Power demand prediction and coal supply optimization system based on big data analysis

Abstract

The invention discloses a power demand prediction and coal supply optimization system based on big data analysis, which remarkably improves the precision of power demand prediction by integrating multi-dimensional real-time data and applying a deep learning algorithm. Meanwhile, the coal supply chain is optimized in combination with mixed integer linear programming and a genetic algorithm, cost minimization is achieved, and dynamic adjustment is conducted through a particle swarm optimization algorithm under the emergency condition. And a feedback learning module of the system performs adaptive adjustment by using a reinforcement learning mechanism, so that the real-time response capability is improved. In addition, the intelligent decision making module provides data visualization and regulation and control suggestions so as to support accurate decision making. On the whole, the problems of inaccurate power demand prediction, insufficient supply chain scheduling response and weak intelligent decision support are effectively solved, and efficient resource scheduling and power supply and demand balance in a complex scene are realized.

Description

Technical Field

[0001] The present application relates to the technical field of power demand forecasting and coal supply optimization, and in particular to a power demand forecasting and coal supply optimization system based on big data analysis. Background Art

[0002] With the continuous growth of electricity demand and the complexity of energy supply chain, the management of power system and coal supply chain faces many challenges. The current electricity demand is highly volatile and uncertain. Especially in the case of seasonal peak electricity demand, holidays or emergencies (such as extreme weather, policy changes), traditional electricity demand forecasting methods are difficult to accurately predict the changes in electricity demand, resulting in low efficiency in power resource scheduling. In addition, as the core resource of thermal power generation, the coal supply chain has a high degree of coordination in production, transportation and inventory scheduling.

[0003] In existing technologies, electricity demand forecasting is mostly based on traditional statistical methods or simple machine learning models, which cannot fully utilize multi-dimensional data (such as real-time electricity load, meteorological data, policy information, etc.) to improve forecasting accuracy. The optimization of the coal supply chain usually relies on static planning or a single algorithm, lacking flexible dynamic adjustment capabilities. These deficiencies may lead to resource waste, inventory backlogs or supply shortages in the coal supply chain, thereby exacerbating the imbalance between supply and demand and affecting the stability and economy of the power system.

[0004] Therefore, there is an urgent need for a system that can integrate big data technology, deep learning algorithms and intelligent optimization scheduling to cope with the complexity of power demand forecasting and coal supply optimization, improve the prediction accuracy of the power system and the response speed of resource scheduling, and thus achieve accurate matching of supply and demand and efficient utilization of coal resources. Summary of the invention

[0005] The present application provides a power demand forecasting and coal supply optimization system based on big data analysis, aiming to solve the problem that the existing technology leads to resource waste, inventory backlog or supply shortage in the coal supply chain, thereby aggravating the imbalance between supply and demand and affecting the stability and economy of the power system.

[0006] A power demand forecasting and coal supply optimization system based on big data analysis, the system comprising:

[0007] The data collection module is used to collect electricity consumption data from power user terminals through IoT devices, obtain meteorological data, policy information, holiday arrangements and external data of economic indicators through crawlers and API interfaces, and build a data input set;

[0008] Big data storage and processing module, which stores and processes data through distributed database and data processing framework, performs data cleaning, dimension reduction and feature extraction, and generates input data sets for power demand forecasting module and coal supply optimization module;

[0009] The power demand forecasting module performs time series forecasting on power load and external influencing factor data based on the long short-term memory network model or the gated recurrent unit model to generate short-term and long-term power demand forecasting results;

[0010] Coal supply optimization module, which optimizes coal production, inventory and transportation scheduling based on mixed integer linear programming and genetic algorithm;

[0011] Intelligent decision-making module, which analyzes the power demand forecast results and coal supply optimization results, generates dynamic control suggestions, and displays the supply and demand status, inventory status and scheduling plan through a visual interface;

[0012] The feedback learning module performs error analysis and model iterative optimization on the prediction and optimization results through the reinforcement learning mechanism.

[0013] In the above solution, optionally, the data acquisition module includes:

[0014] The power data collection submodule collects real-time power consumption and time-sharing load data of power user terminals through IoT devices and smart meters;

[0015] The external data collection submodule obtains unstructured data such as policy information and holiday schedules through crawlers, and collects meteorological data and economic indicators through API interfaces to form an input feature set for power demand forecasting.

[0016] In the above solution, optionally, the big data storage and processing module includes:

[0017] The distributed storage submodule uses a distributed file system to store raw data and a NoSQL database to store processed structured data.

[0018] The data cleaning and dimensionality reduction submodule performs outlier detection, missing value filling and deduplication operations, and uses principal component analysis to reduce the dimensionality of external data and optimize feature extraction efficiency.

[0019] In the above solution, optionally, the power demand prediction module includes:

[0020] The time series forecasting model submodule trains the power load and external data based on the LSTM or GRU model to generate short-term and long-term power demand forecasts;

[0021] The model optimization submodule improves the generalization ability of the model through stratified K-fold cross-validation, and uses Bayesian optimization or genetic algorithm to tune the model hyperparameters to ensure the accuracy of the prediction results.

