Energy microgrid intelligent management system based on zero-carbon cabin

Abstract

The invention provides an energy microgrid intelligent management system based on a zero-carbon cabin. The system comprises a distributed power supply management module, an intelligent home integration module, a load management module and an energy intelligent management module. The distributed power supply management module is used for accessing various distributed power supplies and dynamically adjusting the output of the power supply according to the control strategy of the energy intelligent management module and the load demand; the intelligent home integrated module comprises an intelligent light and building five-constant system in a zero-carbon cabin and an intelligent household electrical appliance system and is used for providing life requirements; the load management module is used for performing classification management on various accessed loads, realizing scalable control of the loads and starting or closing specific loads according to the control instruction issued by the energy intelligent management module; and the energy intelligent management module is used for receiving data from the distributed power supply management module, the load management module and the intelligent home integration module and issuing control instructions to the modules to realize centralized scheduling management.

Description

Technical Field

[0001] The present invention relates to the technical field of zero-carbon cabin intelligent management, and in particular to an energy microgrid intelligent management system based on zero-carbon cabins. Background Art

[0002] As global concern over climate change intensifies, zero-carbon buildings and energy microgrids are becoming increasingly important for urban development and sustainable architecture. A zero-carbon cabin is a type of zero-carbon building, designed to achieve a living space with zero net carbon emissions. Its core concept is to reduce carbon emissions through movable modular green construction technology and offset the remaining carbon emissions through renewable energy and efficient resource management. The zero-carbon cabin aims to address climate change, promote sustainable development, and provide a comfortable, healthy, and intelligent living environment. The zero-carbon station is a multifunctional field application based on a cluster of zero-carbon cabin buildings. Based on the concept of sustainable development and utilizing energy-saving and emission-reduction technologies, it enables off-grid operation and can be applied to projects in various fields, including zero-carbon demonstration parks, green cultural tourism, and low-energy commercial blocks.

[0003] The current zero-carbon cabin lacks a scheduling and management strategy between smart homes and energy, and cannot effectively reduce energy costs. Summary of the Invention

[0004] The purpose of the present invention is to provide an energy microgrid intelligent management system based on zero-carbon cabins to solve the problems raised in the above background technology.

[0005] The present invention is achieved through the following technical solutions: an energy microgrid intelligent management system based on a zero-carbon cabin, the system comprising a distributed power management module, a smart home integration module, a load management module, and an energy intelligent management module;

[0006] The distributed power management module is used to access multiple distributed power sources and dynamically adjust the output of the power supply according to the control strategy of the energy intelligent management module and the load demand;

[0007] The smart home integrated module includes the smart lighting and building five constant systems and smart home appliance systems in the zero-carbon cabin, which are used to provide daily necessities;

[0008] The load management module is used to classify and manage various types of connected loads, realize hierarchical control of loads and enable or disable specific loads as needed according to the control instructions issued by the energy intelligent management module;

[0009] The energy intelligent management module is used to receive data from the distributed power management module, the load management module, and the smart home integration module, and issue control instructions to each module to achieve centralized scheduling management.

[0010] Specifically, the distributed power management module includes a photovoltaic power generation unit, a wind power generation unit, a power grid unit, and an energy storage unit; the photovoltaic power generation unit includes flexible photovoltaic tiles installed on the roof of the zero-carbon cabin for photovoltaic power generation; the wind power generation unit includes a small wind turbine set configured high on the roof of the zero-carbon cabin for wind power generation; the power grid unit is used to automatically connect and use when the photovoltaic power generation unit and the wind power generation unit cannot meet the power supply demand, and the energy storage unit is used to store excess electrical energy in the photovoltaic power generation unit or the wind power generation unit.

[0011] Specifically, the smart home system module uses a standard home automation protocol to connect smart home devices, performs real-time monitoring of current and power through smart socket devices, and works in conjunction with the load management module to update the status data of smart appliances in real time.

[0012] Specifically, the load management module classifies the connected loads, determines the load priority, and establishes a load database. The load priority is determined by: determining the load priority according to the load power consumption, load type, and usage mode:

[0013] Load power consumption includes high-power load and low-power load, and high-power load has a higher priority than low-power load; load type includes essential load and non-essential load, and essential load has a higher priority than non-essential load; usage mode includes high-load usage period and low-load usage period.

