1950 results about "Electrical load" patented technology

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

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

The invention provides a configuration method and system for an overall electrical scheme of a machine room, and relates to the technical field of electrical planning, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out feature extraction and semantic mapping, constructing a high-dimensional semantic association network, carrying out semantic enhancement, semantic modeling and logic association, extracting an abnormal mode, and obtaining a machine room operation state portrait; determining a machine room energy consumption rule and high-energy-consumption equipment, carrying out composite characteristic analysis to obtain a machine room electrical load curve, carrying out hierarchical progressive prediction in combination with exogenous variables, constructing a target function, solving to obtain optimal configuration parameters, and generating a power distribution system design scheme; a self-learning controller is added in a power distribution unit, the electrical load of a machine room is optimized, a power distribution equipment degradation mechanism model is constructed, service life prediction is performed, a power distribution equipment service life management strategy is output, equipment running abnormally is identified, abnormal reasons are judged by referring to historical abnormal data, and an abnormal report is generated and sent to equipment maintenance personnel.

The invention discloses an industrial and commercial park electrical load power prediction method, and relates to the technical field of electrical load prediction, and the method comprises the steps: obtaining park electrical equipment power data, and obtaining a standardized time sequence power matrix through normalization preprocessing; performing wavelet denoising and empirical mode decomposition on the power matrix, and separating a power trend component from a fluctuation component; constructing a trend prediction sub-model by using a long-short term memory network, and constructing a fluctuation prediction sub-model by using a seasonal ARIMA model; carrying out weighted combination on the two sub-models through an integrated learning method, and constructing a comprehensive prediction model; and finally, based on the historical prediction error, setting an adaptive threshold value for constraint processing, and outputting a final prediction result. According to the method, power data features are adaptively separated through a multi-level decomposition strategy, a dual-model collaborative prediction mechanism is adopted, environmental factors are introduced to improve prediction precision, a self-adaptive correction mechanism is designed to ensure result reliability, and accuracy and stability of park power load prediction are remarkably improved.

An energy management system can comprise a housing configured to receive energy storage assemblies within an interior region of the housing via one or more openings on a surface of the housing. One or more guide tracks positioned within the interior region can mechanically and electrically couple to the energy storage assemblies. One or more switches can electrically couple the energy storage assemblies in series to increase a voltage provided from energy storage assemblies to an electrical load and can electrically couple the energy storage assemblies in parallel to facilitate charging the energy storage assemblies from an energy source.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention relates to the technical field of state monitoring, and discloses a tower inclination state monitoring method and system. The method comprises the following steps: performing multi-field digital coupling modeling on an electric power tower to obtain a digital twinborn model and calculating an electrical load-structure response sensitivity matrix; collecting a multi-modal sensing characteristic data set including tower inclination angle time sequence data, insulator chain offset, tower body micro-vibration spectrum and foundation settlement gradient; performing time-delay correlation analysis on the electrical event and the structure response to obtain an electrical-structure mapping matrix; tensor decomposition processing is carried out through a multi-mode inclination feature fusion network, and tower inclination mode fingerprints are obtained; and carrying out abnormal separation processing on the tower foundation area, the connection area and the upper structure area, and outputting structural inclination and non-structural inclination judgment results. According to the invention, the accurate mapping relation between the electrical load and the structure response is realized, the false alarm rate is effectively reduced, and the accuracy and timeliness of early warning are improved.

The invention provides a machine learning-based multi-dimensional auxiliary decision analysis method and system for power supply of a distribution network. The method comprises the following steps of: S1, acquiring historical data and historical electrical load data of the distribution network; s2, obtaining a power supply demand prediction model through the long and short term memory network and the historical data of the distribution network; s3, obtaining a load change model through a time sequence prediction algorithm and historical electrical load data; s4, identifying the real-time operation data of the distribution network based on the output of the steps S2 and S3 to obtain a power supply capacity change trend and a load margin; s5, establishing a comprehensive objective function based on the output in the step S4, performing optimization solution on the objective function through a genetic algorithm, and outputting an optimal power supply decision scheme; and S6, executing the output of the step S5, monitoring the power supply operation parameters of the distribution network in real time, and dynamically adjusting the optimal power supply decision scheme based on the power supply operation parameters of the distribution network. According to the invention, the reliability of distribution network power supply is improved, and the dynamic adjustment and optimization of the distribution network power supply decision scheme are realized.

