Chain park energy consumption load management optimization method based on clustering algorithm
By adopting a load management method based on clustering algorithms, the accurate classification and dynamic optimization of loads within the chain park are achieved, solving the problems of refined and adaptive load management in the chain park, improving energy efficiency and economy, and supporting the balanced access of renewable energy.
Patent Information
- Application Number
- CN202511554699.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-29
- Publication Date
- 2026-02-03
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing chain park load management methods lack refinement, making it difficult to identify and dynamically adjust complex and diverse load characteristics. This results in management strategies that are not targeted enough, have limited optimization effects, and lack adaptive capabilities, making it difficult to cope with renewable energy and load changes.
A load management method based on clustering algorithms is adopted. By collecting, preprocessing and clustering data of electricity consumption units, load pattern categories are generated, personalized optimization strategies are formulated, and dynamic adjustments are made through real-time monitoring and feedback mechanisms. Machine learning and blockchain technologies are integrated to achieve precise management.
It enables precise classification and dynamic optimization of loads within the industrial park, improves energy efficiency and equipment utilization, enhances system adaptability and stability, supports balanced access of renewable energy, reduces management costs and increases economic benefits.
Smart Images
Figure CN121458087A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to a chain park energy load management optimization method based on a clustering algorithm, belonging to the technical field of energy management and smart grid. BACKGROUND
[0002] With the acceleration of urbanization and industrialization, chain park energy units represented by chain commercial parks and industrial parks are increasing. These regional loads are concentrated, with complex and diverse energy consumption modes, and the electricity consumption behaviors of different units (such as office areas, production workshops, and data centers) inside are significantly different. Currently, the load management of such chain parks mainly faces the following challenges: First, the traditional management method is extensive and lacks fine load insight. Most existing park energy management systems only monitor the total load and provide simple peak-valley flat rate guidance, lacking deep mining of the electricity consumption characteristics of internal heterogeneous load units. This "one-size-fits-all" management mode cannot identify which loads are rigid, which are adjustable, and which have potential peak shaving and valley filling capabilities, resulting in weakly targeted management strategies and limited optimization effect.
[0003] Second, the diversity and volatility of load characteristics make optimization difficult. Different users in the chain park have different electricity consumption habits, device start-stop times, and requirements for electricity comfort, resulting in complex and variable load curves. Traditional modeling methods cannot accurately describe this inherent diversity, making the developed optimization strategies often inconsistent with the actual situation, and unable to achieve true load balancing and peak shaving and valley filling.
[0004] Third, existing strategies lack self-adaptation and dynamic adjustment capabilities. Energy supply and demand conditions and user electricity consumption behaviors are dynamic, and static, pre-set management strategies cannot adapt to such changes. When renewable energy is introduced or sudden load changes occur, the system cannot quickly respond and redistribute resources, which may lead to decreased energy efficiency and even unstable power grid.
[0005] Although existing research has attempted to apply data analysis techniques to load management, it has mostly focused on load prediction, and how to directly and efficiently convert load characteristic analysis results into executable and adjustable optimization strategies, and form a closed-loop, self-adaptive management system, especially for chain park energy scenarios with complex internal structures, existing technology has not provided a systematic solution. Therefore, there is an urgent need for an innovative method that can accurately identify load patterns and dynamically and finely manage them accordingly. SUMMARY
[0006] According to the problems described in the background, the present application solves the problem of providing a chain park energy load management optimization method based on a clustering algorithm to solve the problems existing in the above.
