1651 results about "Electric consumption" patented technology

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 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 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 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 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 relates to a time-phased electricity consumption curve prediction method based on a CNN + LSTM + AM depth model. The prediction method comprises the following steps: data collection and arrangement; extracting features; model training; inputting new data into the trained model, and predicting the electricity consumption in the future time range; according to the method, the convolutional neural network CNN in deep learning is used for feature extraction, the long-term and short-term memory network LSTM is used for capturing the long-term dependency relationship of time sequence data, and the attention mechanism AM is used for enhancing the attention of the model to key information, so that the accurate prediction of the electricity consumption curve is realized.

The invention relates to the technical field of vehicle control, and discloses a vehicle control method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a vehicle driving scene corresponding to a vehicle in a remaining driving road section; if the vehicle driving scene is the preset driving scene, the required electricity consumption of the vehicle in the remaining driving road section and the current remaining electricity quantity of the battery pack are obtained; if the electric quantity difference value between the current remaining electric quantity and the required electric quantity is smaller than a preset electric quantity value, an initial driving power parameter and a power limiting parameter of a driving motor of the vehicle are obtained, and the initial driving power parameter is corrected through the power limiting parameter to obtain a target driving power parameter; and controlling the driving motor to work according to the target driving power parameter. Therefore, the target power corresponding to the target driving power parameter obtained based on the power limiting parameter is not too large, the power consumption and the oil consumption of the vehicle can be reduced, and the specific requirements of a user for the power consumption and the oil consumption in the preset driving scene are met.

The invention discloses a data center air conditioner control method and an air conditioner control system, and relates to the technical field of air conditioner control. The method comprises the steps that the real-time electricity consumption of each array cabinet is obtained, the total electricity consumption of a machine room is determined, and the load power of the machine room is determined based on the total electricity consumption; determining the total cooling capacity output of the air conditioner according to the load variable quantity of the load power and a temperature set value in a preset period; and on the basis of the total cooling capacity output, operation parameters of the air conditioner are determined, and the air conditioner is controlled to operate according to the operation parameters. According to the method, the power utilization data of the machine room is converted into the heat representation, so that rapid and accurate heat management is realized, the air conditioning system of the data center can adjust cold supply in real time according to the actual load of the machine room, and the instant cold supply requirement of the data center is met.

The invention belongs to the technical field of data center refrigeration, and discloses a data center electric power-heating power scheduling method combined with building virtual energy storage, and the method comprises the following steps: obtaining the historical electricity consumption, refrigerating capacity, indoor temperature and other data of a data center; according to the correlation between the electrical load and the refrigerating capacity, the future refrigeration demand is predicted by predicting the electricity consumption; the refrigeration demand and the electricity price change are judged, the refrigerating capacity is adjusted in advance through building virtual energy storage and the peak-valley electricity price difference, timely response of the indoor temperature of the data center to the heat dissipation demand change is achieved, energy waste is avoided, and the system stability is improved. From two aspects of electric power and heating power, the electric power-heating power collaborative optimal scheduling method is constructed by taking the lowest operation cost as an objective function and taking building thermal inertia, indoor temperature range, heat dissipating capacity and the like as constraint conditions. According to the method, the system stability is improved, and the operation cost is remarkably reduced.

The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.

The invention relates to the technical field of intelligent scheduling of electric quantity, in particular to an intelligent scheduling method and system for a distributed photovoltaic energy storage system based on an AI technology. The method comprises the following steps: S1, collecting multi-modal data of a distributed photovoltaic system; s2, using a long-short-term memory network model to predict future generating capacity and power consumption based on the multi-modal data; s3, making a preliminary charging and discharging strategy of the energy storage system based on the prediction result; s4, optimizing the preliminary strategy by adopting a reinforcement learning method; and S5, executing the optimized strategy, and carrying out real-time monitoring to detect an abnormal condition. The intelligent scheduling method and system can accurately predict the power generation amount of a photovoltaic system and the power consumption of a user side by integrating a long-short-term memory network model and a reinforcement learning algorithm, and meanwhile, the influence of an aging attenuation model of a photovoltaic panel and external environment factors (such as altitude and cloud layer shielding) on the power generation efficiency is considered.

