51 results about "Final energy" patented technology

The invention discloses an energy storage power station multi-time-scale optimization scheduling method and system fusing adaptive distribution robustness and model prediction control, and the method comprises the following steps: obtaining the data of an energy storage power station, and solving a hierarchical planning model containing weekly-ahead distribution robustness optimization and day-ahead adaptive distribution robustness optimization; obtaining a robust day-ahead energy storage power scheduling plan and a charge state reference trajectory; respectively taking the day-ahead energy storage power scheduling plan and the charge state reference trajectory as a center reference and a terminal constraint of a model prediction controller, and performing rolling optimization solution in each control period by using the model prediction controller to obtain a corresponding power set value; and superposing the power set value with a fine tuning power amount to generate a final energy storage system output power instruction. On the premise of ensuring the robustness of the system, the decision conservative property is obviously reduced, and the economic benefit and the execution precision of the scheduling plan are improved.

The invention belongs to the technical field of power system energy storage, and provides an energy storage planning method and system for peak clipping and valley filling of a regional power grid, and the method comprises the steps: dynamically recognizing the peak-valley time period of standardized load characteristic data through variational mode decomposition and Hilbert transformation, and calculating the peak-valley contribution degree index of each sub-region; obtaining a sub-region load distribution mode; establishing an energy storage-demand response collaborative optimization framework; optimizing site selection and capacity configuration of the energy storage system to obtain an initial energy storage planning scheme; and dynamically adjusting the initial energy storage planning scheme by adopting a near-end strategy optimization algorithm and taking the peak-valley smoothing reward function as a target to obtain a final energy storage planning scheme so as to realize real-time peak clipping and valley filling of the regional power grid. According to the technical scheme of the invention, through a technical path of'accurate identification-dynamic coupling-collaborative optimization ', multi-objective optimization which is difficult to consider by a traditional method is realized, and an innovative solution is provided for stable operation of a high-proportion renewable energy power grid.

The invention provides a charge and discharge control method and system for a liquid air energy storage ship based on a neural network, and relates to the technical field of energy storage ships, and the method comprises the steps: generating a control instruction of a corresponding link based on a final time node decision outputted by a neural network model; executing the control instruction, namely controlling the open sea liquefied air equipment to start or stop liquid air preparation at the final preparation time node; controlling the ship to start navigation at the final transportation start navigation time node; controlling the port gasification power generation device to start or stop power generation at the final energy release power generation time node; and in the process of liquid air preparation, ship transportation or port energy release power generation, monitoring the change of multi-source real-time data in real time, and inputting the changed data into the neural network model again so as to dynamically adjust and update the final time node decision. According to the method, the maximization of the comprehensive benefit of operation is realized, the value window of the peak period of the power grid can be accurately captured and responded, and low-efficiency or invalid discharge is avoided.

The invention belongs to the technical field of rail transit intelligent energy supply, particularly relates to an energy distribution method and system for a rail transit station photovoltaic and energy storage system, and aims to solve the problems of single feature learning dimension, poor multi-objective optimization balance and dynamic feedback response lag in the prior art. The method comprises the following steps: constructing a multi-subject space-time incidence matrix, carrying out feature learning by adopting a space-time double-scale attention fusion deep belief network, establishing a three-dimensional energy flow coupling tensor mining energy supply-demand relationship, constructing a multi-target optimization model based on a weighted ideal point method to generate a preliminary strategy, and carrying out multi-target optimization on the basis of the preliminary strategy; and parameters are dynamically adjusted by using an adaptive gradient feedback correction model, and a final energy distribution strategy is obtained through multiple rounds of iterative optimization. According to the method, the problems of single feature learning dimension, poor multi-objective optimization balance and dynamic feedback response lag in the prior art are solved, the energy supply and demand prediction precision is improved, and collaborative optimization of energy supply economy, environmental protection property and equipment reliability is realized.

