Energy management optimization method and device for energy equipment, equipment and storage medium

Abstract

The invention is applicable to the technical field of energy management, and provides an energy management optimization method for energy equipment, which comprises the following steps: when a data acquisition request for intelligent energy equipment connected with an energy management terminal is received, performing data acquisition on the intelligent energy equipment to obtain energy use data, performing mode identification on the energy use data, generating an energy use mode, performing scheme integration on preset energy demand information and the energy use mode, generating an energy management scheme for the energy management terminal, and performing energy management according to the energy management scheme. The energy management terminal performs energy configuration optimization on the intelligent energy equipment to realize optimization of energy use, so that generation and configuration of a personalized energy management scheme are realized, the energy utilization efficiency is improved, and personalized requirements of different users are met.

Description

Technical Field

[0001] The present invention belongs to the field of energy management technology, and in particular relates to an energy management optimization method, device, equipment and storage medium for energy equipment. Background Art

[0002] In the field of energy management, there are currently two main types of tools or solutions on the market: general energy management solutions and simple data statistics energy management tools. However, both solutions have significant limitations and are difficult to meet the growing demand for personalized energy management.

[0003] First, general energy management solutions: Although general energy management solutions occupy a certain share in the market, their inherent defects limit their effectiveness. First, such solutions are often formulated based on general energy usage rules and energy-saving principles, and fail to fully consider the significant differences in energy usage patterns, living habits, equipment configurations and energy needs of different users. This leads to a lack of personalized customization of the solutions, making it difficult to generate practical energy management strategies for each user, thus affecting the optimization of energy utilization efficiency; second, general solutions have inaccurate data analysis. They usually rely only on macro-general statistical data, rather than digging deep into users' specific energy usage data. This superficial analysis method makes it difficult to accurately identify energy usage patterns, such as peak hours of electricity consumption, common equipment combinations, etc., making the proposed energy management suggestions lack pertinence and effectiveness. In addition, such solutions often lack an effective feedback mechanism, and cannot adjust the solutions in a timely manner according to user feedback and changes in energy usage, further limiting their flexibility and applicability.

[0004] Second, simple data statistics type energy management tools: Simple data statistics type energy management tools mainly focus on the collection, recording and simple chart display of energy usage data. Although such tools can present the basic situation of energy consumption, their functions are limited to data display, and they cannot deeply explore the energy usage patterns and personalized needs behind the data. Therefore, they cannot generate effective energy management solutions based on user data and needs. Users need to rely on their own experience to formulate strategies, which not only increases the difficulty but also reduces the scientificity and effectiveness of energy management. In addition, such tools are often designed for specific scenarios, but they do not take into account the personalized energy use and needs of different individual users in the scenario. This makes it difficult for users to provide feedback based on their own needs and opinions, further limiting the applicability and flexibility of the tools. At the same time, because they are closely designed around specific scenarios, such tools often lack flexibility and cannot play an effective role when applied to other scenarios, and cannot meet the personalized needs of users in different scenarios.

[0005] In summary, the general energy management solutions and simple data statistics-based energy management tools currently available on the market have obvious limitations and cannot meet the growing demand for personalized energy management. Summary of the invention

[0006] The purpose of the present invention is to provide an energy management optimization method, device, equipment and storage medium for energy equipment, aiming to solve the problem that the energy management solution cannot meet personalized needs and the energy utilization rate is poor due to the inability of existing technologies.

[0007] In one aspect, the present invention provides a method for optimizing energy management of energy equipment, the method comprising the following steps:

[0008] When receiving a data collection request for a smart energy device connected to the energy management terminal, collecting data from the smart energy device to obtain energy usage data;

[0009] Performing pattern recognition on the energy usage data to generate an energy usage pattern;

[0010] Integrate the preset energy demand information and the energy usage pattern to generate an energy management plan for the energy management terminal;

[0011] According to the energy management solution, the energy configuration of the smart energy device is optimized through the energy management terminal to achieve optimal energy use.