The invention relates to the technical field of power grid side energy storage, in particular to a power grid side flywheel energy storage stationing method considering source grid requirements, and aims to solve the problem of how to improve the adjusting effect of power grid side shared energy storage in the peak period of power utilization according to the source grid requirements. The invention provides a power grid side flywheel energy storage point distribution method considering a source network demand. The point distribution method comprises the following steps: determining pre-site selection data of power grid side shared energy storage according to an energy storage demand of a target area and a topological structure of cable equipment in the target area; acquiring cable equipment corresponding to the corrected site selection data, and calculating an energy storage demand corresponding to the corrected site selection data according to historical electricity consumption of the cable equipment; and obtaining a power supply unit in the target area, planning all the corrected site selection data according to the power supply capability and the energy storage demand of the power supply unit, and obtaining the actual distribution point of the power grid side shared energy storage.

The invention discloses an energy storage user identification method, system and device based on power grid energy consumption data. The method comprises the steps of obtaining daily power consumption time sequence data of a target user; obtaining daily peak electricity consumption time sequence data and daily valley electricity consumption time sequence data according to the daily electricity consumption time sequence data; judging whether a date with negative daily peak electricity consumption exists in the daily peak electricity consumption time sequence data or not; if the date that the peak electricity consumption is negative exists, the target user is an energy storage user; if the date with the negative peak electricity consumption does not exist, daily valley electricity consumption data corresponding to a first date is obtained, and the first date is configured to be the date with the daily peak electricity consumption larger than a preset first threshold value; judging whether the dispersion degree of the daily valley electricity consumption data corresponding to the first date accords with a preset index or not; and judging whether the target user is an energy storage user. The problems that at present, an intelligent energy storage user identification method is lacked, and accurate identification is difficult to carry out are at least solved.

The invention relates to the field of power dispatching control, in particular to a virtual power plant power supply optimization control method based on supply-demand balance. The method comprises the following steps: firstly, obtaining the generating capacity and the power consumption of different power supply areas in each historical period, obtaining the environmental data of different environmental indexes in each historical period, obtaining the power supply priority of the power supply areas according to the difference between the predicted generating capacity and the predicted power consumption of a target power supply area, and selecting the supply-demand imbalance time period of the power supply areas; analyzing the correlation between the generating capacity and the environmental data of the power supply area in the supply-demand imbalance time period, obtaining the environmental influence sensitivity of the supply-demand imbalance time period, and obtaining the schedulable potential value of the power supply area in combination with the environmental influence sensitivity and length of each supply-demand imbalance time period of the power supply area and the power supply priority; and the power supply of each power supply area is optimally controlled. The method can improve the power supply optimization control effect of the virtual power plant, and avoids the imbalance of power supply and demand.

The invention discloses a centralized distributed comprehensive demand response method and system for a comprehensive energy system, and the method comprises the steps: obtaining the planned power consumption of each park, generating noise disturbance based on a differential privacy mechanism, and determining the response amount of each park through the peak regulation demand of a power grid and the planned power consumption after the noise disturbance is increased; taking a difference value between the planned power consumption of each park and the response quantity as a power consumption reference value of each park participating in response; according to the difference value between the electricity utilization reference value of each park participating in response and the actual electricity utilization amount of each park, the reward and punishment cost of each park obtained from the integrated energy park operator is determined; taking the response quantity corresponding to the minimum value of the difference value between the operation cost and the reward and punishment cost of each park as the optimal response quantity of each park; the minimum error between the optimal response amount and the response amount of each park and the maximum income of the integrated energy park operator are taken as optimization objectives, and the demand response amount instruction of each park is determined, so that the problems of privacy disclosure, low participation intention and heavy calculation burden of the industrial park in the existing demand response mechanism are solved.