The invention discloses an AI-based green building energy-saving evaluation management system, and relates to the field of energy-saving management, and the system comprises a region division module which is used for carrying out the partitioning of the internal space of a building according to a preset initial standard, forming an initial region range containing a plurality of power utilization function devices, and carrying out the dynamic adjustment of the division range of each region; the equipment analysis module is used for reading functional characteristics of the electric equipment in the current area, identifying an operation mode, a use scene and basic energy consumption parameters of the equipment and generating an equipment functional characteristic set; the final energy-saving strategy not only can represent the common demands of most people in the area, but also can respect the individual differences, so that the system achieves the energy-saving goal, meanwhile, the user discomfort and complaint rate caused by a forced unified strategy are reduced, the system can cope with various complicated and changeable internal and external conditions in building use, and the system has a wide application prospect. And the continuity and the stability of the energy-saving effect are ensured.

The application relates to the technical field of compressed air energy storage, and discloses an energy storage method and system for compressed air in an aquifer, which can select the most suitable aquifer for storing the excess power of a compressed air storage power plant according to the actual state of the aquifer when the excess power needs to be converted into compressed air and stored in the aquifer, ensures that the energy storage efficiency is relatively high, reduces energy loss, and reduces the cost; in addition, the suitable energy storage mode can be selected according to the actual aquifer state of the final energy storage aquifer, the energy conversion efficiency coefficient is ensured to be low, the operation cost is reduced, and the overall energy utilization efficiency is ensured.

The invention discloses a provincial power system pumped storage power station configuration method based on an NNC-MILP method, and belongs to the technical field of pumped storage power station configuration. Comprising the steps that S1, a peak regulation demand model is constructed based on provincial power system composition equipment, and the peak regulation demand model is used for obtaining a planning target annual peak regulation demand evaluation result; s2, constructing a pumped storage power station power-capacity configuration model, and constructing a dual-objective function based on the pumped storage power-capacity configuration model and the peak regulation demand model; solving the dual-objective function by adopting an NNC method to obtain a dual-objective configuration scheme; and S3, measuring and calculating the suppression effect of the power abandoning time and the power shortage time of the pumped storage power station after configuration based on the double-target configuration scheme and the return-on-investment ratio to obtain a final energy storage configuration scheme. According to the method, the power abandoning / shortage, the duration, the peak value, the power abandoning / shortage time sequence distribution, the power abandoning / shortage coverage and the alternating condition are comprehensively considered, and the annual peak regulation demand of the planning target is accurately depicted.

Ion implantation systems and methods implant varying energies of an ion beam across a workpiece in a serial single-workpiece end station, where electrodes of an acceleration / deceleration stage, bend electrode and / or energy filter control a final energy or path of the ion beam to the workpiece. The bend electrode or an energy filter can form part of the acceleration / deceleration stage or can be positioned downstream. A scanning apparatus scans the ion beam and / or the workpiece, and a power source provides varied electrical bias signals to the electrodes. A controller selectively varies the electrical bias signals concurrent with the scanning of the ion beam and / or workpiece through the ion beam based on a desired ion beam energy at the workpiece. A waveform generator can provide the variation and synchronize the electrical bias signals supplied to the acceleration / deceleration stage, bend electrode and / or energy filter.

This invention relates to the field of natural gas boiler energy dispatching technology, and discloses a method for optimizing energy dispatching through waste heat recovery from natural gas boiler flue gas. The method includes collecting flue gas parameters from the gas boiler and user-end heat demand parameters; determining the heat recovery threshold of the primary condensing waste heat recovery system and the heat exchange load range of the secondary white smoke elimination heat exchange system based on the flue gas parameters; generating a multi-energy coupled dispatching strategy by combining the operating status data of the four-pipe air source heat pump unit and environmental temperature and humidity parameters; allocating the heat supply ratio of the underfloor heating and air conditioning heating systems according to this strategy, and dynamically adjusting the operating modes of the two-stage systems; correcting the dispatching strategy based on real-time heat supply and demand deviation, and outputting the final energy dispatching command. This method can fully recover waste heat from the flue gas, achieve multi-energy coordinated dispatching and heat supply and demand matching, and avoid the "white smoke" phenomenon.