[0014] Specifically, the load management module classifies the incoming loads and establishes a load database according to the load priorities. The established load database specifically includes:

[0015] The load database includes a basic layer: storing basic information of the load, including name, model, power, type, manufacturer and purchase date;

[0016] Status layer: records the status information of the load in real time, including real-time power consumption, switch status, operating time, fault information, and environmental status of the load;

[0017] Usage pattern layer: Analyzes user usage habits and patterns, including peak usage periods, common loads, scenario patterns, and event-triggered usage. By analyzing historical data and time series, a complete user profile is formed.

[0018] Specifically, it also includes a load fault prediction module for analyzing historical power consumption data, identifying the normal mode and potential fault mode of the load, and sending the identification information to the energy intelligent management module.

[0019] Specifically, the specific process of the energy intelligent management module dynamically adjusting the output of the distributed power supply is as follows:

[0020] First, obtain the power generation status, historical load data, weather parameters and related life event information of distributed power sources in real time;

[0021] Second, historical load data is modeled and combined with real-time data analysis to predict the cyclical and sudden trends of load demand;

[0022] Third, taking into account the power generation cost and weather parameters, and based on the load forecast results, a multi-objective evolutionary algorithm is used to generate the final scheduling result, which is then sent to the distributed power management module.

[0023] Specifically, the specific process of the energy intelligent management module issuing control instructions to enable or disable specific loads as needed is as follows:

[0024] First, based on historical load data, identify the time period with peak load;

[0025] Second, it combines external data, including weather conditions, time of day, season, and activity schedules, with internal electricity usage data, and then uses a convolutional neural network to identify the impact of context on electricity usage behavior.

[0026] Third, based on power consumption behavior and changes in each load, it analyzes usage patterns. During peak hours, it automatically assesses the connected load status and current load conditions and dynamically adjusts load priorities.

[0027] Fourth, based on the adjusted load priority, load start and stop instructions are issued to the load management module according to the current load priority order to ensure the normal use of loads with high load priority.

[0028] Specifically, the load management module updates the load database according to the adjusted load priority, and starts and stops the load according to the control instructions issued by the energy intelligent management module.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention provides an energy microgrid intelligent management system based on a zero-carbon cabin. Through real-time monitoring and dynamic scheduling, the system can maximize the use of new energy, reduce dependence on traditional energy, achieve higher energy utilization, and optimize the scheduling plan to effectively reduce energy costs, especially during electricity procurement and peak load periods. Through demand response management, users can reduce energy consumption during high-demand periods. Through intelligent regulation, it is ensured that the zero-carbon cabin maintains a preset comfort mode, thereby protecting residents' living experience, reducing carbon footprint and other pollutant emissions, contributing to the city's sustainable development goals, and achieving a green lifestyle. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 This is a module diagram of an energy microgrid intelligent management system based on a zero-carbon cabin provided by the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present invention more apparent, exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0034] In the following description, numerous specific details are provided to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without one or more of these details. In other instances, certain technical features well known in the art are not described to avoid confusion with the present invention.

[0035] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0036] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0037] In order to fully understand the present invention, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present invention. Optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other implementations.

[0038] See Figure 1 , an energy microgrid intelligent management system based on a zero-carbon cabin, the system includes a distributed power management module, a smart home integration module, a load management module, and an energy intelligent management module;

[0039] The distributed power management module is used to access multiple distributed power sources and dynamically adjust the output of the power supply according to the control strategy of the energy intelligent management module and the load demand;

[0040] The smart home integrated module includes the smart lighting and building five constant systems and smart home appliance systems in the zero-carbon cabin, which are used to provide daily necessities;

[0041] The load management module is used to classify and manage various types of connected loads, realize hierarchical control of loads and enable or disable specific loads as needed according to the control instructions issued by the energy intelligent management module;

[0042] The energy intelligent management module is used to receive data from the distributed power management module, the load management module, and the smart home integration module, and issue control instructions to each module to achieve centralized scheduling management.

[0043] For example, the present invention provides an intelligent energy microgrid management system for a zero-carbon cabin. This system uses a distributed power management module to connect to multiple distributed power sources (such as solar, wind, and battery storage) to provide renewable energy for the cabin. Based on the intelligent energy management module's control strategy, real-time load demand, and power supply status, it dynamically adjusts the output power of each distributed power source to ensure user needs and system stability are met.

