy analysis and management subsystem, an electric vehicle optimal scheduling subsystem and a regional multi-level energy comprehensive coordination control subsystem. The regional energy comprehensive coordination management and control system achieves comprehensive operation monitoring of power sources, power grids and user loads in a region and forecasting, analysis and scheduling of a variety distributed energy resources on the basis of multi-source information integration, achieves rapid fault diagnosis and processing of the power grid, achieves load forecasting, energy consumption analysis, energy saving management and electric vehicle intelligent scheduling of users and achieves reasonable distribution and pluralistic complementary of regional energy through comprehensive coordination of energy interaction among the power sources, the power grids and the user loads, thereby greatly increasing the energy efficiency and enabling the power grids to be in economical and efficient operation.
Owner:NANJING DIANRUN TECH
Short-term load prediction method based on clustering and sliding window
The invention relates to a short-term load prediction method based on a clustering and sliding window. The method comprises the following steps of: preprocessing electric power load data; clustering historical data of a prediction user by utilizing a clustering algorithm, and adjusting clustering parameters; selecting k data from near to far of the prediction time in a category, containing most data, in clustering results to form a sliding window k; predicting the k selected data by utilizing a combination model based on the sliding window, and acquiring a primary prediction result; and correcting the primary prediction result of the combination model according to meteorological factors to obtain a final load prediction result. Compared with the prior art, the method has the advantages of high prediction precision, good adaptability and the like.
Owner:STATE GRID CORP OF CHINA +1
Optimal configuration method suitable for energy storage power of electrical power system with wind electricity
InactiveCN103023066ATroubleshoot Power Balance IssuesForecast Error StabilizationSingle network parallel feeding arrangementsEnergy storageElectricityLower limit
The invention discloses an optimal configuration method suitable for the energy storage power of an electrical power system with wind electricity. The method comprises the following steps of: S1, obtaining the sample data of the wind power and the load of the electrical power system with wind electricity; S2, obtaining a positive rotation spare capacity and a negative rotation spare capacity according to the sample data and an energy storage power configuration model, wherein the energy storage power configuration model takes that the energy storage power used by the electrical power system in a dispatching cycle is the minimum as an object function, takes that the sum of the rated total force output upper limit of thermal power generating units in the electrical power system and the energy storage power upper limit is greater than the actually generated net load value as a positive rotation spare chance constraint, and takes that the sum of the rated total force output lower limit of the thermal power generating units in the electrical power system and the energy storage power lower limit is less than the actually generated net load value as a negative rotation spare chance constraint; and S3, obtaining the optimal configuration for the energy storage power needed by the electrical power system with wind electricity for coping a net load prediction error according to the positive rotation spare capacity and the negative rotation spare capacity. Via the method disclosed by the invention, the minimum configuration for the energy storage power can be obtained, safe operation can be ensured, and cost can be saved.
Owner:HUAZHONG UNIV OF SCI & TECH +2
Who we serve
- R&D Engineer
- R&D Manager
- IP Professional
Why Patsnap Eureka
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com