The application provides an energy storage prediction method, system and device, and relates to the technical field of electric energy storage.The energy storage prediction method comprises the following steps: obtaining a bus voltage signal in a current time period through a public connection point of a microgrid, and extracting a quantum phase perturbation sequence therefrom; identifying and separating a secondary modulation component of the bus by using an adaptive noise shaping algorithm; performing unsupervised clustering on the secondary modulation component by using a density peak clustering algorithm, obtaining a plurality of aging mode clusters, and extracting statistical features thereof; obtaining an initial residual energy evaluation value by using a variable coefficient regression submodel; performing ampere-hour integration on an instantaneous value sequence, determining an estimated covariance of a statistical mean channel, and obtaining an estimated covariance of a Kalman filtering channel through a Kalman filtering prediction correction cycle; and finally, weighting and fusing the initial residual energy evaluation value according to the estimated covariances of the two channels to obtain a final energy evaluation value.The application improves the adaptability and stability of the prediction.

The invention relates to the technical field of natural gas boiler energy scheduling, and discloses a natural gas boiler flue gas white smoke elimination waste heat recovery energy optimization scheduling method. The method comprises the steps that gas-fired boiler flue gas parameters and user side heat energy demand parameters are collected, and the recovery heat threshold value of a first-stage condensation waste heat recovery system and the heat exchange load range of a second-stage white smoke elimination heat exchange system are determined according to the flue gas parameters; a multi-energy coupling scheduling strategy is generated by combining the operation state data and the environment temperature and humidity parameters of the four-pipe air source heat pump unit; according to the strategy, the heat energy supply proportion of the floor heating system and the air conditioner heating system is distributed, and the two-stage system operation mode is dynamically adjusted; and based on the real-time heat energy supply and demand deviation correction scheduling strategy, outputting a final energy scheduling instruction. According to the method, flue gas waste heat can be fully recycled, multi-energy collaborative scheduling and heat energy supply and demand matching are achieved, and the white smoke phenomenon is avoided.

The invention discloses a method and a device for testing the radio frequency direct current conversion efficiency of a diode. The method comprises the following steps: S1, providing input power; s2, adjusting source traction and load traction; s3, adjusting the resistance value of a variable resistor for voltage division; s4, measuring the actual input power of the diode to be tested through the vector network analyzer; s5, obtaining energy efficiency conversion efficiency according to the maximum direct current output power and the actual input power; s6, increasing the input power of the signal source for inputting the diode to be tested, and returning to S2 to obtain the energy efficiency conversion efficiency of the current round; and when the energy efficiency conversion efficiency is not improved any more, the energy efficiency conversion efficiency of the current round serves as the final energy efficiency conversion efficiency. According to the invention, the input power and the load impedance can be adjusted, the conjugate of the source traction and the input impedance can be adjusted according to different input powers, the impedance of the direct current load can be dynamically adjusted through the load traction, the nonlinear response characteristics of the diode under different working conditions are accurately captured, and accurate data support is provided for the design and optimization of a microwave rectification circuit.

This invention relates to the field of carbon emission forecasting technology, providing a carbon emission forecasting method, system, computer equipment, and storage medium. The method includes: decomposing the industrial-specific final energy consumption data from the overall industry-specific final energy consumption data based on historical annual industrial end-product energy consumption data by category and type; predicting and analyzing this data to obtain the target year's overall industry-specific final energy consumption forecast data; and calculating the primary energy consumption forecast data based on the target year's overall industry-specific final energy consumption forecast data using power generation ratio prediction data obtained from historical annual electricity data evolution analysis, thereby obtaining the predicted value of regional energy consumption carbon emissions. This invention can capture the natural laws governing the impact of energy structure adjustment, technology diffusion, and resource constraints on energy demand and carbon emissions, improving the reliability and accuracy of regional energy carbon emission forecasting.