[0044] The smart home integration module provides intelligent lighting systems, the "Five Constants" building system (constant temperature, humidity, oxygen, quietness, and cleanliness), and smart appliances, ensuring a comfortable environment for users' daily lives. It also monitors indoor environmental parameters and device status in real time, providing feedback to the smart energy management module to improve overall resource management.

[0045] The load management module categorizes and manages all connected loads, allowing different types of loads to be controlled based on their importance and energy requirements. Furthermore, the system receives control commands from the intelligent energy management module and enables or disables specific loads as needed to reduce energy waste. For example, it automatically disables non-essential loads during periods of low power or peak load.

[0046] The Intelligent Energy Management Module collects real-time data from the Distributed Power Management Module, the Load Management Module, and the Smart Home Integration Module for comprehensive analysis. Based on the collected data and predefined control strategies, it generates and issues control instructions to each module, enabling centralized scheduling and optimizing energy distribution and usage.

[0047] Specifically, the distributed power management module includes a photovoltaic power generation unit, a wind power generation unit, a power grid unit, and an energy storage unit; the photovoltaic power generation unit includes flexible photovoltaic tiles installed on the roof of the zero-carbon cabin for photovoltaic power generation; the wind power generation unit includes a small wind turbine set configured high on the roof of the zero-carbon cabin for wind power generation; the power grid unit is used to automatically connect and use when the photovoltaic power generation unit and the wind power generation unit cannot meet the power supply demand, and the energy storage unit is used to store excess electrical energy in the photovoltaic power generation unit or the wind power generation unit.

[0048] For example, by laying flexible photovoltaic tiles on the roof of a zero-carbon cabin, these photovoltaic tiles convert sunlight into electrical energy through the photoelectric effect. This electrical energy can be directly supplied to the smart home system after conversion, or enter the energy storage unit for storage when there is excess electricity. The photovoltaic power generation unit is integrated with an MPPT (maximum power point tracking) controller to monitor the solar radiation intensity in real time and optimize the power generation efficiency to ensure maximum power output under different lighting conditions.

[0049] By configuring small wind turbines high on the roof, wind energy is used to generate electricity. These small wind turbines can automatically adjust the angle of the wind rotor according to changes in wind speed to maintain the optimal operating state, ensuring power generation when wind energy is abundant. The output power of the unit can also be used to supply smart home systems, or be transmitted to energy storage units when there is a surplus.

[0050] When photovoltaic and wind power generation cannot meet the power needs of the smart home system, the traditional grid unit will automatically connect to provide supplementary power to the system. This unit can be linked with the intelligent control module to monitor the grid status in real time and dynamically adjust the connection to ensure that the home's power supply will not be interrupted.

[0051] The energy storage unit is mainly composed of batteries or other energy storage devices (such as supercapacitors), which are used to store excess electricity from photovoltaic and wind power generation units for subsequent use. During peak periods of household electricity demand or when renewable energy generation is insufficient, the energy storage unit can quickly release the stored electricity.

[0052] This new energy power supply and energy storage module can effectively integrate multiple energy forms and achieve efficient and sustainable operation of the zero-carbon cabin through intelligent scheduling and energy management.

[0053] Specifically, the smart home system module uses a standard home automation protocol to connect smart home devices, performs real-time monitoring of current and power through smart socket devices, and works in conjunction with the load management module to update the status data of smart appliances in real time.

[0054] For example, by monitoring the power consumption of each home appliance in real time through smart sockets, users can intuitively understand the energy consumption of each home appliance and make appropriate usage decisions.

[0055] The load management module can distribute the load reasonably before the user peak period based on the real-time status data, thus avoiding energy waste and improving energy efficiency.

[0056] Specifically, the load management module classifies the connected loads, determines the load priority, and establishes a load database. The load priority is determined by: determining the load priority according to the load power consumption, load type, and usage mode:

[0057] Load power consumption includes high-power load and low-power load, and high-power load has a higher priority than low-power load; load type includes essential load and non-essential load, and essential load has a higher priority than non-essential load; usage mode includes high-load usage period and low-load usage period.