[0012] Preferably, after the step of optimizing the energy configuration of the smart energy device through the energy management terminal, the method further comprises:

[0013] receiving feedback information from a user on the energy management solution output by the energy management terminal;

[0014] When it is determined according to the feedback information that the energy usage mode and / or the energy demand information has changed, the process jumps to the step of collecting data from the smart energy device to continue the process, so as to generate an energy management solution that is more in line with the user's current situation.

[0015] Preferably, the step of performing pattern recognition on the energy usage data to generate an energy usage pattern comprises:

[0016] Performing data cleaning on the energy usage data;

[0017] Cluster analysis is performed on the cleaned energy usage data to obtain the energy usage pattern.

[0018] Preferably, the step of integrating the preset energy demand information and the energy usage mode to generate an energy management plan for the energy management terminal includes:

[0019] Selecting an energy management policy that matches the energy management scenario currently in which the energy management terminal is located from a preset energy management policy template;

[0020] According to the energy demand information and the energy usage pattern, the energy management strategy is adjusted and optimized using a preset optimization algorithm to obtain the energy management solution.

[0021] Preferably, the step of adjusting and optimizing the energy management strategy using a preset optimization algorithm includes:

[0022] Using a pre-trained linear regression model to predict the energy consumption value of the intelligent energy device under different energy consumption influencing factors, and obtaining energy consumption prediction values ​​corresponding to the energy consumption influencing factors;

[0023] The energy management strategy is adjusted and optimized according to the energy consumption forecast value.

[0024] In another aspect, the present invention provides an energy management optimization device for energy equipment, the device comprising:

[0025] A data collection unit, configured to collect data from a smart energy device connected to an energy management terminal to obtain energy usage data when receiving a data collection request from the smart energy device.

[0026] A pattern generation unit, configured to perform pattern recognition on the energy usage data to generate an energy usage pattern;

[0027] A solution generating unit, configured to integrate the preset energy demand information and the energy usage mode into a solution to generate an energy management solution for the energy management terminal;

[0028] A configuration optimization unit is used to optimize the energy configuration of the smart energy device through the energy management terminal according to the energy management plan to achieve optimal energy use.

[0029] Preferably, the device further comprises:

[0030] A feedback information receiving unit, configured to receive feedback information from a user on the energy management solution output by the energy management terminal;

[0031] The data re-collection unit is used to trigger the data collection unit to perform data collection on the smart energy device when it is determined according to the feedback information that the energy usage mode and / or the energy demand information has changed, so as to generate an energy management plan that is more in line with the user's current situation.

[0032] Preferably, the pattern generating unit comprises:

[0033] A data cleaning unit, used for cleaning the energy usage data;

[0034] The data analysis unit is used to perform cluster analysis on the cleaned energy usage data to obtain the energy usage pattern.

[0035] On the other hand, the present invention also provides an energy management intelligent device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps described in the energy management optimization method of the above-mentioned energy device are implemented.

[0036] On the other hand, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps described in the energy management optimization method of the energy device are implemented.

[0037] When the present invention receives a data collection request for an intelligent energy device connected to an energy management terminal, it collects data from the intelligent energy device to obtain energy usage data, performs pattern recognition on the energy usage data to generate an energy usage pattern, integrates the preset energy demand information and the energy usage pattern to generate an energy management plan for the energy management terminal, and according to the energy management plan, optimizes the energy configuration of the intelligent energy device through the energy management terminal to achieve optimization of energy usage, thereby realizing the generation and configuration of personalized energy management plans, improving energy utilization efficiency, and meeting the personalized needs of different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of an implementation method of an energy management optimization method for energy equipment provided in Embodiment 1 of the present invention;

[0039] Figure 2 It is a structural schematic diagram of an energy management optimization device for energy equipment provided in Embodiment 2 of the present invention;

[0040] Figure 3 It is a schematic diagram of the structure of the energy management intelligent device provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments:

[0043] Embodiment 1:

[0044] Figure 1 The implementation process of the energy management optimization method of energy equipment provided by the first embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0045] In step S101, when a data collection request for a smart energy device connected to an energy management terminal is received, data is collected from the smart energy device to obtain energy usage data.

