2536 results about "Energy management system" patented technology

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

The invention provides a photovoltaic power station photovoltaic power prediction method and system. A cloud layer motion time sequence library is constructed based on a historical cloud picture, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and the aerosol density in real time, calculates the light intensity attenuation gradient of the shadow area in combination with the model, and predicts the remaining time of the cloud cluster reaching the core area. Dividing a power prediction confidence interval according to remaining time, inputting satellite data into a multi-scale meteorological fusion architecture, and adjusting a meteorological feature extraction path of an attention mechanism based on cloud density. And fusing the surface reflectance correction value calculated by the scattering data of the light sensor to generate a short-term power prediction sequence of the sudden change cloud layer scene. And inputting the sequence into an energy management system, and outputting an active and reactive double-target scheduling instruction in combination with a grid-connected point voltage deviation and a reactive compensation parameter constrained by a power grid rule. According to the technical scheme provided by the invention, the cooperative scheduling precision of the photovoltaic power station in the cloud abrupt change scene is improved.

The invention relates to the technical field of data processing, in particular to a building light storage and charging integrated energy management system, which is used for solving the problems of data processing delay and resource coordination mismatch caused by insufficient multi-source heterogeneous data fusion in photovoltaic power generation, energy storage and grid connection links. The system comprises a data acquisition module, a protocol conversion module, a data fusion module, a scheduling optimization module, an instruction generation module and a feedback module. The protocol conversion module carries out Modbus, CAN bus and IEC 61850 protocol analysis on the photovoltaic output time sequence data, the energy storage charging and discharging state data and the power grid dispatching instruction to generate standardized data streams. And the safety verification module intercepts the overrun instruction and triggers an amplitude limiting strategy. Through protocol unification, dynamic fusion and closed-loop control, the system realizes improvement of photovoltaic consumption rate, reduction of energy storage circulation loss and optimization of power grid frequency modulation deviation, and significantly improves the efficiency and economy of light storage and charging cooperative operation.

A system and method for directing energy use within a facility comprising charging stations for charging a fleet of electric vehicles, using an energy optimisation engine. The energy optimisation engine receives energy supply characteristics and facility operational energy consumption characteristic of energy assets associated with the facility over a first time period, and electrical vehicle operational requirements for the electric vehicles during a second period; computes energy distribution instructions for the first time period and satisfying a designated energy condition corresponding at least in part with the energy supply characteristics; and directs operation of the charge stations or the energy assets in accordance with the energy distribution instructions.

The invention discloses an optical storage and charging integrated micro-grid energy management system, which relates to the related technical field of micro-grids and comprises an electric energy collection layer, a digital twinborn layer, a collaborative optimization layer, an application execution layer, a photovoltaic power generation unit, an energy storage unit, a charging load unit and a distributed measurement and control terminal. The invention further discloses an energy management method of the photovoltaic, storage and charging integrated micro-grid. The energy management method comprises the steps of data acquisition, digital twin modeling, multi-target strategy generation and optimization, strategy evaluation and screening, strategy issuing and execution and closed-loop feedback and dynamic correction. According to the invention, through short-term prediction of a digital twinborn layer and real-time generation of a charging and discharging strategy by a collaborative optimization layer, fluctuating renewable energy sources are preferentially consumed, the light abandoning rate is reduced, energy storage charging is automatically triggered in an illumination peak period, and power overflow is avoided; based on the simulation result of the digital twinborn model, the power distribution of energy storage and load is dynamically adjusted, so that the photovoltaic utilization rate is improved, and the dependence on a traditional power grid is reduced.

The invention relates to a distributed energy management system for prolonging the cycle life of a lithium battery. The system comprises an acquisition unit used for acquiring battery state characteristic data of single batteries in a lithium battery pack; the identification unit is used for carrying out charge and discharge abnormal mode identification on the battery state characteristic data to obtain charge and discharge abnormal event data; the decomposition unit is used for performing hierarchical characteristic decomposition and multi-dimensional fusion based on the charging and discharging abnormal event data to obtain a cycle life attenuation characteristic matrix; and the equalization control unit is used for carrying out life equalization control based on the cycle life attenuation characteristic matrix to obtain a target charge and discharge coefficient. According to the invention, the local over-charge and over-discharge phenomena are effectively inhibited, the service life loss of the battery pack is reduced, and the energy balance adjustment of the multi-node control unit is realized.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