[0058] For example, the priorities are sorted from high to low:

[0059] Set high-power consumption loads, essential loads, and loads in high-usage periods as urgent high-priority loads;

[0060] Examples: air conditioners (during high temperatures in summer), electric water heaters (for morning showers)

[0061] Set high-power consumption loads, essential loads, and loads in low-usage periods as important high-priority loads;

[0062] Examples: air conditioner (for cold winter months), electric water heater (for nighttime heating)

[0063] Set high-power consumption loads, non-essential loads, and high-usage periods as secondary high-priority loads;

[0064] Examples: washing machine (after a family gathering), dishwasher (after dinner)

[0065] Prioritize high-power consumption loads, non-essential loads, and loads with low usage periods

[0066] Examples: Water heater (nighttime laundry), oven (weekend baking)

[0067] Set low-power loads, essential loads, and loads during high-usage periods as basic guaranteed loads

[0068] Examples: refrigerator (needs to run all day), small kitchen equipment (breakfast peak)

[0069] Set low-power loads, essential loads, and low-usage loads as regular guaranteed loads

[0070] Examples: Refrigerator (at night), Heater (rarely used)

[0071] Set low-power loads, non-essential loads, and high-usage periods as flexible loads

[0072] Examples: coffee machine (in the morning), microwave (at lunch)

[0073] Set low-priority loads for low power consumption, non-essential loads, and low-usage periods

[0074] Examples: Electric toothbrushes, small portable appliances (occasional use)

[0075] Specifically, the load management module establishes a load database according to the priority of the load, and the load database established specifically includes:

[0076] The load database includes a basic layer: storing basic information of the load, including name, model, power, type, manufacturer and purchase date;

[0077] Status layer: records the status information of the load in real time, including real-time power consumption, switch status, operating time, fault information, and environmental status of the load;

[0078] Usage pattern layer: Analyzes user usage habits and patterns, including peak usage periods, common loads, scenario patterns, and event-triggered usage. By analyzing historical data and time series, a complete user profile is formed.

[0079] For example, in the base layer, the system records basic information of each load, including:

[0080] Name: The name of the device (such as "refrigerator", "washing machine", etc.).

[0081] Model: The specific model of the device.

[0082] Power: The rated power of the device.

[0083] Type: The type of equipment (such as home appliances, lighting equipment, etc.).

[0084] Manufacturer: The manufacturer of the equipment.

[0085] Purchase Date: The date the user purchased the device.

[0086] State layer:

[0087] The status layer records the working status of the load in real time, including:

[0088] Real-time power consumption: Real-time monitoring of power usage through smart sockets or sensors.

[0089] Switch status: records whether the device is turned on.

[0090] Running time: The cumulative running time of the device.

[0091] Fault information: Faults or abnormal conditions that occur during equipment operation.

[0092] Environmental conditions: the impact of external environment such as temperature and humidity on equipment operation.

[0093] These data are updated in real time through the smart socket and synchronized to the load management module regularly.

[0094] Using the pattern layer:

[0095] The usage pattern layer analyzes user usage habits and forms user profiles, including:

[0096] Peak usage period: The peak period when users use electrical appliances, identifying the peak electricity consumption of users.

[0097] Common load: electrical equipment most frequently used by users.

[0098] Scenario mode: User's device usage habits in specific scenarios (such as family gatherings, work mode, etc.).

[0099] Event-triggered usage: device usage triggered by specific events (such as returning home, going out, etc.).

[0100] By analyzing historical data and time series, the system can identify user usage patterns.

[0101] Specifically, it also includes a load fault prediction module for analyzing historical power consumption data, identifying the normal mode and potential fault mode of the load, and sending the identification information to the energy intelligent management module.

[0102] For example, the load fault prediction module first collects historical electricity usage data from household appliances through smart sockets, including information such as power consumption, on / off status, and operating time. This data is stored in a time series format, forming a complete electricity usage profile. Machine learning algorithms are then used to analyze this historical data to identify the normal operating modes of the devices. By analyzing current and power variations, a model is established to identify the normal operating state. After successfully identifying the normal operating mode, the module continuously monitors the data. If any deviations from the normal pattern are detected, further analysis is performed to identify potential fault modes, including abnormal power consumption and very high or very low operating time.

[0103] By comparing load status with normal patterns, the load fault prediction module can identify abnormal load conditions. For example, if an appliance experiences a significant increase in power consumption during a certain period of time and fails to operate as expected, the module will flag this as a potential fault and analyze it.