[0046] The embodiments of the present invention are applicable to smart devices, platforms or systems that can consume, convert, store or manage energy, including but not limited to smart home devices (such as smart electricity meters, smart water meters, smart gas meters, smart air conditioners, etc.), industrial energy management systems (such as smart grid equipment, industrial automation systems, etc.). In the embodiments of the present invention, smart energy devices include but are not limited to energy metering devices such as smart electricity meters, smart water meters, smart gas meters, etc. at the user end, and other related energy use devices. The energy management terminal is an energy management assistant, which covers professional knowledge in various aspects from power generation, transmission, distribution to power users, power trading, system operation, qualification examinations, investment decisions, policies and regulations, and keeps up with industry trends and regularly updates its knowledge base. At the same time, the energy management terminal can provide users with various energy-related information and suggestions through advanced algorithms and artificial intelligence technologies. Here, the energy management terminal establishes a stable connection with each smart energy device through wireless (such as Wi-Fi, Bluetooth, etc.) communication. When a data collection request for a smart energy device connected to the energy management terminal is received, the smart energy The device collects data to obtain energy usage data. Specifically, different types of energy usage data are collected in a pre-set format. The energy usage data includes but is not limited to specific values ​​such as electricity consumption, gas consumption, water consumption, and the operating status of the device (such as power on, power off, standby, etc.), usage time, etc. At the same time, the user is allowed to supplement some relevant information that cannot be automatically collected under special circumstances through a manual input interface, such as the use of temporarily borrowed electrical equipment, etc., so as to obtain more comprehensive and accurate user energy usage data, which not only covers common energy consumption values, but also includes information such as equipment operation details, laying a solid foundation for subsequent accurate analysis of user energy usage patterns, and overcoming the problems of incomplete and inaccurate data collection in existing general energy management solutions and simple data statistics tools. In addition, the data collection request can be automatically triggered. Specifically, the data collection request is automatically triggered according to the preset data collection frequency (such as collecting once every 15 minutes). The data collection request can also be manually triggered by the user according to the actual situation. The triggering conditions of the data collection request are not specifically limited here.

[0047] In step S102, pattern recognition is performed on the energy usage data to generate an energy usage pattern.

[0048] In an embodiment of the present invention, a large amount of collected energy usage data is deeply analyzed to identify the regularity, periodic changes and abnormal consumption patterns of energy usage, thereby constructing a user-specific energy usage pattern.

[0049] In a feasible embodiment, the generation of energy usage pattern is achieved through the following steps:

[0050] (S102.1) Cleaning energy usage data;

[0051] In an embodiment of the present invention, a data preprocessing algorithm is used to clean the collected energy usage data. Specifically, outliers, duplicate data, and erroneous data caused by equipment failures and other reasons are removed to ensure the accuracy and consistency of the data, thereby improving the data quality of subsequent analysis.

[0052] (S102.2) Perform cluster analysis on the cleaned energy usage data to obtain an energy usage pattern.

[0053] In an embodiment of the present invention, advanced data analysis algorithms (such as cluster analysis, association rule mining, etc.) are used to analyze the cleaned energy usage data. Through cluster analysis, the user's energy usage behavior is classified according to similarity, and different types of energy usage patterns are identified. For example, users are divided into different categories such as "daytime concentrated electricity usage type" and "nighttime concentrated electricity usage type" according to the electricity usage time and the frequency of equipment use; through association rule mining, the intrinsic connection between energy usage data and factors such as equipment, time, and activities is found, such as determining which devices are often used together in any time period.

[0054] Through the above steps (S102.1) and (S102.2), the user's specific energy usage pattern can be accurately identified, thereby providing a key basis for the subsequent generation of an energy management plan that truly meets the user's personalization.

[0055] In step S103, the preset energy demand information and energy usage pattern are integrated to generate an energy management plan for the energy management terminal.

[0056] In an embodiment of the present invention, the identified energy usage pattern is matched and combined with the energy demand information pre-set by the user to generate a personalized energy management plan for the energy management terminal, which aims to balance multiple dimensions such as energy efficiency, cost savings, and environmental sustainability, while ensuring that the specific needs of the user are met, wherein the energy demand information includes but is not limited to energy saving goals, cost control goals, carbon emission limits, energy supply stability requirements, energy use priorities within a specific time period (such as energy allocation differences between weekdays and weekends), etc. For example, if the user sets an energy saving goal, then the analysis process will focus on how to adjust the device usage time, reduce energy consumption peaks, etc. according to the identified energy usage pattern to meet energy saving needs.