The invention relates to the technical field of electrical load management, and discloses an electrical load spatial-temporal distribution modeling and adaptive optimization regulation and control system. The system comprises a load monitoring module, an optimization decision-making module, an exception handling module, a communication coordination module and a data tracing module. The load monitoring module collects load data by using a space-time correlation analysis technology; the optimization decision-making module adopts a dynamic clustering algorithm to generate a regulation and control strategy; the exception handling module corrects load exception in a graded manner based on a multi-scale coordination mechanism; the communication coordination module realizes instruction interaction through a hierarchical routing protocol; and the data tracing module generates a topology storage index by using a multi-dimensional feature coding algorithm. The system can accurately collect load data, reasonably distribute power, effectively process load abnormity, guarantee communication stability, facilitate data tracing, improve the operation efficiency and reliability of an electric power system, and meet the requirement of a modern electric power system for electrical load management.

The invention discloses an optimized dispatching control method and system for transformer area energy storage, and relates to the technical field of energy storage dispatching, and the method comprises the following steps: integrating an intelligent load management system with an energy storage system, thereby monitoring the electrical load of a transformer area in real time, and feeding back the real-time data to the energy storage system; historical load data and electricity price data are collected, a prediction model is trained, future load and electricity price are predicted in real time, and prediction data are generated. And according to a prediction result, a preliminary energy storage system scheduling strategy is formulated, and a target function R is constructed, so that the target function R can dynamically adapt to changing load and electricity price conditions. According to the invention, through tight integration of the intelligent load management system and the energy storage system, real-time monitoring of the power grid load and efficient scheduling of the energy storage system are realized; in other words, the energy storage system can charge or discharge at the best time in combination with accurate load and electricity price prediction and scheduling strategy rules, power grid resources are utilized to the maximum extent, and unnecessary resource waste is avoided.

The invention discloses a box-type energy storage substation operation control method and system, a product and a medium. The method comprises the following steps: collecting gas concentration data in the box-type energy storage substation, comparing the gas concentration data with a preset threshold value, and judging whether gas leakage exists or not; if yes, recording related data, and detecting an insulation resistance value of the electrical contact component; based on the gas concentration data and the leakage time of the leakage area, judging whether a chemical reaction triggering condition is met or not; if yes, inert gas is sprayed to the leakage area; if the insulation resistance value is lower than a safety threshold value, electromagnetic isolation is carried out on the leakage area, and distribution of the electric load to the related energy storage unit is stopped; then, current is applied to the electrical contact component at a preset frequency, and an insulating property value is detected; when the insulating property is restored to within the safe range, the electrical load is redistributed to the leakage region. By implementing the technical scheme, the safety of the fault area after the insulation performance is reduced due to gas leakage in the operation process of the box-type energy storage substation is improved.

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

The invention discloses a laboratory air-conditioning temperature control system and method based on artificial intelligence, and relates to the field of laboratory temperature control. Optimizing the positions of the air supply and exhaust outlets by using a pre-constructed airflow analysis model; according to the optimized positions of the air supply and exhaust outlets, the air conditioning system is used for controlling airflow to flow to the corresponding air supply and exhaust outlets, and directional airflow from the first area to the second area is generated; according to laboratory electrical load data, an optimal charging and discharging strategy of an energy storage device is generated by using a reinforcement learning algorithm, and the reinforcement learning algorithm enables an intelligent agent to learn optimal strategies in different states by constructing a state space including electricity price, electrical load, energy storage electric quantity and photovoltaic power generation and setting a reward function with maximum load stability. Aiming at the low laboratory temperature control precision in the prior art, the temperature control precision is improved by simulating an airflow velocity field and radiation quantity distribution and optimizing the layout of air supply and exhaust outlets by using CFD simulation and a genetic algorithm.