The invention discloses a data feature analysis method and system for an intelligent electric meter, and relates to the technical field of data processing analysis, and the method comprises the steps: collecting daily electricity consumption data of all electricity users in a target region in a preset time period, and carrying out the preprocessing, and obtaining a corresponding daily electricity consumption time sequence and a daily electricity consumption curve; calculating the similarity between the adjacent daily electricity consumption curves of each user, and obtaining the stability characteristic value of the electricity consumption change of the user; calculating an optimal Euclidean distance between the daily electricity consumption time sequences of different users in combination with the stability characteristic values of the electricity consumption changes of the users; clustering the users in the target area based on the optimal Euclidean distance; analyzing the power consumption behavior of the user based on the clustering result; by measuring the stability of the power consumption behavior of the user, the problem of clustering deviation caused by neglecting time sequence fluctuation in a traditional method is solved, and the clustering accuracy is improved, so that the precision of power consumption behavior analysis and the reliability of anomaly detection are effectively improved.

The invention provides a building energy-saving potential assessment method and system based on an energy consumption quota, and the method comprises the steps: carrying out the energy consumption analysis of a to-be-assessed building through a clustering analysis algorithm and a regression analysis algorithm according to the energy consumption multi-factor data of the to-be-assessed building, and obtaining the energy consumption quota data and the carbon emission quota data; based on the energy consumption multi-factor data and the energy consumption quota data, performing power consumption prediction on the to-be-evaluated building by using a multiple linear regression algorithm to obtain power consumption prediction data; calculating target energy efficiency data according to the electricity consumption prediction data; according to the energy consumption quota data, the carbon emission quota data and the target energy efficiency data, performing energy-saving potential assessment on the to-be-assessed building to obtain a comprehensive potential score of the to-be-assessed building; according to the method, the energy-saving potential of the building is evaluated by combining the energy consumption quota data, the carbon emission quota data and the power consumption prediction data obtained by using the multiple linear regression algorithm, the actual energy consumption of the building can be comprehensively reflected, and thus the potential energy-saving opportunity of the building can be identified more accurately.

The invention discloses a smart city power consumption data statistical planning analysis method and system, and belongs to the technical field of energy systems. According to the method, electricity consumption data of a smart city in set time are collected, electricity consumption data of one smart city in set time are collected, the collection period is 24 hours, data are loaded through functions in the pandas, the number and accuracy of the data can meet the requirements, and then training of subsequent data on a model is facilitated; by analyzing the rule of data changing along with time and analyzing time sequence data distribution, a corresponding mathematical model is established to analyze and obtain a future electricity consumption prediction result of the smart city, statistics and operation of future electricity consumption in operation of the smart city are achieved, and allocation of power generation resources such as coal based on budget of the future electricity consumption is facilitated.

The invention belongs to the technical field of electric energy meter metering correction, and provides a two-way metering correction method and system for an electric energy meter, and the method comprises the following steps: obtaining the power generation amount of distributed energy in historical data and the power consumption of a user, recognizing a continuous time period in which the power generation amount and the power consumption have a significant difference as a mismatch high-incidence time period, and determining the mismatch high-incidence time period; performing selection analysis on the mismatch high-incidence time period, and determining a time period division rule for prediction analysis; and performing time interval division based on the determined time interval division rule to obtain an analysis time interval. By analyzing the metering abnormal proportions of different types of key mismatch time period groups, screening out high-proportion class groups, predicting the occurrence trend and time of the high-proportion class groups by using a neural network model, and performing electric energy metering correction compensation, the occurrence of the key mismatch time periods can be predicted in advance, and correction compensation is performed based on a historical metering deviation rate mean value. Settlement errors caused by inaccurate metering of the electric energy meter can be effectively reduced.

The invention belongs to the technical field of distributed power supply, and particularly relates to a prediction method for port power load coupling photovoltaic power generation capacity, which comprises the following steps of: integrating two groups of time sequence sequences of port photovoltaic power generation capacity and port power consumption into a composite time sequence according to a causal relationship, and training and learning through multi-layer neural networks such as TCN, GRU and an attention layer; the prediction value in the future 24 hours is obtained, the process that a training model must obtain associated features through spontaneous learning of a large amount of original random data is reduced, the burden on the calculation power of the model is reduced, and the calculation efficiency and the accuracy of the prediction result are improved. The invention further provides a non-transient readable recording medium storing the program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.