2761 results about "Electric consumption" patented technology

The invention relates to an optical storage cooperative control optimization method, electronic equipment and a medium. The method comprises the steps of collecting historical energy consumption and photovoltaic data of a park, constructing a photovoltaic electronic model based on the data, performing day-ahead prediction by adopting CNN-BiGRU, and generating a load power and photovoltaic generation power sequence; calculating the electricity consumption and the photovoltaic generating capacity of each time period; setting the capacity of an energy storage module, and calculating the energy storage charging and discharging capacity based on an SOC formula; according to the electricity consumption, the generating capacity and the energy storage capacity, determining a peak section discharge demand; photovoltaic fluctuation constraint is combined, and energy storage charging and discharging power is adjusted to stabilize fluctuation; optimizing load power utilization, photovoltaic power generation and energy storage power scheduling based on the power balance relation; and constructing a revenue maximization objective function, establishing a light storage collaborative optimization model in combination with constraints, and solving an optimal operation scheme of a prediction day. According to the method, photovoltaic power generation errors are reduced, power output is balanced, power grid requirements are met, the photovoltaic consumption rate is improved, healthy operation of the system is guaranteed, and operation benefits are improved.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention relates to an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.

The invention provides an industrial carbon footprint dynamic prediction method fusing multi-source data, which comprises the following steps: step 1, collecting multi-source data, core data including electricity consumption, converted standard coal consumption, key industrial process product yield and carbon emission, and three types of extended data including new energy consumption data, process upgrading data and external data; 2, performing dynamic modeling according to the multi-source data obtained in the step 1, including dynamic carbon emission factor calculation and a process-environment coupling equation; 3, an industrial carbon-energy-economy multi-objective optimization model of dynamic coupling new energy consumption is constructed, minimization of carbon emission and operation cost is taken as an objective function, and constraint conditions comprise production requirements, energy supply and policy compliance; and 4, performing optimization solution on the target function of the multi-target optimization model by adopting an NSGA-II algorithm, and outputting a carbon emission-cost tradeoff curve.

The invention relates to the technical field of battery electric quantity estimation, in particular to a lithium ion battery electric quantity accurate estimation method and system based on a BMS. The method comprises the following steps: collecting BMS original data, carrying out voltage current interval slicing, constructing a charging and discharging period node map, detecting node interruption joints, comparing slope differences to identify charging and discharging turning points, dividing independent charging and discharging periods according to the turning points, and determining current change time sequence data of each period. The actual charging and discharging electric quantity is calculated through the time sequence data, the charging and discharging difference is compared, the historical input and discharging electric quantity is further inquired, the fuzzy electric quantity range of the battery is deduced, and finally the real-time remaining available electric quantity is accurately calculated according to the fuzzy electric quantity range and the actual charging and discharging electric quantity difference and is uploaded to the BMS system. According to the invention, real-time monitoring and management of the electric quantity estimation result are realized, and the intelligent level of the battery management system is improved.

The invention relates to a method, device and equipment for controlling energy conservation of a clean room with an AI algorithm, and the method comprises the steps: collecting real-time environment parameters and equipment power consumption data through a temperature and humidity sensor, a cleanliness monitor, a dust falling particle catcher and an intelligent electric meter which are disposed in the clean room; associating the real-time environment parameters and the equipment electricity consumption data with production scheduling information to generate a dynamic regulation and control strategy; based on the real-time environment parameters, the equipment electricity consumption data, the dynamic regulation and control strategy and historical energy consumption data, a start-stop strategy of the fan filter unit is calculated through a deep learning model, and the start-stop strategy comprises a three-gear hierarchical regulation and control strategy for an alternating current type FFU. And the voltage and the rotating speed of the fan FFU are dynamically adjusted according to the direct current type FFU, so that intelligent linkage of production requirements and environment control is achieved, and the purposes of reducing cost and increasing efficiency are achieved.