The application discloses a kind of energy supplement facilities positioning method and system. Obtain multi-modal multi-source vector data and carry out pretreatment;The spatial constraint system of demarcating energy supplement facilities and energy supplement demand distribution boundary;According to the topological structure of road network, select initial candidate site and extract characteristic attribute based on spatial constraint system, generate corresponding multidimensional space characteristic vector;Construct simulation positioning scene to extract the spatial characteristic vector of simulation demand point set, combine heuristic optimization result to generate corresponding construction value score label, obtain labeled dataset;Neural network is trained using labeled dataset, the mapping relationship of space characteristic and construction value is established, and the model parameter trained is stored as offline value evaluator;Using offline value evaluator, the initial candidate site is carried out quickly value retrieval screening, obtain high-value candidate site pool and carry out heuristic optimization, output the final energy supplement facility layout scheme.

The invention discloses a railway vehicle power optimization method based on energy routing control, which comprises the following steps: acquiring a velocity vector and an acceleration parameter from a channel allocation state, fusing the parameters through a Kalman filtering algorithm, determining an energy demand curve in a future time period, and obtaining a predicted power demand sequence; a reference value of a hierarchical scheduling frame is obtained from the power flow distribution scheme, instantaneous power waves are processed through a multi-time-scale hierarchical architecture, a control instruction sequence from the millisecond level to the minute level is determined, and a coordinated short-term scheduling result is obtained; and acquiring position information data from the long-term energy network topology, fusing the data through a real-time synchronization mechanism, determining a final energy routing control state, and obtaining a closed-loop feedback cycle of vehicle power optimization.

The invention belongs to the technical field of electric power energy storage systems, and discloses an energy storage system frequency stability evaluation and control method, system, device and medium, and the energy storage system frequency stability evaluation method comprises the steps: building a typical network construction type energy storage system frequency evaluation model based on the state variable of an energy storage system; constructing an initial frequency stability evaluation model; constructing a neural network optimization problem based on the system disturbance obtained at the preset time node and the corresponding real state; carrying out gradient solution on a neural network optimization problem by utilizing the defined evaluation adjoint, and carrying out iterative updating on model parameters at a fixed step length until convergence; and based on the final energy storage frequency stability evaluation model, in combination with the system initial state and the disturbance condition, solving by using an ordinary differential equation solver to obtain a prediction state of a target time node, and judging the frequency stability of the energy storage system according to the prediction state of the target time node. According to the method, the system frequency change is accurately predicted by using an ordinary differential equation solver.

The present application provides a kind of based on the persistent self-evolution vehicle energy management method and system of certainty working condition and uncertainty disturbance coordination, belong to vehicle energy management and intelligent control technical field, the method of the present application is with stable basic strategy as main controller, generates basic energy management control instruction according to current vehicle state;On this basis, based on current certainty working condition identification result and prior information of road ahead constructs certainty correction term, for the feedforward correction of basic control instruction in combination with current working condition difference and future working condition transition trend;While constructing uncertainty disturbance correction term, when detecting driving style mutation, traffic state fluctuation or demand distribution offset, output limited residual compensation quantity, and through amplitude constraint, change rate constraint and automatic attenuation mechanism guarantee online collaborative stability.Finally, basic energy management control instruction, certainty correction term and uncertainty disturbance correction term are fused to obtain final energy management control instruction.