A vehicle having a primary mover, a charging system, multiple electric loads, and a controller. The charging system includes a charge storing device and an alternator. The alternator is configured to convert mechanical energy generated by the prime mover into electrical energy to charge the charge storing device. The multiple electrical loads are electrically coupled to the charging system via a power distribution module. The controller is communicably coupled to the charging system and is configured to receive an indication that an electrical output of the charging system is unable to provide sufficient electrical energy to each electrical load in the electrical loads. The controller is also configured to provide a control signal to the power distribution module in response to the indication. The control signal is configured to cause the power distribution module to decouple at least one of the electrical loads from the charging system.

The invention discloses a resident household electrical appliance electrical load prediction method and device based on deep learning, and a medium. The method comprises the steps of collecting resident user electrical appliance load data; constructing a training set according to the collected data; building a multi-task prediction model with a long-time and short-time view field based on deep learning, wherein the multi-task prediction model is used for predicting whether electric equipment works or not and predicting an electric load of the equipment; designing a three-stage training strategy of the multi-task prediction model, and training the model by adopting the training set; and using the trained multi-task prediction model to predict the working state and the electrical load of each electrical device of each resident user accessed to the virtual power grid in a future short period. According to the method, the multi-task prediction model is established, the long-time and short-time key information of the historical power consumption data can be efficiently extracted through the three-stage strategy training model, and the method has the multi-task associated feature extraction capability, dynamically adapts to different scenes and accurately predicts the working state and load change of the power consumption equipment.

The invention relates to the field of intelligent early warning, and particularly discloses a composite home fire early warning system combined with smoke and gas monitoring, which is characterized in that firstly, multi-parameter acquisition is started by taking temperature abnormity as a trigger condition, and gas concentration, smoke diffusion and electrical load data are synchronously acquired; local feature extraction is carried out on the time sequence of each parameter; and then establishing an association relationship among the parameters through a cross-time-sequence dynamic message passing fusion mechanism. And finally, fusing the multi-dimensional time sequence characteristics, inputting the fused multi-dimensional time sequence characteristics into a risk grading model, and judging the current risk grade in combination with a historical fire evolution rule. According to the scheme, through time-space correlation analysis of multi-dimensional parameters, daily environment fluctuation (such as cooking fume) and real fire initial characteristics can be effectively distinguished, the method can adapt to risk characteristics of different house type structures, and upgrading from passive alarm to intelligent pre-judgment is achieved.

The invention relates to the technical field of multi-energy complementary power generation, and discloses an industrial steam supply peak shaving system and method coupling fused salt heat storage and solar energy, and the industrial steam supply peak shaving system comprises a coal-fired power generation system and an industrial steam supply system; in the heat storage mode, the boiler, the steam cooler and the condenser are sequentially connected; in the heat release mode, the high-pressure heater, the primary feed water heater and the low-pressure cylinder are sequentially connected through pipelines; and the feed pump, the secondary feed water heater and the boiler are connected in sequence. In the low ebb of the electrical load, solar energy is used for heating fused salt to store heat, and part of high-temperature steam is extracted from steam-water circulation of the improved coal-fired power generation system to store the heat in a heat storage device and does not enter a steam turbine for power generation; the power load output is adjusted by adjusting the thermal load output of the thermodynamic system in the power generation process, and load decoupling of the boiler and the steam turbine is achieved. And in the electrical load peak, the stored heat energy is released back to steam-water circulation, and the electrical load of the coal-fired power generation system is increased.