[0007] To achieve the above object, the present application provides the following technical solution: a chain park energy load management optimization method based on a clustering algorithm is provided, comprising the following steps: (1) Collecting load data of multiple electricity-consuming units in a chain park energy system, the load data including historical electricity consumption, real-time electricity power, electricity time series and environmental parameters; (2) Preprocessing the load data to eliminate noise and outliers, and generating a standardized load data set; (3) Applying a clustering algorithm to perform clustering analysis on the standardized load data set, and dividing the multiple electricity-consuming units into multiple load mode categories, each load mode category representing a set of electricity-consuming units with similar electricity consumption characteristics; (4) Based on the results of the clustering analysis, extracting load characteristic indicators of each load mode category, including peak value, valley value, volatility and electricity consumption trend of the load curve; (5) Generating a load management optimization strategy for each load mode category according to the load characteristic indicators, the optimization strategy including load scheduling, demand response and energy efficiency improvement measures; (6) Implementing the load management optimization strategy into the chain park energy system, and controlling the electricity-consuming units in real time through control equipment to achieve load balancing and energy efficiency improvement; (7) Monitoring the implementation effect of the load management optimization strategy, and dynamically adjusting the clustering analysis or optimization strategy according to the monitoring data to continuously optimize the chain park energy load management.
[0008] Preferably, the step (1) of collecting load data includes real-time acquisition of electricity consumption data through intelligent electric meters, sensor networks and energy management systems, and storing the load data in a distributed database; The environmental parameters include temperature, humidity and seasonal factors, which are used to associate load changes with environmental conditions.
[0009] Preferably, the step (2) of preprocessing the load data includes the following steps: (2.1) Cleaning the load data to remove outliers caused by equipment failure or transmission errors; (2.2) Normalizing the cleaned load data to convert the data to a uniform scale to eliminate dimensional effects; (2.3) Extracting features from the normalized load data to generate a feature vector including electricity consumption period, load change rate and peak value occurrence frequency as the standardized load data set.
[0010] Preferably, the clustering algorithm in step (3) is a density-based clustering algorithm or a divisive clustering algorithm; The density-based clustering algorithm can automatically identify noise points and handle non-spherical clusters; The partition-based clustering algorithm assigns the power consumption units to a predetermined number of clusters through iterative optimization; The cluster analysis further includes using the silhouette coefficient or elbow rule to evaluate the clustering quality, and adjusting the clustering parameters according to the evaluation results.
[0011] Preferably, the extraction of load characteristic indicators in step (4) includes calculating the average load curve, load fluctuation variance and load period distribution of each load mode category, and identifying high load categories, low load categories and adjustable load categories based on the load characteristic indicators, wherein the high load categories correspond to concentrated power consumption periods, the low load categories correspond to dispersed power consumption periods, and the adjustable load categories correspond to power consumption units that can achieve peak shaving and valley filling through scheduling.
[0012] Preferably, the generation of load management optimization strategies in step (5) includes: For high load categories, develop demand response strategies based on time-of-use electricity prices to encourage power consumption units to consume electricity during valley periods; For low load categories, develop energy efficiency monitoring strategies to reduce basic energy consumption through device upgrades or behavior guidance; For adjustable load categories, develop load shifting strategies to schedule part of the power consumption tasks to low load periods; The optimization strategy also integrates a machine learning model to predict load changes and adaptively adjust strategy parameters.
[0013] Preferably, the implementation of load management optimization strategies into the chain park energy system in step (6) includes sending control instructions to power consumption units through an energy management platform, the control instructions including adjusting air conditioning temperature, turning off unnecessary devices or starting energy storage devices; The real-time regulation is based on Internet of Things technology, realizing two-way communication between power consumption units and central control system, and priority scheduling according to power grid state and user preferences.
[0014] Preferably, the monitoring of implementation effects in step (7) includes collecting load data, energy efficiency indicators and user feedback after the implementation of strategies, and calculating load balancing degree, peak reduction rate and energy saving rate; The dynamic adjustment includes: if the monitoring data shows that the optimization effect is not as expected, re-executing steps (3) to (6) to update the cluster analysis or optimization strategy; The dynamic adjustment also introduces a feedback mechanism to optimize the clustering algorithm parameters based on historical data.