The invention relates to the technical field of optoelectronic materials, in particular to a single-matrix triple-doped white fluorescent material and a preparation method thereof, the chemical composition expression of the single-matrix triple-doped white fluorescent material is M1-x-y-zAl2O4: xCe < 3 + >, yTb < 3 + > and zMn < 2 + >, three ions of Mn < 2 + >, Ce < 3 + > and Tb < 3 + > are jointly doped into an MAl2O4 (M = Mg, Ca, Sr and Ba) aluminate matrix, the doping concentrations of Ce < 3 + >, Tb < 3 + > and Mn < 2 + > are accurately regulated and controlled (0.005-0.03, 0.02-0.1 and 0.01-0.1 respectively), and the single-matrix triple-doped white fluorescent material is obtained. According to the method, Mn < 2 + > occupies specific sites in a matrix crystal structure, the peak value of an emission spectrum of the Mn < 2 + > is accurately regulated and controlled at a red light wave band of 620-700nm, and meanwhile, a three-doped system capable of realizing efficient energy cascade transfer in a single matrix is successfully constructed by virtue of efficient absorption of Ce < 3 + > on 350-420nm near ultraviolet light, energy transfer from Ce < 3 + > to Tb < 3 + > and final energy transfer from Tb < 3 + > to Mn < 2 + >. Therefore, the fundamental technical obstacle that in the prior art, it is difficult to achieve efficient synergistic luminescence in a single matrix through multi-ion combination is solved.

This invention belongs to the field of intelligent energy supply technology for rail transit, specifically involving an energy allocation method and system for photovoltaic and energy storage systems in rail transit stations. It aims to solve the problems of existing technologies, such as single feature learning dimensions, poor balance in multi-objective optimization, and lagging dynamic feedback response. The invention includes: constructing a multi-agent spatiotemporal correlation matrix; employing a deep belief network with spatiotemporal dual-scale attention fusion for feature learning; establishing a three-dimensional energy flow coupled tensor to mine energy supply and demand relationships; constructing a multi-objective optimization model based on the weighted ideal point method to generate a preliminary strategy; and using adaptive gradient feedback to correct the model and dynamically adjust parameters. Through multiple rounds of iterative optimization, the final energy allocation strategy is obtained. This invention solves the problems of single feature learning dimensions, poor balance in multi-objective optimization, and lagging dynamic feedback response in existing technologies, improving the accuracy of energy supply and demand prediction, and achieving synergistic optimization of energy economy, environmental protection, and equipment reliability.

The invention discloses a cleaning method for a semiconductor photoetching mask substrate, and belongs to the technical field of semiconductor manufacturing. According to the method, dynamic cleaning logic of step cooperation and energy matching is adopted, and the steps of interface wetting and pre-cleaning, chemical soft interface construction, low-damage mechanical stripping and synchronous washing, targeted reaction clearing, microstructure deep cleaning, interface dehydration and modification and final energy-assisted drying are sequentially executed. The method is characterized in that through the dependency relationship of chemical interface softening and low-damage mechanical stripping in sequence, the rotation speed of a substrate in each step is strictly controlled to meet the specific dynamic relationship (V2 < V1 < V4 < V3 < V5 < V6), and precise cooperation of chemical action, physical stripping and energy field cleaning is achieved. The contradiction between efficiency and substrate damage in a traditional cleaning process is effectively solved, full-scale pollutants from micron-scale particles to nano-scale molecular contamination can be removed without dead corners, and a clean, stable and secondary pollution resistant surface state is actively reconstructed at a cleaning end point; and the cleanliness and the service life of the mask substrate and the yield of a downstream photoetching process are remarkably improved.

The invention discloses a multi-time scale energy scheduling method and system based on electricity price prediction. The method comprises the steps of obtaining electricity price prediction values on multiple time scales in the future; generating an energy scheduling strategy on multiple time scales; executing an energy scheduling strategy on multiple time scales, monitoring an actual electricity price in real time, and comparing the actual electricity price with a corresponding electricity price prediction value to determine a prediction error; judging whether the prediction error is greater than an error threshold; in response to the fact that the prediction error is larger than an error threshold value, updating parameters of the electricity price prediction model according to the prediction error, and returning to the step of obtaining electricity price prediction values on multiple time scales in the future; and taking the current energy scheduling strategy on the multi-time scale as a final energy scheduling strategy on the multi-time scale in response to the fact that the prediction error is not greater than the error threshold value. By using the scheme of the invention, the electricity price trend in the short period, the middle period and even the long period in the future can be pre-judged, and a solid foundation is provided for formulating a comprehensive and fine energy scheduling strategy.