The invention discloses a multi-source heterogeneous data fusion and processing method based on a localized LLM model, and relates to the technical field of factory energy management.The method comprises the steps that multi-source heterogeneous data is collected from a factory energy management system, and the collected original data is preprocessed and standardized; performing feature selection, feature extraction and feature combination on the processed data; carrying out embedding dimensionality reduction by adopting principal component analysis and a localized LLM model; performing intelligent semantic analysis and fusion on the dimension reduction feature data by utilizing a localized LLM model, performing weighted fusion on embedded vectors by adopting a multi-head attention mechanism, and extracting semantic association among the data; and performing intelligent anomaly detection on the fused high-quality data by using localized LLM model embedding analysis and cosine similarity calculation. According to the method, the processing efficiency and quality of the multi-source heterogeneous data are comprehensively improved, high-quality data support is provided for comprehensive energy management of factories, and efficient and intelligent energy scheduling and management optimization are achieved.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

An energy storage apparatus can comprise a battery enclosed within a housing and an ultracapacitor directly electrically coupled with the battery via one or more high voltage lines. The ultracapacitor can form a single integrated unit with the battery within the housing. The ultracapacitor can have a voltage capacity at least as large as the battery. The ultracapacitor can provide energy to the battery via the one or more high voltage lines to charge the battery. The ultracapacitor can have a greater power density and a smaller energy density than the battery. The ultracapacitor can removably electrically couple with a 110V or 220V outlet to receive energy originating from a utility grid. The battery can receive energy from the ultracapacitor when a voltage of the battery is below a threshold and can resist receiving energy from the ultracapacitor when the voltage of the battery is above a threshold.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention relates to the technical field of electric power peak regulation of energy storage equipment, in particular to a dispatching energy management system for assisting electric power peak regulation, which comprises a load prediction unit, a dynamic adjustment unit and a model optimization unit. A dynamic adjustment unit monitors multiple parameters of an energy storage equipment power system through principal component analysis dimensionality reduction, a peak regulation threshold value of the energy storage equipment is dynamically adjusted according to working conditions and load prediction in combination with a fuzzy logic reasoning model, and a model optimization unit analyzes operation data, compares deviation, adjusts fuzzy rules and evaluates the peak regulation effect of the energy storage equipment. The method can flexibly adjust the threshold according to the system operation parameters and the load prediction, improves the peak regulation accuracy of the energy storage equipment, guarantees the stable operation of a power grid and the efficient utilization of energy, further optimizes the peak regulation effect of the energy storage equipment through the feedback improvement rule, and solves the problem of insufficient adaptability of the peak regulation strategy of the existing energy storage equipment.

The invention relates to the technical field of ship energy management, particularly discloses an energy collaborative management system and method based on an energy storage battery, and is used for solving the problems that when an existing ship energy management system integrates energy storage battery information, it is difficult to accurately evaluate actual energy consumption requirements, and consequently energy waste and equipment overload risks are caused. The system comprises an energy storage system unit, a system monitoring unit, a data processing unit and an energy management system unit, and is characterized in that the system operation state is determined according to real-time data monitored by the system monitoring unit; by introducing an adaptive learning rate adjustment formula, dynamically adjusting a weight factor, optimizing an energy efficiency function, adjusting output power of an energy storage battery and a generator in real time, optimizing energy distribution, ensuring stable power supply, reducing overload of the generator and energy waste, improving stability and reliability of a ship electric power system especially in a complex navigation environment, and reducing energy consumption. The service life of equipment is prolonged and the overall operation cost of ships is reduced.

The invention discloses a micro-grid anti-countercurrent control method and system based on multi-objective optimization, and the method comprises the steps: generating a dynamic load prediction result according to the power grid side voltage, user side load power and energy storage system charge state data collected by an energy management system in real time; based on the dynamic load prediction result, obtaining a countercurrent risk quantitative index; generating a corresponding dynamic anti-reflux threshold curve according to the reflux risk quantitative index; the dynamic anti-reflux threshold curve is corrected through a real-time collaborative verification module, and an anti-disturbance optimization threshold is generated; and converting the anti-disturbance optimization threshold value into a PCS power regulation and control instruction, synchronously displaying a threshold value execution state and reverse feed early warning information on an energy management interface, and triggering adaptive power clamping protection when detecting that the voltage is out of limit or the power is reverse. According to the embodiment of the invention, the intelligent level of countercurrent prevention and control of the micro-grid can be improved, and safe and stable operation of the grid is guaranteed.

The invention discloses an energy management system data filling method and architecture based on time sequence analysis, and the method achieves the intelligent prediction and filling of a data missing segment in an energy management system through constructing a filling model based on a naive Bayes algorithm, and improves the data integrity and continuity. The system firstly dynamically manages historical data and new data through a sliding window mechanism to ensure that the model can fully consider the change trend of time sequence data in the filling process. The filling model combines the distribution characteristics of historical data and current data during initialization, and endows the data in different time periods with weights, so that the model can adapt to the equipment state and environment change. And after the data missing segment is detected, the system immediately calls the filling model to carry out prediction filling, replaces the original missing data with the result and writes the result into the database, so that the accuracy of subsequent analysis and anomaly detection is ensured.