The invention discloses a distributed robust optimization scheduling method and system for a solar photovoltaic photo-thermal comprehensive utilization system, and belongs to the field of comprehensive utilization of renewable energy sources. The method comprises the steps of obtaining an electrical load and a thermal load based on an integrated load prediction model; obtaining typical sunlight photovoltaic and photo-thermal based on scene clustering analysis; based on photovoltaic, photo-thermal, electrical load and thermal load, an objective function and a constraint condition for optimal scheduling are set for the solar photovoltaic photo-thermal comprehensive utilization system, and based on the objective function and the constraint condition, a two-stage distribution robust optimal scheduling model is obtained by combining a comprehensive demand response mechanism; and solving the two-stage distribution robust optimization scheduling model by adopting a column and constraint generation algorithm to obtain a two-stage distribution robust optimization scheduling result. The method obviously improves the utilization rate of renewable energy sources, reduces the operation cost and carbon emission of the system, and is suitable for building energy systems in solar energy-enriched areas.

The invention relates to a clustering-based industry subdivision electricity sale quantity prediction method and device and a storage medium, which are applied to the technical field of electricity market transactions, and comprise the steps of obtaining representation vectors in a historical preset period of time of an electricity user, so that the electricity consumption behavior of the user can be represented more accurately, the accuracy of a prediction model is improved, and the user experience is improved. Meanwhile, vectorization representation is carried out on the attributes of the users, and the attributes are spliced with power consumption behavior representation vectors, so that more comprehensive user features can be provided through the integration, and the generalization ability of the model is enhanced; according to the method, high-dimensional original time sequence data is converted into low-dimensional feature vectors, so that the calculation burden of a clustering algorithm is reduced, the clustering efficiency is also improved, users are divided into different class clusters, modeling prediction is carried out for the electrical load of each group of users, clearer model interpretation can be provided, and the user experience is improved. And the electricity utilization characteristics of different user groups can be understood conveniently.

An integrated electrical management system includes an electrical panel, at least one busbar mounted to the electrical panel, at least one controllable breaker electrically coupled to the at least one busbar, and processing equipment configured to process electrical signals. The processing equipment includes control circuitry configured to control the at least one controllable breaker, and one or more current sensing modules configured to sense a respective current for each of the at least one controllable breakers. The system senses currents or otherwise electrical load in branch circuit, and is configured to control the breakers on or off to control electrical load in each branch circuit. The system may receive user input, input from devices, input from other systems or network entities, or inputs from sensors, and in response determines operating parameters. Operating parameters may include on-off schedules for breakers, temporal information, fault information, or other suitable operating parameters.

The invention discloses a non-intrusive load decomposition method and system based on an xLSTM-Transform model, and is applied to the technical field of non-intrusive load decomposition. XLSTM is constructed by combining sLSTM (scalar LSTM) and mLSTM (matrix LSTM), so that the processing capability of the model on time sequence data is enhanced; a causal convolution module is added behind an input layer, a sliding window in a fixed direction of the causal convolution module can more accurately extract a local load mode of an electric appliance while ensuring causality, and future information leakage is avoided; a self-attention mechanism in a Transform architecture is introduced to establish an xLSTM-Transform combination model, historical data is adopted to train the model, and finally, the model is used to perform electric appliance load decomposition. According to the method, a multi-scale electrical load mode can be effectively processed, and the problem of long-term dependence faced by a traditional time sequence model is solved.

The invention discloses an electricity stealing detection method and device for a low-voltage distribution network, an intelligent electric energy meter, equipment and a medium, and the method comprises the steps: collecting the electricity utilization information and internal temperature state data of a detected electric energy meter in real time, and respectively generating corresponding real-time electricity utilization load change trend data and temperature change trend data; detecting each electric device loaded by the detected electric energy meter, and identifying the type of each electric device; predicting the electrical load of the tested electric energy meter according to the identified type of each electric device; according to the predicted electrical load of the tested electric energy meter, generating corresponding predicted electrical load change trend data; and according to the real-time electrical load change trend data, the real-time temperature change trend data and the predicted electrical load change trend data in the same time period, judging whether the detected electric energy meter has an electricity stealing behavior or not. According to the invention, the electricity larceny behavior in the low-voltage power distribution network can be quickly and accurately identified, the electricity larceny position is accurately positioned, and electricity larceny prevention is effectively realized.