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 user electric quantity abnormity detection analysis method and system, and relates to the technical field of electric quantity monitoring and detection, and the method comprises the steps: obtaining an electric quantity detection value of a target user in a preset period, dividing the period into a plurality of stages, carrying out the stage sampling detection, and obtaining the electric quantity of a first stage and the electric quantity of a second stage. A target reference electric quantity is set according to the two electric quantities. When the absolute value of the deviation between the target detection electric quantity and the reference electric quantity does not meet the preset threshold value, the system sends out an abnormal early warning signal, and a preset electric quantity checking strategy is called according to the signal to check the electricity consumption of the user. The technical problem that single reason troubleshooting is insufficient in existing electric quantity anomaly detection is solved, and the technical effect of improving the electric quantity anomaly detection accuracy and efficiency is achieved by recognizing and solving electric quantity anomaly caused by user behaviors, hardware faults or software problems.

The invention relates to a virtual power plant load end resource optimization scheduling method and device and a medium, and is applied to the field of data processing, and the method comprises the steps: obtaining historical total electricity consumption corresponding to a plurality of historical periods of an energy supply range of a virtual power plant; according to the historical total electricity consumption corresponding to a plurality of historical periods of the energy supply range of the virtual power plant, estimating the first total electricity consumption required by the next period; for each region, according to the probability distribution model corresponding to the region and the first total electricity consumption, estimating the estimated electricity consumption of the maximum probability corresponding to the region; and performing power scheduling according to the estimated power consumption of the maximum probability corresponding to each region. According to the invention, after overall estimation, each region can be finely estimated based on the first total power consumption after overall estimation, overall data are considered, estimation data of each region are also considered, and the accuracy and reliability of power estimation are effectively improved.

The invention discloses an intelligent power distribution energy-saving power supply system and method, relates to the technical field of power systems, and solves the technical problems that the user experience is poor due to the lack of consideration of the user participation degree in the prior art, and the efficiency of intelligent power distribution energy-saving power supply is low due to the fact that the electric quantity generated by renewable energy cannot meet the user demand. Obtaining predicted electricity consumption of the plurality of areas through an electric power prediction model; calculating a regional power utilization priority coefficient according to the regional data; the power station state is adjusted according to the predicted electricity consumption and the regional electricity consumption priority coefficient, the user participation degree is considered, whether the generating capacity meets the electricity consumption requirement or not is predicted, regulation and control are conducted in advance, the situation of insufficient power supply is reduced, and the user experience and the power supply efficiency are improved.

The invention belongs to the technical field of smart power grids, and discloses a multi-energy collaborative optimization regional power peak regulation cloud service system and method. The method comprises the following steps: collecting regional electricity consumption time sequence data, and collecting photovoltaic power station key data and wind power station key data in unit time; preprocessing the key data of the photovoltaic power station and the key data of the wind power station to obtain preprocessed key data of the photovoltaic power station and preprocessed key data of the wind power station; inputting the preprocessed key data of the photovoltaic power station into a photovoltaic power generation prediction model, inputting the preprocessed key data of the wind power station into a wind power generation prediction model, and respectively obtaining predicted photovoltaic power generation and predicted wind power generation; inputting the regional electricity consumption time sequence data into the regional electricity consumption prediction model to obtain future unit time regional electricity consumption; according to the invention, the prediction accuracy of the regional predicted electricity consumption and the predicted generating capacity of the distributed energy power station is improved.

The present invention is applicable to the technical field of carbon emission monitoring. Provided are a carbon emission prediction method and a related apparatus. The method comprises: acquiring total carbon emission and total power consumption of each industry; calculating a correlation index of the total carbon emission and the total power consumption, so as to screen a target monitored industry; acquiring carbon emission and power consumption of the target monitored industry; on the basis of the carbon emission and the power consumption, calculating an electricity-to-carbon emission transfer coefficient based on the target monitored industry; on the basis of the target monitored industry, matching a target monitored enterprise, and on the basis of randomness, periodicity and climate factors, constructing a power consumption prediction model of the target monitored enterprise; acquiring actual power consumption of the target monitored enterprise and inputting same into the power consumption prediction model for training; matching an outputted predicted power consumption value with the electricity-to-carbon emission transfer coefficient; and generating a predicted carbon emission value. On the basis of the linear relationship between carbon emission and power consumption, the present invention uses power consumption prediction to estimate predicted carbon emission values, thus effectively improving the efficiency and accuracy of carbon emission prediction.