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

The present disclosure provides a dispatch method of an energy management system for energy storage and a photovoltaic system for industrial and commercial use. The dispatch method includes: determining whether a third-party control system issues dispatch instructions, and initiating a third-party dispatching mode in response to determining that the third-party control system issues dispatch instructions; determining whether the cloud terminal issues dispatch curves, and initiating an active dispatching mode in response to determining that the cloud terminal issues dispatch curves; and initiating a passive dispatching mode in response to determining that the third-party control system does not issue any dispatch instruction and the cloud terminal does not issue any dispatch curve.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention discloses a gas generator and energy storage battery hybrid energy supply scheduling strategy, belongs to the technical field of storage and charging, and mainly solves the problems that in an energy supply mode of a gas generator in existing oil and gas drilling operation, the power generation efficiency of the gas generator is low, and the gas generator is prone to flameout when coping with an impact load. Measuring and acquiring data of voltage and current of oil and gas drilling operation energy consumption equipment, the starting number of gas generators and the residual electric quantity of an energy storage battery; 2, calculating the real-time total power, the power abrupt change rate and the load rate of the gas generator according to the measured data; 3, the number of gas generators needing to be started and the charging and discharging state of energy storage electricity are determined according to the calculation and analysis result; and 4, repeating the steps 1-3 until the drilling operation is finished. The scheduling strategy depends on an existing energy management system, the starting number of the gas generators and the charging and discharging states of the energy storage batteries are reasonably controlled by combining the total power fluctuation characteristics of oil and gas drilling operation, comprehensive management of hybrid energy supply of the gas generators and the energy storage batteries in the oil and gas drilling operation is achieved, the power generation efficiency of the gas generators is improved, and the energy consumption is reduced. Meanwhile, flameout of the gas generator is avoided, and operation safety is guaranteed.

Embodiments of the disclosed systems and methods provide allow for dynamic management of electrical systems using an intelligent energy management system. Consistent with embodiments disclosed herein, an intelligent energy management system may allow for the dynamic connection of electrical sources and / or loads in accordance with applicable operating rules. Operating rules may be articulated and enforced by the intelligent energy management system that provide for event and / or state driven of an electrical system and / or associated sources and / or loads. Among other things, embodiments of the disclosed intelligent energy management system may support relatively flexible connection of electrical sources and loads, bidirectional energy usage to allow for energy to be both consumed from and fed back to a utility grid, improved battery storage and / or management, proactive energy source switching and predictive battery discharge management, and / or the like.

The invention provides a hydrogen fuel cell hybrid power unmanned aerial vehicle energy adaptive optimization system and method. The system and method aim at achieving adaptive optimization when multiple uncertain factors exist in the flight process of an unmanned aerial vehicle. Through mechanical analysis of the typical flight profile of the unmanned aerial vehicle, an estimation model of load demand power in each stage in the flight process of the unmanned aerial vehicle is constructed. The core of the method is that the flight process of the unmanned aerial vehicle is modeled as a Markov decision process, and a self-adaptive optimization model composed of a power energy consumption state space, a power supply power distribution action space, a reward function, an electric energy supply constraint condition and the like is reasonably constructed. And for an electric energy distribution reward function, a near-end strategy optimization algorithm is applied to train an intelligent agent to continuously update an actor network and iterative learning to approach an expected control strategy, so that self-adaptive intelligent optimal distribution of the hydrogen energy and the electric energy by the hydrogen fuel hybrid power unmanned aerial vehicle energy management system is realized, and a system implementation scheme is provided.

The invention discloses an optimization method for a new energy ship energy management system, and the method comprises the steps: S1, obtaining a priority assumption condition related to the energy use of a new energy ship according to a pre-created new energy ship system; s2, constructing a ship energy management mathematical model about a new energy ship system based on priority hypothesis conditions; s3, according to the constructed ship energy management mathematical model, obtaining an objective function model and optimization constraint conditions of ship energy for quantitatively evaluating advantages and disadvantages of different energy use schemes; s4, ship operation energy consumption and historical navigation data are collected and obtained; s5, according to the historical data, obtaining a neural network prediction model for pre-estimating the photovoltaic generating capacity and the ship navigation power demand based on weather prediction data; and S6, according to the neural network prediction model, obtaining estimated data of the photovoltaic power generation amount and the ship navigation power demand, and based on the target function model of the ship energy and the optimization constraint condition, obtaining an optimal energy use scheme of a corresponding new energy ship energy management system according to an optimization algorithm. The problems that an existing ship energy management system and a calculation method thereof have remarkable defects in the aspects of energy efficiency optimization, multi-energy coordination, dynamic response, operation simplicity and convenience, intelligent management, safety, new energy compatibility, comprehensive optimization strategies and data processing capacity, and complex requirements and environmental protection requirements of modern shipping are difficult to meet are solved.