[0057] In a feasible embodiment, when integrating the preset energy demand information and energy usage pattern into a plan, they are first matched and integrated at the data level. Specifically, the characteristics of the energy usage pattern are extracted and quantified, and the energy demand information is also quantified into specific numerical indicators. Then, the quantified energy usage pattern and energy demand information are matched according to pre-established matching rules, and an energy management plan that conforms to the user's actual situation and meets the user's needs is generated based on the matching results.

[0058] In another feasible embodiment, the generation of the energy management plan is achieved through the following steps:

[0059] (S103.1) Selecting an energy management strategy that matches the energy management scenario currently in which the energy management terminal is located from a preset energy management strategy template;

[0060] In an embodiment of the present invention, the energy management policy template includes various energy management policies (such as household electricity peak and valley adjustment policies, commercial equipment time-sharing operation policies) created according to different scenarios (such as home and business), so as to select appropriate policies from the template as a basis according to the specific scenario in which the energy management terminal is currently located. Here, the energy management scenario in which the energy management terminal is currently located is first determined by analyzing the device type, usage time and other information of the smart energy device. Then, an energy management policy with a high degree of match with the current energy management scenario and that can meet the actual situation of the user is selected from the energy management policy template as the basis for subsequent solution customization and integration, so as to ensure that the selected policy can be effectively applied to the energy management scenario of the user.

[0061] As an example, through analysis, it is found that smart energy devices mainly include multiple air conditioners, refrigerators, washing machines, televisions and various small appliances, and the peak electricity consumption is concentrated in the summer evening (18:00-22:00). Therefore, it can be determined that the current energy management scenario is a home. Since the peak electricity consumption of the home is obvious, the home electricity peak-valley adjustment strategy is selected from the preset energy management strategy template as the basis to optimize the electricity cost by utilizing the difference in peak-valley electricity prices. For example, it is recommended to set some timed appliances (such as washing machines) to run automatically during the low electricity price period at night (such as 0:00-8:00) to complete tasks such as laundry. For air conditioners, the temperature setting value can be appropriately increased during peak hours (such as from the original 24°C to 26°C). At the same time, combined with smart devices, it can automatically adjust to energy-saving mode or shut down after family members leave home to reduce electricity consumption during peak hours, achieve electricity cost control and rational use of energy.

[0062] (S103.2) According to the energy demand information and energy usage pattern, the energy management strategy is adjusted and optimized using a preset optimization algorithm to obtain an energy management plan.

[0063] In the embodiment of the present invention, the energy management strategy is adjusted and optimized by using a preset optimization algorithm (such as a linear programming algorithm) in combination with energy demand information and energy usage patterns to obtain a complete personalized energy management plan, which may include suggestions for adjusting the usage time of smart energy devices, energy consumption quota allocation, energy-saving measures, etc. For example, if the user is a "nighttime concentrated electricity consumption" household and has set an energy-saving goal, the household electricity peak-valley adjustment strategy will be optimized, and the user is advised to use more non-urgent electrical appliances during the low-valley electricity price period at night, and adjust the usage time of some high-energy-consuming equipment to achieve energy-saving purposes.