[0015] Preferably, it further comprises step (8): recording the implementation process and results of the load management optimization strategy by using blockchain technology, ensuring data transparency and non-tamperability, and providing users with traceable energy efficiency reports; The step (8) is performed after step (7) and integrated with the settlement module of the chain park energy system to support energy transactions based on the optimization results.
[0016] Preferably, the chain park energy system is an energy network composed of multiple parks or chain facilities, and the power consumption units include industrial equipment, commercial facilities and residential power consumption terminals. The method also supports multi-energy collaborative management, integrates renewable energy sources and energy storage systems, and optimizes energy distribution based on clustering results to improve the reliability and economy of the entire chain park energy system.
[0017] The beneficial effects of the present application are: 1. By using clustering algorithm to mine massive load data, the present application can automatically and accurately divide power consumption units into different load mode categories. This is equivalent to providing managers with a clear "load portrait", enabling them to deeply understand the complex power consumption behavior in the park, and thus completely changing the traditional "one-size-fits-all" extensive management mode.
[0018] 2. Based on accurate load classification, the present application can develop and implement optimization strategies that are highly matched with the characteristics of each load category. For example, precise demand response is implemented for high load categories, and effective peak shaving and valley filling is performed for adjustable loads. This classification and strategy greatly improve the effectiveness of the strategy, thereby significantly reducing system peak load, improving valley power consumption rate, optimizing load curve shape, and ultimately improving overall energy efficiency and equipment utilization efficiency.
[0019] 3. The present application introduces a dynamic monitoring and feedback adjustment mechanism. By continuously tracking the implementation effect of the strategy and reanalyzing the clustering or adjusting the strategy parameters accordingly, the system forms a closed-loop optimization loop. This makes the management method adaptable to long-term evolution and short-term fluctuations of load characteristics, ensuring the sustainability and stability of the optimization effect, especially when accessing fluctuating renewable energy.
[0020] 4. The present application applies the data-driven concept throughout, from clustering analysis to strategy generation (which can integrate machine learning prediction), to blockchain-based reliable recording, fully improving the automation and intelligence of management. This not only reduces the cost of manual management, but also brings direct economic benefits to chain park operators through improving energy efficiency, participating in demand response projects, delaying grid expansion investment, etc.
[0021] 5. Through a deep understanding and flexible control of load characteristics, this invention lays the foundation for the large-scale integration of renewable energy sources such as photovoltaics and wind power in industrial parks. The system can prioritize the mobilization of adjustable loads to match the power generation output of renewable energy, thereby effectively mitigating the volatility of renewable energy, increasing the proportion of green electricity consumption in the park, and supporting the construction of a cleaner and more efficient regional energy system. Attached Figure Description
[0022] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation
[0023] The embodiments of the present invention will be further described below with reference to the accompanying drawings: Example 1: As Figure 1 As shown, this invention provides a chain-based energy load management optimization method based on clustering algorithms, comprising the following steps: (1) Collect load data of multiple power consumption units in the energy consumption system of the chain park, the load data including historical power consumption, real-time power consumption, power consumption time series and environmental parameters; Load data from multiple power-consuming units, such as historical power consumption, real-time power, time series data, and environmental parameters, are collected to provide basic data for subsequent analysis.
[0024] (2) The load data is preprocessed to eliminate noise and outliers and generate a standardized load dataset; The data is preprocessed to remove noise and outliers, and a standardized dataset is generated to ensure data quality and consistency.
[0025] (3) Apply clustering algorithm to perform clustering analysis on the standardized load dataset, and divide the multiple power consumption units into multiple load pattern categories. Each load pattern category represents a set of power consumption units with similar power consumption characteristics. Clustering algorithms are applied to analyze standardized data, dividing electricity-consuming units into multiple load pattern categories with similar electricity consumption characteristics, thereby classifying electricity consumption behavior.