The invention relates to the technical field of energy management, and discloses an optical storage and charging integrated system energy optimization management method and system. The method comprises the steps that output data and user load demand data of a renewable energy power generation system are collected in real time, output sudden change characteristics are recognized, and a dynamic coupling relation mode between the power generation system and load demands is established; acquiring a sudden interruption signal and abnormal information based on the mode, and if the sudden change of the output exceeds a preset threshold value, judging that the type is a power generation related interruption type; for the interruption type, an optimization algorithm is adopted to adjust emergency discharge parameters of the energy storage system, an emergency discharge sequence is generated, and according to the load distribution state of the current charging facility, if the matching degree is low, the load distribution proportion is adjusted; by processing the energy transmission data, generating the real-time energy scheduling scheme and verifying the matching degree of the real-time energy scheduling scheme and the interruption scene, if the matching degree is high, the final energy scheduling path is determined, and the energy utilization efficiency of the optical storage and charging integrated system is improved.

The invention relates to the technical field of power system energy efficiency, and discloses a renewable energy power generation side power system energy efficiency optimization method. The method comprises the following steps: acquiring renewable energy power generation output data, load demand data and system stability parameters in different operation scenes; for each operation scene, determining an associated reference operation scene group according to the load demand of the operation scene, and calculating the energy efficiency influence degree of each evaluation time point; integrating the energy efficiency and load distribution data of all the operation scenes, calculating a preliminary energy efficiency correlation index, and performing correction in combination with the energy efficiency influence degree and the stability parameter to obtain a final energy efficiency correlation index; non-energy efficiency factor weights of all time points are deduced according to load requirements and stability parameter distribution; identifying a core evaluation time point from all evaluation time points according to the final energy efficiency association index and the non-energy efficiency factor weight; and establishing an energy efficiency optimization model by using the energy efficiency data and the stability parameters of the core point, and adjusting the operation state of the power system.

The invention provides a distributed energy storage multi-target power control method and device. The method comprises the following steps: acquiring real-time power, wherein the real-time power comprises power grid side power at the current moment, energy storage power at the current moment and scheduling plan power at the current moment; based on the real-time power, the current power grid side target power is adjusted by executing a control strategy; based on the current power grid side target power and the real-time power, the energy storage power at the next moment is calculated in combination with a closed-loop increment damping adjustment algorithm; and correcting the energy storage power at the next moment based on constraint conditions to obtain final energy storage power, wherein the constraint conditions comprise a one-way threshold logic constraint, a physical boundary constraint and a battery electric quantity protection constraint. According to the distributed energy storage multi-target power control method and device provided by the invention, the problem of power oscillation caused by sampling time delay in an existing energy management system and the problem of strategy conflict in a multifunctional scene can be solved.

This invention discloses a method for generating energy storage configuration principles based on LLM and time-series production simulation, belonging to the field of power system planning. This method uses a closed-loop "simulation-analysis-iteration" process to extract interpretable and reusable configuration principles from massive scenarios and configuration schemes. An AI agent globally schedules these principles: based on system topology and operating scenarios, it automatically invokes year-round time-series production simulations to simulate different combinations of energy storage capacity, power, placement location, and control strategies, obtaining key operating indicators. The simulation results are input into the LLM to obtain the optimal energy storage configuration principles, which are then verified and corrected by the AI ​​agent based on effectiveness indicators, thus outputting the final energy storage configuration principles. Furthermore, the year-round simulation employs time-dimensional decomposition, breaking down the overall model into multiple sub-optimization problems, alleviating the difficulty of solving problems caused by excessively large integer variables, and improving computational efficiency and engineering usability.