[0064] In a specific embodiment, when optimizing the peak-valley adjustment strategy for household electricity consumption, the temperature setting adjustment and timing function of the air-conditioning equipment are applied. Specifically, when adjusting the temperature setting, during the peak electricity consumption period (such as 14:00-19:00 in the afternoon in summer), according to the tolerance of family members, the air-conditioning temperature is appropriately increased, such as from the conventional 24°C to 26°C or even higher. This can significantly reduce the power consumption of the air-conditioning during peak hours. During the low electricity price period at night (such as 0:00-8:00), if the indoor temperature permits, the air-conditioning temperature can be lowered to utilize the low-priced electricity. Refrigeration and storage of cold energy can be performed to reduce the running time of the air conditioner during non-peak hours during the day. At the same time, when the timing function is used, make full use of the scheduled start and shutdown function of the air conditioner. For example, one hour before getting up in the morning (assuming it is 6:00-7:00, the electricity price may be relatively low during this period or it has not yet entered the peak period), set the air conditioner to start up on time to cool down the room in advance so that the family can enjoy a comfortable temperature after getting up, and avoid turning on the machine for a long time during the peak electricity price period. Similarly, before going to bed at night, you can set the air conditioner to shut down on time according to the time you fall asleep to prevent wasting electricity by running all night; for electric water heaters, Heating time planning and insulation setting optimization. Specifically, when planning the heating time period, analyze the family's daily hot water usage habits and determine the time periods when the family members use hot water intensively, such as washing up in the morning from 7:00 to 9:00 and bathing in the evening from 19:00 to 22:00. Then set the heating time of the electric water heater to the night-time low electricity price period (such as 0:00-8:00) to ensure that the water heater has completed heating and insulation when hot water is needed, avoiding heating during peak electricity price periods, which can effectively save electricity bills. When optimizing the insulation setting, reasonably adjust the insulation temperature of the electric water heater. Generally speaking, the insulation temperature The temperature can be set slightly higher than the actual hot water temperature to avoid frequent heating caused by excessively high insulation temperature, which will increase unnecessary power consumption. For example, if your family is accustomed to using hot water at around 40°C, you can set the insulation temperature to around 45°C; for other timed appliances such as washing machines, run them during off-peak hours. Specifically, for appliances such as washing machines and dishwashers with timing functions, try to arrange them to run during the night when electricity prices are low. For example, set the washing machine to run between 1:00-3:00 in the morning. This will not affect your family's normal life, but will also use low-priced electricity to complete the laundry task and reduce electricity costs.

[0065] In a feasible embodiment, the energy management strategy is adjusted and optimized by using a preset optimization algorithm through the following steps:

[0066] (S103.2.1) using a pre-trained linear regression model to predict the energy consumption values ​​of the smart energy device under different energy consumption influencing factors, and obtaining energy consumption prediction values ​​corresponding to the energy consumption influencing factors;

[0067] In an embodiment of the present invention, based on current or future energy consumption influencing factors (such as equipment type, quantity, usage time, ambient temperature, etc.), these factors are provided as input variables to a pre-trained linear regression model. The linear regression model calculates the corresponding energy consumption prediction value based on the input variables. This prediction value reflects the possible energy consumption level of the smart energy device under given energy consumption influencing factors.

[0068] In a feasible embodiment, historical energy consumption data of smart energy devices under different energy consumption influencing factors are collected. These influencing factors include but are not limited to device type, quantity, usage time, ambient temperature, humidity, load size, etc. The collected data is cleaned to remove outliers, missing values, etc. to ensure the accuracy and completeness of the data, and features related to energy consumption are extracted from the cleaned data. A linear regression model is constructed based on the selected features to predict the energy consumption of smart energy devices under different energy consumption influencing factors. The cleaned data is used to train the linear regression model. During the training process, the mean square error (MSE) value between the energy consumption value predicted by the linear regression model and the actual historical energy consumption value is calculated to evaluate the prediction performance of the model, and the model parameters (such as the slope and intercept in the linear regression) are adjusted according to the MSE value. The smaller the MSE value, the higher the prediction accuracy of the model and the better the fitting effect on the sample data, which means that the constructed linear regression model can more accurately reflect the relationship between the various factors affecting the energy consumption of smart energy devices and the energy consumption of smart energy devices. As an example, considering only one main factor (such as the number of electrical appliances in the home) on the impact of energy consumption (such as monthly electricity consumption), the constructed linear regression model is expressed as y = β 0 +β 1 x+ε, where y represents energy consumption (dependent variable), such as monthly electricity consumption (kWh), gas consumption (cubic meters), etc., x represents a major factor affecting energy consumption (independent variable), such as the number of electrical equipment mentioned above (units), β 0 It represents the intercept term, which can be understood as the expected value of the dependent variable y when the independent variable x=0. For example, when the number of electrical appliances is 0, there may still be some basic energy consumption in theory (such as the refrigerator is in standby mode, etc.). 0 This reflects the basic energy consumption, β 1 represents the slope, which can be understood as the average increase in the dependent variable y when the independent variable x increases by one unit. For example, if β 1 =5, which means that for every additional electrical device, the average monthly electricity consumption will increase by 5 kWh. ε represents the error term, which represents the difference between the actual observed energy consumption value and the value predicted by the model. This is due to the existence of many other unconsidered factors or measurement errors in actual situations.