The invention belongs to the technical field of intelligent power grids, and discloses an intelligent power grid load balance control method and system. The method comprises the steps of dividing a power grid into a plurality of regions based on a predetermined rule; based on the regional power supply quantity and the regional power consumption quantity, identifying an overload region with insufficient power supply quantity; formulating and executing a load balancing strategy for the overload area by adopting a natural heuristic optimization algorithm, wherein the load balancing strategy is a load scheduling strategy taking scheduling time minimization as a target; calculating the priority coefficient of the electric equipment in the overload area, and supplying power to each electric equipment according to the priority coefficient; effective balance and optimal allocation of intelligent power grid loads are achieved, emergency power supply is guaranteed to the maximum extent in natural disaster time, and therefore the power grid operation level and livelihood guarantee capacity are improved, and the power supply reliability and the power utilization efficiency of the power grid are improved.

The invention belongs to the technical field of power supply, and discloses a source network load storage power supply-based energy control method and system and a storage medium, and the method comprises the steps: determining a first photovoltaic power generation amount and a first power consumption demand amount of a prediction day at a set moment based on first historical data and prediction data, calculating a difference value between a first available power consumption amount and the first power consumption demand amount, and when the difference value is greater than a first preset value, establishing a cost objective function based on the capacity of the energy storage device, the difference value, the operation loss and maintenance loss of the energy storage device, and the electricity price cost of each time period in the prediction day, and charging the energy storage device based on the charging time and the charging electric quantity obtained by solving the cost objective function. And periodically determining a second photovoltaic power generation amount and a second power consumption demand amount in each set time period, and carrying out charging and discharging control on the energy storage device according to a source-grid-load-storage cooperative power supply mechanism. According to the method, the source network load storage power supply system can be comprehensively optimized through accurate prediction and control.

The invention discloses an energy storage management system of a photovoltaic power station, relates to the technical field of energy storage management, and solves the technical problems that energy storage optimization analysis of different time periods cannot be performed according to data of the power station, and the overall energy storage efficiency is further affected. Generating a generating capacity interval and a power consumption interval, calculating a time interval judgment value, accurately marking a unit time period as a peak period or a valley period, calculating a discharge power level in the peak period in combination with the change of power grid frequency and voltage and a real-time power grid load, and performing discharge power adjustment and optimized discharge analysis; in the low ebb period, energy storage management is performed according to the relation between the power consumption and the power supply quantity and the power transmission quantity of the distributed power supply, the utilization efficiency of photovoltaic power generation and the reliability of the energy storage system are improved, the proper preselected power supply can be found for energy storage adjustment by screening and selecting the distributed power supply, and the flexibility and the reliability of the system are improved.

The invention relates to the technical field of power load analysis, and discloses an intelligent ammeter power load analysis system and method, and the method comprises the steps: obtaining the current overall power consumption of a user, judging whether the power consumption of the user is abnormal or not, forming abnormal analysis data, and carrying out the corresponding abnormal prompt for the user according to the abnormal analysis data. According to the intelligent electric meter power load analysis system and method, whether the electric equipment is abnormal or not is judged according to the power utilization data collected by the electric meter, the abnormal reason of the electric equipment is judged according to the power utilization data, and a user is timely notified to carry out adjustment according to the abnormal reason of the electric equipment; therefore, the service life of the electric equipment is prevented from being shortened due to the fact that the use condition of the electric equipment cannot be adjusted in time, the equipment is prevented from being damaged due to the fact that equipment abnormity cannot be perceived in time, the service life of the equipment is prolonged, property loss of a user is reduced, fire catching of the electric equipment is reduced, and personal injury to personnel due to fire disasters is avoided.