[0104] Specifically, the specific process of the energy intelligent management module dynamically adjusting the output of the distributed power supply is as follows:

[0105] First, obtain the power generation status, historical load data, weather parameters and related life event information of distributed power sources in real time;

[0106] Second, historical load data is modeled and combined with real-time data analysis to predict the cyclical and sudden trends of load demand;

[0107] Third, taking into account the power generation cost and weather parameters, and based on the load forecast results, a multi-objective evolutionary algorithm is used to generate the final scheduling result, which is then sent to the distributed power management module.

[0108] For example, the energy intelligent management module first obtains the power generation status, historical load data, weather parameters (such as temperature, humidity, wind speed, etc.) and related life event information (such as family activity time, special holidays, etc.) of the distributed power source in real time through sensors and monitoring systems. This information provides basic data for subsequent load forecasting and scheduling.

[0109] Secondly, the collected historical load data is analyzed and modeled to identify cyclical and sudden trends in load demand. For example, time series analysis can be used to explore user electricity usage habits and identify characteristics such as peak load periods and seasonal changes in electricity consumption. Furthermore, if there are unexpected events (such as major weather changes or people leaving their homes), reasonable predictions can be made based on historical patterns.

[0110] After completing the load demand forecast, the system comprehensively considers the generation costs of distributed generation (DGs), weather parameters (such as projected generation capacity and whether it can meet load demand), and the load forecast results. A multi-objective evolutionary algorithm is then used to generate a set of optimal scheduling solutions, ensuring cost-effectiveness and feasibility under various conditions.

[0111] The specific process of generating a set of optimal scheduling solutions through a multi-objective evolutionary algorithm is as follows:

[0112] Clarify optimization goals, including:

[0113] Minimizing power generation costs: Considering the power generation costs of various power generation sources (such as fuel costs and maintenance costs).

[0114] Reliability assurance: ensuring that forecasted load demands are met, especially during peak periods.

[0115] Environmental impact: Use clean energy for power generation whenever possible to reduce carbon emissions and environmental impact.

[0116] Weather impact analysis: Evaluate the generation potential of distributed power sources in combination with weather parameters. For example, when sunny days are expected, solar power generation will reach its peak, while in severe weather, it may need to be supplemented by energy storage units or other energy sources.

[0117] Based on the predicted load demand, it matches the available distributed generation capacity to ensure resource optimization.

[0118] Generate the final scheduling plan, including the output targets and operating sequence of each distributed power source.

[0119] Finally, the energy intelligent management module sends the final scheduling results to the distributed power management module. Based on the received scheduling signal, the distributed power management module dynamically adjusts the output of the distributed power supply to achieve effective energy management.

[0120] Specifically, the specific process of the energy intelligent management module issuing control instructions to enable or disable specific loads as needed is as follows:

[0121] First, based on historical load data, identify the time period with peak load;

[0122] Second, it combines external data, including weather conditions, time of day, season, and activity schedules, with internal electricity usage data, and then uses a convolutional neural network to identify the impact of context on electricity usage behavior.

[0123] Third, based on power consumption behavior and changes in each load, it analyzes usage patterns. During peak hours, it automatically assesses the connected load status and current load conditions and dynamically adjusts load priorities.

[0124] Fourth, based on the adjusted load priority, load start and stop instructions are issued to the load management module according to the current load priority order to ensure the normal use of loads with high load priority.

[0125] Exemplarily, internal electricity consumption data includes historical electricity consumption data, which is recorded in a time series format (such as hourly electricity consumption). External data includes weather conditions: including temperature, humidity, precipitation, wind speed, etc. Time characteristics: including hours, days of the week, months, etc. Seasonal characteristics: calculating the season (spring, summer, autumn, winter) based on the date. Event schedule: marking of special event days (such as holidays, promotions, etc.). By inputting real-time external data and internal electricity consumption data, predict future electricity consumption. Identify load peak periods and electricity consumption behavior patterns to provide a basis for subsequent load management. Based on the predicted electricity consumption and the current load status, automatically evaluate the connected load situation and dynamically adjust the load priority. In time periods close to the peak, priority is given to ensuring the normal use of loads with high load priority.

[0126] Specifically, the load management module updates the load database according to the adjusted load priority, and starts and stops the load according to the control instructions issued by the energy intelligent management module.