[0069] In a specific embodiment, for air-conditioning equipment, based on historical electricity consumption data (such as electricity consumption in different seasons, different time periods, and different temperature settings), indoor and outdoor temperature data, family members' activity patterns and other factors, a linear regression model is constructed to predict the energy consumption of the air conditioner under different energy consumption influencing factors. The independent variables of the model include but are not limited to the indoor and outdoor temperature difference, set temperature, operating time, etc. The prediction performance of the model is evaluated by the MSE value. If the MSE value is smaller, it means that the constructed linear regression model can more accurately reflect the relationship between the various factors affecting the air-conditioning energy consumption and the air-conditioning energy consumption. In this way, when optimizing the household electricity peak and valley adjustment strategy, the prediction of the air-conditioning energy consumption under different adjustment strategies based on this model will be more reliable, which will be more helpful to formulate reasonable and effective adjustment strategies, such as determining how to set the air-conditioning temperature and operating time in different time periods to achieve energy saving purposes.

[0070] (S103.2.2) Adjust and optimize energy management strategies based on energy consumption forecasts.

[0071] In an embodiment of the present invention, the effectiveness of the current energy management strategy is evaluated based on the energy consumption prediction value. If the energy consumption prediction value is higher than expected, it means that the current energy management strategy may not be energy-efficient enough. If the energy consumption prediction value is lower than expected, it means that the energy management strategy may be too conservative. According to the evaluation results, the energy management strategy is adjusted and optimized. For example, if the energy consumption prediction value is too high, you can consider adjusting the working mode of the smart energy device, optimizing load distribution, and improving energy utilization efficiency. If the energy consumption prediction value is too low, you can consider appropriately relaxing energy usage restrictions to improve the operating efficiency of the equipment while ensuring safety.

[0072] In a feasible embodiment, after adjusting and optimizing the energy management strategy, the energy consumption of the smart energy equipment is continuously monitored, and the actual energy consumption data is fed back to the linear regression model for model updating and optimization. Through continuous iterative training, the prediction accuracy and generalization ability of the model are improved.

[0073] Through the above steps (S103.1) and (S103.2), it is possible to provide users with a practical energy management solution that suits their own circumstances, effectively improve energy utilization efficiency, and meet the personalized needs of users.

[0074] In step S104, according to the energy management plan, the energy configuration of the smart energy device is optimized through the energy management terminal to achieve optimal energy use.

[0075] In an embodiment of the present invention, the energy management terminal automatically or through user authorization remotely controls and optimizes the energy configuration of the connected smart energy devices based on the generated energy management plan, such as adjusting the working hours of electrical appliances, optimizing energy distribution, starting energy-saving mode, etc., to achieve optimal energy use.

[0076] In a feasible embodiment, the solution feedback and improvement are achieved through the following steps:

[0077] (1) receiving user feedback on the energy management plan output by the energy management terminal;

[0078] (2) When it is determined based on the feedback information that the energy usage pattern and / or energy demand information has changed, the process jumps to the step of collecting data from the smart energy device to continue the process, so as to generate an energy management solution that is more in line with the user's current situation.