The invention relates to the technical field of battery charging and discharging optimization, and discloses an energy management system data analysis method, device and equipment and a storage medium, and the method comprises the steps: collecting a working condition feature data set of a robot energy storage battery through an energy management system; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, accurate prediction of battery performance is further achieved, and stable operation of an energy management system is ensured.

Systems and methods for testing a power plant emulation are disclosed. Software for an energy management system is deployed to a computer system comprising computing devices. A model of a power plant site is deployed to a test framework. Test commands and / or use cases are injected, via the test framework to public energy management system end points. The test commands and / or use cases are used to emulate and test the model of at least the portion of the power plant site and to generate simulated operating parameter values. The generated simulated operating parameter values of the model are compared with expected values. A determination is made as to whether the model passed testing using the comparison. After determining the model passed testing, software, corresponding to at least a portion of the model, is made available for deployment to operate a physical version of the power plant site.

The invention discloses a power distribution method and system based on fuel cell multi-stack health state difference, and belongs to the technical field of fuel cell application. The specific method comprises the following steps: monitoring operation parameters of a plurality of fuel cell stacks through a sensor; obtaining the health state of each fuel cell stack and the health state of the lithium battery; establishing a hydrogen consumption model, a fuel cell SOH loss model, a multi-stack SOH difference equivalent loss model, a lithium battery SOH loss model and a lithium battery start and end SOC loss model; the calculation loss is converted into equivalent hydrogen loss, and a target function model is constructed; and optimizing the target function by using a reinforcement learning algorithm, distributing power output of the fuel cell and the lithium battery according to the current required power and the health state through an energy management system, and regularly updating parameters of a parameter identification module and a model to realize dynamic optimization. The method can identify the life difference between multiple piles, balance the life difference to realize multi-pile performance balance and improve the overall life of a multi-pile system.

A method of operating a virtual power plant (VPP) controller, that manages an electric vehicle (EV) charging station connected to a power grid, a battery storage system, and an independent power plant, includes: obtaining a first data set including time-series information of power output from the independent power plant; training a machine learning (ML) model that predicts power output from the independent power plant over a predetermined prediction horizon; obtaining a second data set including time-series information of: power demand information from the EV charging station; and power availability information from each of the battery storage system, the power grid, and the independent power plant; generating a predicted schedule of power output from the independent power plant over the predetermined prediction horizon; generating a command by inputting the second data set and the predicted schedule into an energy management system (EMS) policy.

According to the intelligent energy-saving analysis method based on the building information model (BIM), building thermal performance parameters and an intelligent simulation algorithm are integrated, building heat conduction, a heat bridge effect and solar heat gain are accurately simulated, and a scientific basis is provided for design. Meanwhile, an energy-saving optimization strategy is automatically generated, such as wall thickness adjustment and window design, so that energy consumption is reduced. Besides, a multidisciplinary cooperative working platform is established, effective implementation of energy-saving measures is ensured, and energy use and maintenance tracking of the whole life cycle of the building are realized and energy-saving performance is continuously optimized in combination with an energy management system. The innovation of the method lies in the automatic and intelligent analysis process, and a comprehensive solution is provided for the field of building energy conservation.

The invention discloses an intelligent photovoltaic power generation system control method, and relates to the technical field of photovoltaic power generation management, and the method comprises the steps: collecting data in real time based on a sensor in a photovoltaic system, employing an edge computing technology, and transmitting important information to a cloud end; the future illumination change is predicted, so that the output of the inverter is adjusted in advance; based on an energy management system, the power demand and the generating capacity are monitored in real time, and interaction of photovoltaic power generation, energy storage and a power grid is coordinated; acquiring historical data, identifying a fault mode, and performing fault detection and prediction maintenance; based on the AR technology, the system state is displayed in real time. According to the method, environment data such as illumination, temperature and wind speed are collected in real time through a sensor, the edge computing technology is combined, the system state and the abnormal condition can be rapidly detected, the power generation efficiency is improved, illumination prediction based on the deep learning algorithm can dynamically adjust the working point of the inverter, the power generation capacity of the photovoltaic system is improved to the maximum extent, and the power generation efficiency is improved. And the method adapts to rapidly changing environmental conditions.