[0026] (4) Based on the results of the cluster analysis, extract the load characteristic indicators for each load pattern category, including the peak value, valley value, volatility and electricity consumption trend of the load curve; Based on the clustering results, load characteristic indicators (such as peak, valley, and volatility) of each category are extracted to quantify electricity consumption characteristics.
[0027] (5) Based on the load characteristic indicators, generate load management optimization strategies for each load mode category, the optimization strategies including load scheduling, demand response and energy efficiency improvement measures; According to the characteristic index, a load management optimization strategy (such as load scheduling, demand response, etc.) for each category is generated to realize individualized management.
[0028] (6) The load management optimization strategy is implemented into the chain park energy system, and the power consumption unit is real-time regulated by the control device to realize load balancing and energy efficiency improvement; The strategy is implemented into the energy system, and the control device is used for real-time regulation to achieve the goal of load balancing and energy efficiency improvement.
[0029] (7) Monitor the implementation effect of the load management optimization strategy, and dynamically adjust the clustering analysis or optimization strategy according to the monitoring data to continuously optimize the chain park energy load management.
[0030] Monitor the effect of the strategy and dynamically adjust the clustering analysis or strategy according to the feedback to achieve continuous optimization.
[0031] The load data collected in step (1) includes real-time acquisition of power consumption data through intelligent electric meters, sensor networks and energy management systems, and the load data is stored in a distributed database; the environmental parameters include temperature, humidity and seasonal factors, which are used to associate load changes with environmental conditions.
[0032] The specified load data is acquired in real time through intelligent electric meters, sensor networks and energy management systems, and stored in a distributed database, ensuring the reliability and real-time nature of the data source, refining the specific means of data collection and the purpose of environmental parameters, highlighting the importance of data integration and external factor analysis, and providing more comprehensive input for clustering analysis.
[0033] The pre-processing of the load data in step (2) includes the following steps: (2.1) Clean the load data to remove abnormal values caused by equipment failure or transmission errors; Data cleaning removes abnormal values caused by equipment failure or transmission errors to improve data accuracy.
[0034] (2.2) Normalize the cleaned load data to convert the data to a uniform scale to eliminate dimensional effects; Normalization converts data to a uniform scale to eliminate dimensional effects and facilitate subsequent algorithm processing.
[0035] (2.3) Feature extraction is performed on the normalized load data to generate a feature vector including power consumption period, load change rate and peak value occurrence frequency, which is used as the standardized load data set.
[0036] Feature extraction generates feature vectors such as power consumption period, load change rate, etc., to form a standardized data set for clustering analysis.
[0037] The clustering algorithm in step (3) is a density-based clustering algorithm or a partition-based clustering algorithm; the density-based clustering algorithm can automatically identify noise points and handle non-spherical clusters; the partition-based clustering algorithm assigns the power consumption units to a predetermined number of clusters through iterative optimization; the cluster analysis also includes using the silhouette coefficient or elbow rule to evaluate the clustering quality, and adjusting the clustering parameters according to the evaluation results.
[0038] The clustering quality evaluation uses the silhouette coefficient or elbow rule, and adjusts the clustering parameters according to the evaluation results, ensuring the rationality of the clustering results.
[0039] The load characteristic index extraction in step (4) includes calculating the average load curve, load fluctuation variance and power consumption period distribution of each load mode category, and identifying high load categories, low load categories and adjustable load categories based on the load characteristic index, wherein the high load category corresponds to a power consumption concentrated period, the low load category corresponds to a power consumption dispersed period, and the adjustable load category corresponds to a power consumption unit that can achieve peak load shifting through scheduling.
[0040] The specific content of the characteristic index and the definition of the load category are clarified, providing classification basis for subsequent strategy generation and enhancing the pertinence of management.
[0041] The generation of the load management optimization strategy in step (5) includes: for the high load category, developing a demand response strategy based on time electricity price, encouraging power consumption units to consume electricity in the valley period to reduce peak load; for the low load category, developing an energy efficiency monitoring strategy to reduce basic energy consumption through equipment upgrade or behavior guidance; for the adjustable load category, developing a load shifting strategy to schedule part of the power consumption task to the low load period to achieve peak load shifting; the optimization strategy also integrates a machine learning model to predict load changes and adaptively adjust strategy parameters, improving the intelligence of the strategy.