The invention discloses an enterprise electricity consumption analysis and prediction method based on data mining, and relates to the technical field of DC power supply monitoring, historical electricity consumption data is acquired, and electricity consumption characteristic analysis and mining are performed from multiple directions according to the historical electricity consumption data to obtain electricity consumption rule information and seasonal change characteristics; analyzing the scale and speed increase of the agent electricity purchasing users and the annual change trend of the agent electricity purchasing quantity to obtain development and operation information of agent electricity purchasing; based on historical electric quantity data, constructing exponential smoothing, ARIMA, SARIMA and LSTM neural network multiple time sequence prediction models, and performing comparison to select an optimal model; through accurate power consumption prediction, the enterprise is helped to optimize power resource configuration, plan power purchase work in advance and reduce the power cost of the enterprise, and especially under the condition that the power market fluctuates greatly, the enterprise can better understand the market dynamic condition and formulate a corresponding power purchase strategy by predicting the scale and speed increase of the proxy power purchase user.

The invention relates to the technical field of data reasoning, and discloses an electricity consumption prediction method, which comprises the following steps: generating a conventional data set and a holiday and festival data set; generating a weather-load coupling feature set according to the extracted load volatility of the historical power consumption data and according to the temperature abrupt change point and the load volatility; extracting a multi-scale time sequence characteristic sub-sequence, and adding elements one by one to obtain a conventional prediction model; performing cross-modal fusion on the date feature and the load feature of the electricity consumption based on the date key value pair to obtain a holiday and festival prediction model; selecting a prediction model according to the target date type, and outputting an electricity consumption prediction value in combination with the weather-load coupling feature set; when the deviation value between the predicted value and the actual electricity consumption of the target area exceeds the limit, the electricity consumption data with the deviation value exceeding the limit are fed back to the historical electricity consumption data in the S1, the prediction model is optimized, the final electricity consumption predicted value is obtained, and the accuracy of electricity consumption prediction can be improved.

The invention discloses an optical storage layered collaborative optimization control method coupling photovoltaic priority absorption and time-of-use electricity price, and relates to the technical field of new energy power system optimization control. The method comprises the following steps: taking a photovoltaic generating capacity prediction value, a load electricity consumption prediction value and peak and valley electricity price information as input data; carrying out rolling optimization by utilizing a model prediction control framework, and generating a light storage plan table; and calculating a photovoltaic output regulation value and energy storage charging and discharging power based on the generated light storage plan table in combination with the photovoltaic power generation power and the load power consumption power which are acquired in real time, executing corresponding energy storage charging and discharging and photovoltaic output processing by applying a decision tree control mechanism, and optimizing light storage control according to a processing result. According to the method, the photovoltaic preferential consumption strategy is executed, the light abandoning amount is reduced, the photovoltaic consumption rate is improved, the time-of-use electricity price dynamic response mechanism and the energy storage efficiency compensation and light abandoning punishment mechanism are combined, peak-valley arbitrage is maximized, meanwhile, the comprehensive electricity utilization cost is reduced, and the power purchase demand of a power grid is reduced.

The invention discloses a single-phase charge-control intelligent electric energy meter and an intelligent metering system thereof, relates to the technical field of electric variable measurement, and aims to solve the technical problem of inaccurate metering of the existing intelligent electric energy meter, and the single-phase charge-control intelligent electric energy meter comprises a working electric quantity calculation module which is connected to a power grid and is used for counting the electric quantity when the electric energy meter works and storing the electric quantity in a memory; the non-working electric quantity calculation module is used for calculating the electric quantity when the electric meter does not work; the working electric quantity calculation module further comprises a first processing unit which is used for calculating the working electric quantity consumed when the electric meter works according to the working duration of the electric meter; the first calculation unit is used for receiving the working electric quantity from the first processing unit, calculating corrected electric quantity according to the working electric quantity and historical electric quantity data stored in the memory, and transmitting the corrected electric quantity to the correction unit; and the correction unit is used for receiving the correction electric quantity transmitted by the first calculation unit and correcting the working electric quantity. The method has the advantage of improving the electricity metering accuracy of the electricity meter.