[0127] For example, by enabling or disabling loads on demand, the system can reduce unnecessary power consumption while ensuring the normal operation of important loads, thereby achieving higher energy efficiency.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An energy microgrid intelligent management system based on zero-carbon cabin, characterized by: The system includes a distributed power management module, a smart home integration module, a load management module, and an energy intelligent management module; The distributed power management module is used to access multiple distributed power sources and dynamically adjust the output of the power supply according to the control strategy of the energy intelligent management module and the load demand; The smart home integrated module includes the smart lighting and building five constant systems and smart home appliance systems in the zero-carbon cabin, which are used to provide daily necessities; The load management module is used to classify and manage various types of connected loads, realize hierarchical control of loads and enable or disable specific loads as needed according to the control instructions issued by the energy intelligent management module; The energy intelligent management module is used to receive data from the distributed power management module, the load management module, and the smart home integration module, and issue control instructions to each module to achieve centralized scheduling management.

2. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 1 is characterized in that: The distributed power management module includes a photovoltaic power generation unit, a wind power generation unit, a power grid unit, and an energy storage unit; the photovoltaic power generation unit includes flexible photovoltaic tiles installed on the roof of the zero-carbon cabin for photovoltaic power generation; the wind power generation unit includes a small wind turbine set configured high on the roof of the zero-carbon cabin for wind power generation; the power grid unit is used to automatically connect and use when the photovoltaic power generation unit and the wind power generation unit cannot meet the power supply demand, and the energy storage unit is used to store excess electrical energy in the photovoltaic power generation unit or the wind power generation unit.

3. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 2 is characterized in that: The smart home system module uses a standard home automation protocol to connect smart home devices, performs real-time monitoring of current and power through smart socket devices, and works in conjunction with the load management module to update the status data of smart appliances in real time.

4. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 3 is characterized in that: The load management module classifies the connected loads, determines the load priority, and establishes a load database. The load priority is determined according to the load power consumption, load type, and usage mode: Load power consumption includes high-power load and low-power load, and high-power load has a higher priority than low-power load; load type includes essential load and non-essential load, and essential load has a higher priority than non-essential load; usage mode includes high-load usage period and low-load usage period.

5. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 4 is characterized in that: The load management module establishes a load database according to the load priority, and the load database established specifically includes: The load database includes a basic layer: storing basic information of the load, including name, model, power, type, manufacturer and purchase date; Status layer: records the status information of the load in real time, including real-time power consumption, switch status, operating time, fault information, and environmental status of the load; Usage pattern layer: Analyzes user usage habits and patterns, including peak usage periods, common loads, scenario patterns, and event-triggered usage. By analyzing historical data and time series, a complete user profile is formed.

6. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 5 is characterized in that: It also includes a load fault prediction module for analyzing historical power consumption data, identifying the normal mode and potential fault mode of the load, and sending the identification information to the energy intelligent management module.

7. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 6 is characterized in that: The specific process of the energy intelligent management module dynamically adjusting the output of the distributed power supply is as follows: First, obtain the power generation status, historical load data, weather parameters and related life event information of distributed power sources in real time; Second, historical load data is modeled and combined with real-time data analysis to predict the cyclical and sudden trends of load demand; Third, taking into account the power generation cost and weather parameters, and based on the load forecast results, a multi-objective evolutionary algorithm is used to generate the final scheduling result, which is then sent to the distributed power management module.

8. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 7 is characterized in that: The specific process of the energy intelligent management module issuing control instructions to enable or disable specific loads as needed is as follows: First, based on historical load data, identify the time period with peak load; Second, it combines external data, including weather conditions, time of day, season, and activity schedules, with internal electricity usage data, and then uses a convolutional neural network to identify the impact of context on electricity usage behavior. Third, based on power consumption behavior and changes in each load, it analyzes usage patterns. During peak hours, it automatically assesses the connected load status and current load conditions and dynamically adjusts load priorities. Fourth, based on the adjusted load priority, load start and stop instructions are issued to the load management module according to the current load priority order to ensure the normal use of loads with high load priority.

9. The energy microgrid intelligent management system based on zero-carbon cabin according to claim 8 is characterized in that: The load management module updates the load database according to the adjusted load priority, and starts and stops the load according to the control instructions issued by the energy intelligent management module.

Images