[0042] The implementation of the load management optimization strategy into the chain park energy system in step (6) includes: sending control instructions to power consumption units through an energy management platform, the control instructions including adjusting air conditioning temperature, turning off unnecessary equipment or starting energy storage devices; the real-time regulation is based on Internet of Things technology, realizing two-way communication between power consumption units and central control system, and priority scheduling according to power grid state and user preference.
[0043] Step (6) highlights the application of Internet of Things technology, ensuring the real-time of regulation and user participation, and improving the response speed of the system.
[0044] The monitoring effect in step (7) includes collecting load data, energy efficiency indicators and user feedback after the implementation of the strategy, and calculating load balancing, peak reduction rate and energy saving rate; the dynamic adjustment includes: if the monitoring data shows that the optimization effect is not as expected, re-executing steps (3) to (6) to update the clustering analysis or optimization strategy; the dynamic adjustment also introduces a feedback mechanism to optimize the clustering algorithm parameters based on historical data to achieve continuous improvement.
[0045] It also includes step (8): recording the implementation process and results of the load management optimization strategy on the blockchain technology, ensuring data transparency and tamper resistance, and providing users with traceable energy efficiency reports; step (8) is executed after step (7) and integrated with the settlement module of the chain park energy system to support energy transactions based on optimization results.
[0046] Enhances data security and trust, while expanding the application scenarios of the method, supporting energy transactions and financial settlements.
[0047] The chain park energy system is an energy network composed of multiple parks or chain facilities, and the power consumption unit includes industrial equipment, commercial facilities and residential power consumption terminals; the method also supports multi-energy collaborative management, integrates renewable energy sources and energy storage systems, and optimizes energy distribution based on clustering results to improve the reliability and economy of the entire chain park energy system.
[0048] Clarifies the application scenarios and extended functions of the method, highlights multi-energy integration and overall optimization, and improves the practicality and scale effect of the method.
Claims
1. A chain-based energy load management optimization method based on clustering algorithm, characterized in that, Includes the following steps: (1) Collect load data of multiple power consumption units in the energy consumption system of the chain park, the load data including historical power consumption, real-time power consumption, power consumption time series and environmental parameters; (2) The load data is preprocessed to eliminate noise and outliers and generate a standardized load dataset; (3) Apply clustering algorithm to perform clustering analysis on the standardized load dataset, and divide the multiple power consumption units into multiple load pattern categories. Each load pattern category represents a set of power consumption units with similar power consumption characteristics. (4) Based on the results of the cluster analysis, extract the load characteristic indicators for each load pattern category, including the peak value, valley value, volatility and electricity consumption trend of the load curve; (5) Based on the load characteristic indicators, generate load management optimization strategies for each load mode category, the optimization strategies including load scheduling, demand response and energy efficiency improvement measures; (6) Implement the load management optimization strategy into the energy consumption system of the chain park, and use control equipment to regulate the power consumption units in real time to achieve load balancing and energy efficiency improvement; (7) Monitor the implementation effect of the load management optimization strategy and dynamically adjust the cluster analysis or optimization strategy according to the monitoring data to continuously optimize the energy load management of the chain park.
2. The energy load management optimization method for a chain-like environment based on clustering algorithm according to claim 1, characterized in that, The load data collection in step (1) includes acquiring electricity consumption data in real time through smart meters, sensor networks and energy management systems, and storing the load data in a distributed database; The environmental parameters include temperature, humidity, and seasonal factors, which are used to correlate load changes with environmental conditions.