[0079] In an embodiment of the present invention, the generated personalized energy management plan is output to the user in an intuitive and easy-to-understand form, and a perfect feedback mechanism is established to optimize the subsequent plan according to the user feedback. Specifically, according to the different user terminal devices (such as computers, mobile phones, etc.), a suitable output method is selected. If it is a mobile phone, the plan content can be organized into a concise and clear chart or text form and sent to the user through a mobile phone application, ensuring that the user can easily and quickly obtain the generated plan content and can provide information feedback. After receiving the user's feedback information, it is evaluated whether the energy usage mode and / or energy demand information has changed. If it has changed, it means that the user has modified the energy usage mode and / or energy demand information, then a data collection request is triggered, and a personalized energy management plan that is more in line with the user's current situation is regenerated according to the above steps S101 to S104. Therefore, through the perfect output feedback mechanism, not only can the user easily obtain the personalized energy management plan, but also the user feedback can be collected in time, and the plan can be optimized according to the actual situation of the user. This is in contrast to the lack of user feedback mechanism or imperfect feedback mechanism commonly existing in the prior art, and can better meet the dynamic needs of users in the energy management process and improve the user's satisfaction with the energy management plan and the implementation effect.

[0080] In an embodiment of the present invention, when a data collection request for a smart energy device connected to an energy management terminal is received, data collection is performed on the smart energy device to obtain energy usage data, pattern recognition is performed on the energy usage data to generate an energy usage pattern, the preset energy demand information and the energy usage pattern are integrated to generate an energy management plan for the energy management terminal, and according to the energy management plan, the energy configuration of the smart energy device is optimized through the energy management terminal to achieve optimization of energy usage, thereby realizing the generation and configuration of personalized energy management plans, improving energy utilization efficiency, and meeting the personalized needs of different users.

[0081] Embodiment 2:

[0082] Figure 2 The structure of the energy management optimization device for energy equipment provided by the second embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, including:

[0083] The data collection unit 21 is used to collect data from the smart energy device connected to the energy management terminal to obtain energy usage data when receiving a data collection request for the smart energy device connected to the energy management terminal;

[0084] A pattern generation unit 22, used to perform pattern recognition on energy usage data and generate an energy usage pattern;

[0085] A solution generation unit 23 is used to integrate the preset energy demand information and energy usage mode into a solution to generate an energy management solution for the energy management terminal;

[0086] The configuration optimization unit 24 is used to optimize the energy configuration of the smart energy device through the energy management terminal according to the energy management plan to achieve the optimization of energy use.

[0087] Preferably, the energy management optimization device of the energy equipment of the embodiment of the present invention further includes:

[0088] A feedback information receiving unit, used to receive feedback information from a user on the energy management solution output by the energy management terminal;

[0089] The data re-collection unit is used to trigger the data collection unit to perform data collection on the smart energy device when it is determined based on the feedback information that the energy usage mode and / or energy demand information has changed, so as to generate an energy management plan that is more in line with the user's current situation.

[0090] Preferably, the pattern generating unit 22 comprises:

[0091] A data cleaning unit, used for cleaning energy usage data;

[0092] The data analysis unit is used to perform cluster analysis on the cleaned energy usage data to obtain the energy usage pattern.

[0093] In the embodiment of the present invention, each unit of the energy management optimization device of the energy equipment can be implemented by a corresponding hardware or software unit, and each unit can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not intended to limit the present invention. Specifically, the implementation of each unit can refer to the description of the aforementioned embodiment 1, which will not be repeated here.

[0094] Embodiment three:

[0095] Figure 3 The structure of the energy management intelligent device provided by the third embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown.

[0096] The energy management smart device 3 of the embodiment of the present invention includes a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the embodiment of the energy management optimization method of the energy device described above are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 2 Function of the unit shown.

[0097] In an embodiment of the present invention, when a data collection request for a smart energy device connected to an energy management terminal is received, data collection is performed on the smart energy device to obtain energy usage data, pattern recognition is performed on the energy usage data to generate an energy usage pattern, the preset energy demand information and the energy usage pattern are integrated to generate an energy management plan for the energy management terminal, and according to the energy management plan, the energy configuration of the smart energy device is optimized through the energy management terminal to achieve optimization of energy usage, thereby realizing the generation and configuration of personalized energy management plans, improving energy utilization efficiency, and meeting the personalized needs of different users.

[0098] The energy management smart device of the embodiment of the present invention may be a smart home device. The steps implemented when the processor 30 in the energy management smart device 3 executes the computer program 32 to implement the energy management optimization method of the energy device can refer to the description of the above method embodiment, which will not be repeated here.