The invention relates to the technical field of big data analysis and data processing, and provides an intelligent power utilization load management method based on big data analysis, which constructs an accurate power utilization load prediction model by integrating historical power utilization data, climate data, a power grid equipment state and user behavior data. According to the method, the security and traceability of prediction data are improved by adopting a block chain technology, and automation of an electrical load management process is realized by means of an intelligent contract. The method comprises the specific steps of data acquisition and preprocessing, load prediction model construction and verification, prediction data storage and hash value generation, intelligent contract writing and execution, and process node data processing and evaluation. The method effectively solves the problems of load data abnormity and model errors, and improves the stability and management efficiency of a power grid system.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention provides an electrical load data privacy protection method and system, a storage medium and electronic equipment, and is applied to the technical field of electrical data processing. Actual electrical load data of a user and noise data of an energy storage device are collected in real time, and charging and discharging behaviors of the energy storage device are controlled through electricity price linkage; the optimization noise is superposed to the actual power utilization load data to generate target power utilization load data meeting the power grid transmission limitation, the energy storage charging and discharging noise is utilized to cover the real power utilization mode of the user, the energy storage charging and discharging behavior is controlled through electricity price linkage to guarantee the economic benefit, and the dynamic balance of the privacy intensity and the energy storage economical efficiency is achieved.

The invention relates to the technical field of vehicle power supply management, in particular to a vehicle load power supply distribution method, device and equipment and a storage medium, according to the scheme, a vehicle using scene at the current moment is determined by analyzing vehicle operation data, and the load levels of a plurality of electric appliances in use are determined by combining a vehicle using scene-load level mapping table; and a power supply distribution scheme is formulated, on-demand power supply for each electricity load of the vehicle is realized, and meanwhile, a user is allowed to customize load grade setting of a vehicle using scene. According to the scheme, through reasonable distribution of power supply, the power supply proportion of each load can be adjusted in real time according to the vehicle use scene on the premise of ensuring normal operation of key functions of the vehicle, so that the operation efficiency and stability of the vehicle are improved, it can be ensured that each electricity load obtains proper power supply in different scenes, the energy utilization efficiency is improved, and the energy consumption is reduced. The personalized electricity demand of the user is met, the driving experience of the user is improved, and the intelligent level of the vehicle is improved.

The invention discloses a power grid dispatching method based on emergencies and a graph structure. The method comprises the following steps: establishing a baseline load prediction model by collecting historical electrical load data; recognizing emergencies influencing the power utilization demand in real time; and in combination with real-time power consumption behavior data, constructing a time-varying power consumption disturbance factor, and dynamically correcting load prediction. Geographic distribution and historical power generation data of new energy power stations such as wind power and photovoltaic power stations are acquired, a power generation graph structure model is constructed, the power generation amount is independently predicted by adopting a long-short-term memory network, and a prediction result is corrected in combination with a graph structure. And based on the corrected load and power generation prediction, considering the capacity and the energy state of the energy storage system, optimizing multi-time-sequence-scale energy scheduling, and formulating an energy storage charging and discharging and generator set operation scheme. According to the method, the dynamic influence of emergencies and the spatial relevance of new energy are fused, the load prediction precision and the new energy power generation prediction accuracy are remarkably improved, more reasonable power grid dispatching is achieved, and the safety and stability of a power grid are enhanced.

A load control device for regulating an electrical load includes: a support component including a connection portion; a knob frame including a gripping portion and an elastic portion, the gripping portion at least partially surrounding the support component and configured to drive the knob frame to rotate in response to a rotational force acting thereon, the elastic portion configured to move toward the connecting portion in response to the knob frame moving in a first direction, thereby undergoing elastic deformation; a first circuit board component; a switch trigger component; a switch component coupled to the first circuit board component, for controlling the load in response to triggering and releasing the triggering from the switch trigger component; and an adjustment component coupled to the first circuit board component, for adjusting the load in response to the rotation of the knob frame. The load control device simultaneously realizes rotation and pressing functions of the knob frame. A system including the load control device is also disclosed.