The application provides a carbon emission analysis method and device, electronic equipment and storage medium, and relates to the technical field of carbon emission data processing. The method comprises the following steps: acquiring multi-source real-time data and energy consumption data of each energy consumption node in the park within a current time period, and extracting multi-dimensional key features; determining initial energy structure data; according to the correlation relationship between the energy consumption node and other energy consumption nodes in the park, the initial energy structure data is corrected to obtain final energy structure data; then the dynamic emission factor matrix of the energy consumption node is determined; the carbon emission of the energy consumption node is simulated through a hybrid model to obtain the carbon emission result of the energy consumption node; and then the dynamic analysis and interpretation of the carbon emission are performed through an attention mechanism to obtain the carbon emission analysis result of the park within the current time period. The application realizes high-precision dynamic analysis of the carbon emission of the park.

The invention relates to the technical field of smart energy management and power system automation control, in particular to an optical storage and charging system energy scheduling optimization method, which comprises the following steps: step 1, acquiring real-time state data of a heterogeneous energy storage medium in an optical storage and charging system and load demand data of a heavy truck charging station to construct a load demand vector at the current moment; the heterogeneous energy storage medium comprises a power type energy storage unit and an energy type energy storage unit; 2, presetting a power response threshold value, and constructing an initial energy distribution instruction sequence; step 3, collecting historical cycle times, charge and discharge depths and current health states of the power type energy storage unit and the energy type energy storage unit, and correcting the initial energy distribution instruction sequence to obtain a final energy scheduling strategy so as to output a control instruction to the edge computing node and the cloud platform; the overall response speed and the voltage stability of the system are improved.

The invention provides an off-grid scene railway energy optimization scheduling method based on a double-layer optimization model, and relates to the technical field of electrified railway energy management, and the method comprises the steps: obtaining wind and light natural resource data and traction load data; generating photovoltaic and fan maximum output time sequence data; outputting a wind and light real-time output instruction, an energy storage real-time output instruction and wind and light abandoning power data by taking the minimum total operation cost as a target through an upper layer optimization model; outputting a system elasticity index by taking maximum system elasticity as a target through a lower-layer optimization model; and according to the index, correcting the total operation cost and generating a final energy scheduling instruction, and completing optimal scheduling. The upper-layer model contains economical efficiency targets and constraints, and the lower-layer model introduces multi-dimensional elastic indexes, so that collaborative optimization of economical efficiency and reliability is realized. According to the invention, the operation economy and the power supply reliability of the off-grid railway energy system are effectively improved.

The embodiment of the application discloses a kind of rubber production factory energy consumption prediction method, equipment and medium, belong to energy consumption prediction technical field, solve the problem of rubber production factory energy consumption monitoring lag.To be measured rubber production factory thermal imaging data is fused to obtain the space heat data corresponding to the rubber production factory to be measured;The historical energy consumption data set is divided and compared, and the abnormal historical data is screened out to determine the reference energy consumption prediction data based on the historical data after screening;According to the building distribution data in the rubber production factory to be measured, the equipment distribution data in the rubber production factory to be measured and the space heat data, the energy consumption influence relationship diagram is constructed;Based on the preset energy consumption influence factor database and the energy consumption influence relationship diagram, the final energy consumption influence factor is determined;The reference energy consumption prediction data is adjusted based on the final energy consumption influence factor to obtain the final energy consumption prediction data.

The invention relates to the technical field of smart home and energy management, and discloses a smart home load management method and system based on a micro energy storage system, and the method comprises the steps: obtaining energy consumption data and environmental factor data, and carrying out the preprocessing, and obtaining a clean energy consumption set; performing grouping processing and feature extraction according to the clean energy consumption set to obtain an equipment operation state classification result; according to the equipment operation state classification result, performing user behavior preference association analysis, and determining a target idle equipment list; performing priority screening verification according to the target idle equipment list to obtain an idle equipment data set; performing energy-saving strategy parameter adjustment and strategy execution conflict detection according to the idle equipment data set to obtain a temporary configuration scheme; and performing matching degree verification according to the temporary configuration scheme, and determining a final energy-saving management scheme. The method can solve the technical problems of rigid energy-saving management strategy and low energy utilization efficiency in the prior art.