The invention provides a power supply optimization model and method for a converter station. The method comprises the steps that firstly, a converter station energy consumption characteristic analysis and electric quantity prediction model is constructed, and the converter station energy consumption characteristic analysis and electric quantity prediction model comprises user converter station energy consumption characteristic analysis based on wavelet transformation and an optimization fast density peak value clustering algorithm and a converter station electric quantity prediction model based on an XGBoost algorithm; then, constructing a wind power and photovoltaic novel power supply output prediction model, and adopting a wavelet decomposition-bidirectional long-short term memory network based on an Attention mechanism as a prediction model; and finally, in combination with the electric quantity prediction model and the novel power supply output prediction model, constructing a power supply combination optimization design model for the converter station. Through the model, the power optimization capability and the use efficiency of the converter station can be improved, electric energy resources are managed and reasonably utilized, and the economical efficiency and the safety of a power system are improved. The power supply optimization model and method for the converter station are innovative and have practical value.

The invention discloses an electric quantity monitoring system for an intelligent park, and the system comprises a data collection module which is used for extracting evaluation indexes from a historical database; the power distribution module is used for obtaining a power prediction value of the sub-park in a prediction period and predicted power consumption in a monitoring period through a power prediction model constructed based on a neural network model; and determining the power demand priority of the sub-park according to the evaluation index, and constructing a multi-target optimization model based on the electric quantity predicted value of the sub-park to obtain a power distribution scheme, and the electric quantity monitoring and regulation and control module is used for obtaining the actual electricity consumption of the sub-park in the monitoring period by using monitoring equipment, comparing the actual electricity consumption of the sub-park in the previous period with the predicted electricity consumption of the sub-park in the corresponding period, and adjusting an electric power distribution scheme according to the electricity demand priority of the sub-park and a comparison result. The invention relates to the field of park management, and solves the technical problem that a power distribution scheme in an existing electric quantity monitoring system is not intelligent enough.

The invention relates to the technical field of power grid operation control, in particular to a distribution network dispatching operation intelligent identification control method based on a cloud platform, which comprises the following steps: calculating a power consumption representative value of a power consumption area in each time interval according to power consumption data stored by the cloud platform, determining an interval characteristic value of each time interval according to the power consumption representative value, the method comprises the following steps: determining data properties of electricity representative values based on interval characteristic values, determining a parallel region according to the data properties, calculating combined path values in the parallel region to obtain an optimal combined region, and automatically adjusting route configuration by a power distribution network according to the optimal combined region so as to realize uniform distribution of electricity loads and avoid local overload or light load. And meanwhile, the reliability and economy of the power grid are enhanced.

The invention discloses an electricity consumption anomaly detection method, system and device based on an environmental perception graph convolutional network and a medium, and belongs to the technical field of smart power grids. The method comprises the steps that historical electricity consumption of a power grid area unit and external environmental factors are acquired to construct a multi-dimensional space-time association graph; a periodic trend component and environment-related residual fluctuation are separated through the multi-dimensional space-time correlation diagram, and a two-channel diagram convolution feature is obtained; in combination with the convolution features of the two-channel graph, feature space decoupling of a periodic channel and a residual channel is realized, and decoupling features are obtained; and extracting a historical residual fluctuation sequence based on the decoupling features, and constructing an environmental state sensitive probability density function. According to the method, the historical electricity consumption and the external environment factors such as temperature, humidity or weather events are closely fused by constructing the multi-dimensional space-time association diagram, so that the space-time dependence of the electricity consumption behavior is effectively captured, and misjudgment caused by ignoring environment dynamics in a traditional method is avoided.