3. The energy load management optimization method for a chain-like environment based on clustering algorithm according to claim 1, characterized in that, The preprocessing of load data in step (2) includes the following steps: (2.1) Clean the load data to remove abnormal values caused by equipment failure or transmission errors; (2.2) Normalize the cleaned load data to convert the data to a uniform scale in order to eliminate the influence of dimensions; (2.3) Extract features from the normalized load data to generate feature vectors including electricity consumption time period, load change rate and peak occurrence frequency, as the standardized load dataset.
4. The chain-based energy load management optimization method based on clustering algorithm according to claim 1, characterized in that, The clustering algorithm in step (3) is a density-based clustering algorithm or a partitioning clustering algorithm; The density-based clustering algorithm can automatically identify noise points and handle non-spherical clusters; The partitioning clustering algorithm assigns electricity-consuming units to a predetermined number of clusters through iterative optimization. The cluster analysis also includes using the silhouette coefficient or elbow rule to assess cluster quality and adjusting cluster parameters based on the assessment results.
5. The energy load management optimization method for a clustered ecosystem based on clustering algorithm according to claim 1, characterized in that, The step (4) of extracting load characteristic indicators includes: calculating the average load curve, load fluctuation variance and electricity consumption period distribution for each load mode category, and identifying high load category, low load category and adjustable load category based on the load characteristic indicators. The high load category corresponds to the concentrated electricity consumption period, the low load category corresponds to the dispersed electricity consumption period, and the adjustable load category corresponds to the electricity consumption unit that can achieve peak shaving and valley filling through scheduling.
6. The energy load management optimization method for a chain circle based on clustering algorithm according to claim 1, characterized in that, The load management optimization strategy generated in step (5) includes: For high-load categories, develop time-based electricity pricing-based demand response strategies to encourage electricity users to consume electricity during off-peak hours; For low-load categories, develop energy efficiency monitoring strategies to reduce basic energy consumption through equipment upgrades or behavioral guidance; For adjustable load categories, develop load transfer strategies to schedule some electricity consumption tasks to low-load periods; The optimization strategy also integrates machine learning models to predict load changes and adaptively adjust strategy parameters.
7. The energy load management optimization method for a chain-like environment based on clustering algorithm according to claim 1, characterized in that, The implementation of the load management optimization strategy into the energy consumption system of the chain park in step (6) includes: sending control commands to the power consumption unit through the energy management platform. The control commands include adjusting the air conditioning temperature, turning off unnecessary equipment, or starting the energy storage device. The real-time control is based on Internet of Things (IoT) technology, enabling two-way communication between the power-consuming unit and the central control system, and prioritizing scheduling according to the power grid status and user preferences.
8. The energy load management optimization method for a chain circle based on clustering algorithm according to claim 1, characterized in that, The monitoring of the implementation effect in step (7) includes: collecting load data, energy efficiency indicators and user feedback after the implementation of the strategy, and calculating load balance, peak reduction rate and energy saving rate; The dynamic adjustment includes: if the monitoring data shows that the optimization effect is not as expected, then re-execute steps (3) to (6) and update the cluster analysis or optimization strategy; The dynamic adjustment also introduces a feedback mechanism to optimize the clustering algorithm parameters based on historical data.
9. The energy load management optimization method for a chain-like environment based on clustering algorithm according to claim 1, characterized in that, It also includes step (8): using blockchain technology to record the implementation process and results of the load management optimization strategy, ensuring data transparency and immutability, and providing users with traceable energy efficiency reports; Step (8) is performed after step (7) and is integrated with the settlement module of the Chain Park Energy System to support energy trading based on optimization results.
10. The energy load management optimization method for a chain circle based on clustering algorithm according to claim 1, characterized in that, The chain park energy system is an energy network composed of multiple parks or chain facilities, and the power consumption units include industrial equipment, commercial facilities and residential power terminals; The method also supports multi-energy collaborative management, integrates renewable energy sources and energy storage systems, and optimizes energy allocation based on clustering results to improve the reliability and economy of the entire industrial park energy system.