[0099] Embodiment 4:

[0100] In an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned energy management optimization method embodiment of the energy device are implemented, for example, Figure 1 Alternatively, when the computer program is executed by a processor, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 2 Function of the unit shown.

[0101] In an embodiment of the present invention, when a data collection request for a smart energy device connected to an energy management terminal is received, data collection is performed on the smart energy device to obtain energy usage data, pattern recognition is performed on the energy usage data to generate an energy usage pattern, the preset energy demand information and the energy usage pattern are integrated to generate an energy management plan for the energy management terminal, and according to the energy management plan, the energy configuration of the smart energy device is optimized through the energy management terminal to achieve optimization of energy usage, thereby realizing the generation and configuration of personalized energy management plans, improving energy utilization efficiency, and meeting the personalized needs of different users.

[0102] The computer-readable storage medium of the embodiment of the present invention may include any entity or device or recording medium capable of carrying computer program code, for example, ROM / RAM, magnetic disk, optical disk, flash memory and other memories.

[0103] 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 and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing energy management of energy equipment, characterized in that: The method comprises the following steps: When receiving a data collection request for a smart energy device connected to the energy management terminal, collecting data from the smart energy device to obtain energy usage data; Performing pattern recognition on the energy usage data to generate an energy usage pattern; Integrate the preset energy demand information and the energy usage pattern to generate an energy management plan for the energy management terminal; According to the energy management solution, the energy configuration of the smart energy device is optimized through the energy management terminal to achieve optimal energy use.

2. The method according to claim 1, characterized in that After the step of optimizing the energy configuration of the smart energy device by the energy management terminal, the method further includes: receiving feedback information from a user on the energy management solution output by the energy management terminal; When it is determined according to the feedback information that the energy usage mode and / or the energy demand information has changed, the process jumps to the step of collecting data from the smart energy device to continue the process, so as to generate an energy management solution that is more in line with the user's current situation.

3. The method according to claim 1, characterized in that The step of performing pattern recognition on the energy usage data to generate an energy usage pattern comprises: Performing data cleaning on the energy usage data; Cluster analysis is performed on the cleaned energy usage data to obtain the energy usage pattern.

4. The method according to claim 1, characterized in that The step of integrating the preset energy demand information and the energy usage mode to generate an energy management plan for the energy management terminal includes: Selecting an energy management policy that matches the energy management scenario currently in which the energy management terminal is located from a preset energy management policy template; According to the energy demand information and the energy usage pattern, the energy management strategy is adjusted and optimized using a preset optimization algorithm to obtain the energy management solution.

5. The method according to claim 4, characterized in that The step of adjusting and optimizing the energy management strategy using a preset optimization algorithm includes: Using a pre-trained linear regression model to predict the energy consumption value of the intelligent energy device under different energy consumption influencing factors, and obtaining energy consumption prediction values ​​corresponding to the energy consumption influencing factors; The energy management strategy is adjusted and optimized according to the energy consumption forecast value.

6. An energy management optimization device for energy equipment, characterized in that: The device comprises: A data collection unit, configured to collect data from a smart energy device connected to the energy management terminal to obtain energy usage data when receiving a data collection request from the smart energy device connected to the energy management terminal; A pattern generation unit, configured to perform pattern recognition on the energy usage data to generate an energy usage pattern; A solution generating unit, configured to integrate the preset energy demand information and the energy usage mode into a solution to generate an energy management solution for the energy management terminal; A configuration optimization unit is used to optimize the energy configuration of the smart energy device through the energy management terminal according to the energy management plan to achieve optimal energy use.

7. The device according to claim 6, characterized in that The device also includes: A feedback information receiving unit, configured to receive feedback information from a user on the energy management solution output by the energy management terminal; The data re-collection unit is used to trigger the data collection unit to perform data collection on the smart energy device when it is determined according to the feedback information that the energy usage mode and / or the energy demand information has changed, so as to generate an energy management plan that is more in line with the user's current situation.

8. The device according to claim 6, characterized in that The pattern generation unit comprises: A data cleaning unit, used for cleaning the energy usage data; The data analysis unit is used to perform cluster analysis on the cleaned energy usage data to obtain the energy usage pattern.

9. An energy management intelligent device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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