The invention relates to the technical field of power, in particular to a power load prediction method based on improved DWT-LSTM. According to the technical scheme, the power load prediction method based on the improved DWT-LSTM comprises the following steps of S1, collecting power load data and related input variables thereof, including temperature, humidity and weather conditions, dividing the collected original data into a training set and a test set, and performing data cleaning; s2, the cleaned input variables are decomposed through a DWT method, the signals are decomposed into approximate coefficients and detail coefficients, the approximate coefficients represent low-frequency components of the signals, and the detail coefficients capture high-frequency changes of the signals; and S3, in combination with the input variables after the DWT decomposition, a long short-term memory network LSTM model is constructed, and the structural design of the LSTM model comprises an input layer, an LSTM layer and a full connection layer. According to the method, the change of the power consumption mode and the power consumption in various scenes is predicted in advance, the power consumption load and the power consumption can be allocated in advance, and stable operation of power is ensured.

The invention discloses a regional electricity consumption prediction method based on a hybrid model and a terminal. The method comprises the following steps: acquiring historical electricity consumption data of a target region; performing data preprocessing according to the historical power consumption data to obtain an original data set; performing data processing on the original data set through an empirical mode decomposition model to obtain a combined data set; constructing an electric quantity prediction model based on a convolutional neural network model and a dual echo state network model, and training the electric quantity prediction model according to the combined data set; and predicting the future power consumption of the target area through the trained power consumption prediction model. According to the method, the spatial feature extraction capability of the convolutional neural network and the time sequence feature prediction capability of the echo state network are effectively fused, so that the electric quantity prediction model can effectively learn spatial features and time features from historical power consumption data; according to the electric quantity prediction model, the prediction precision is effectively improved.

The invention discloses a transformer flexible capacity expansion method and system based on historical operation data. The method comprises the steps that the electricity consumption of subordinate load equipment of a distribution transformer is predicted based on the historical operation data; predicting the generating capacity of subordinate photovoltaic power generation equipment of the distribution transformer based on the historical operation data; calculating the residual capacity according to the rated capacity of the distribution transformer, the total load amount and the total photovoltaic grid-connected amount; when the residual capacity is insufficient, the energy storage equipment is controlled to discharge or the flexible interconnection device is used for mutual assistance; and when the residual capacity is surplus, controlling the energy storage equipment to charge or supply power to the adjacent transformer. According to the invention, the low-voltage flexible interconnection device is installed between the two transformers, the peak time of the superposed transformer load is predicted and analyzed according to the transformer load and the historical data of the operation of each device, the edge side energy management strategy of demand consideration is synthesized, and the efficient allocation and transfer of power resources are realized through the flexible mutual aid technology. Therefore, the power utilization requirement of flexible capacity expansion of the transformer area is met.

The invention provides a power grid supply and demand balance regulation and control method based on an improved multi-objective particle swarm optimization algorithm, and the method comprises the steps: taking the capacity of the power of each line of a power distribution network in each line as a constraint, taking the minimum cost, the minimum loss and the minimum voltage deviation as objective functions, and constructing a multi-optimization objective function; a multi-optimization objective function is solved based on improved multi-objective particle swarm optimization, and a power grid supply and demand balance regulation and control scheme is obtained; and performing supply-demand balance regulation and control on the power grid by adopting the power grid supply-demand balance regulation and control scheme. According to the method, three objectives of loss reduction, voltage deviation and minimum cost are taken as a total objective of optimization, and an optimal solution is obtained by adopting an improved multi-objective particle swarm optimization algorithm, so that the capacity of the distributed generator set can be optimized, and the electricity consumption in the peak period can be effectively managed, thereby realizing supply and demand balance of a power grid, reducing line congestion, and improving the reliability of the power grid. The power grid stability and operation efficiency are improved.