[0022] In the above solution, optionally, the coal supply optimization module includes:

[0023] The optimization algorithm submodule optimizes the scheduling of the coal supply chain based on the MILP algorithm, with the goal of minimizing inventory and transportation costs;

[0024] The solution generation and screening submodule uses genetic algorithms to generate alternative coal supply chain solutions and uses particle swarm optimization algorithms to screen the optimal solution to meet the elastic response requirements of demand fluctuations.

[0025] In the above solution, optionally, the intelligent decision-making module includes:

[0026] The rule base analysis submodule generates control suggestions based on power demand forecast and supply optimization results, including inventory replenishment, transportation optimization and scheduling plans;

[0027] The data visualization submodule displays supply and demand forecast trends, inventory status, and scheduling suggestions through a visual interface, allowing managers to monitor the supply and demand balance in real time and make manual adjustments.

[0028] In the above solution, optionally, the feedback learning module includes:

[0029] The error analysis submodule performs real-time detection and feedback of errors in prediction and optimization results;

[0030] The reinforcement learning submodule dynamically adjusts model parameters based on error analysis results through the Q-learning algorithm to maximize the accuracy of power demand forecasting and supply chain scheduling;

[0031] The emergency response submodule automatically triggers local optimization when emergencies cause demand fluctuations to enhance the system's rapid adaptability.

[0032] In the above scheme, optionally, the training goal of the time series prediction model submodule is to minimize the power load prediction error, output short-term prediction results and long-term prediction results, and automatically trigger the adjustment of short-term prediction when the actual demand deviation exceeds the preset threshold to optimize the prediction accuracy.

[0033] In the above scheme, optionally, the optimization algorithm submodule comprehensively considers transportation time, inventory capacity and energy price when generating the coal supply plan to ensure the economy and reliability of coal supply.

[0034] In the above scheme, optionally, the reinforcement learning submodule adjusts the hyperparameters of the power demand forecasting model through the Q-learning algorithm to improve the accuracy of the forecasting results, and automatically iterates and optimizes when the error increases to achieve dynamic updating of the model.

[0035] Compared with the prior art, this application has at least the following beneficial effects:

[0036] Based on further analysis and research on the problems of the prior art, this application recognizes that the prior art leads to resource waste, inventory backlogs or supply shortages in the coal supply chain, thereby exacerbating the imbalance between supply and demand and affecting the stability and economy of the power system. By integrating multi-dimensional real-time data and applying deep learning algorithms, the accuracy of power demand forecasting has been significantly improved. At the same time, the coal supply chain is optimized by combining mixed integer linear programming and genetic algorithms to minimize costs, and dynamic adjustments are made through particle swarm optimization algorithms in emergency situations. The feedback learning module of the system uses a reinforcement learning mechanism to make adaptive adjustments to improve real-time response capabilities. In addition, the intelligent decision-making module provides data visualization and regulation recommendations to support accurate decision-making. On the whole, this application effectively solves the problems of inaccurate power demand forecasting, insufficient supply chain scheduling response, and weak intelligent decision-making support, and achieves efficient resource scheduling and power supply and demand balance in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A block diagram of the module architecture of an electricity demand forecasting and coal supply optimization device based on big data analysis provided in one embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application 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 application and are not used to limit the present application.

[0039] In one embodiment, Figure 1 As shown, a power demand forecasting and coal supply optimization system based on big data analysis is provided, which includes the following program modules: a data acquisition module, which is used to collect power consumption data of power user terminals through Internet of Things devices, obtain meteorological data, policy information, holiday arrangements and external data of economic indicators through crawlers and API interfaces, and construct a data input set;

[0040] Big data storage and processing module, which stores and processes data through distributed database and data processing framework, performs data cleaning, dimension reduction and feature extraction, and generates input data sets for power demand forecasting module and coal supply optimization module;

[0041] The power demand forecasting module performs time series forecasting on power load and external influencing factor data based on the long short-term memory network model or the gated recurrent unit model to generate short-term and long-term power demand forecasting results;

[0042] Coal supply optimization module, which optimizes coal production, inventory and transportation scheduling based on mixed integer linear programming and genetic algorithm;

[0043] Intelligent decision-making module, which analyzes the power demand forecast results and coal supply optimization results, generates dynamic control suggestions, and displays the supply and demand status, inventory status and scheduling plan through a visual interface;

[0044] The feedback learning module performs error analysis and model iterative optimization on the prediction and optimization results through the reinforcement learning mechanism.

[0045] This embodiment integrates multi-dimensional real-time data through the data acquisition module and the power demand forecasting module, including power load, meteorological data, policy information and economic indicators, and conducts a comprehensive analysis of various factors affecting power demand. Deep learning algorithms such as long short-term memory network (LSTM) and gated recurrent unit (GRU) are used to achieve efficient processing of complex time series data in the power demand forecasting process, significantly improving the accuracy and reliability of power demand forecasting and meeting the forecasting needs in various scenarios.

[0046] This embodiment uses the coal supply optimization module to use mixed integer linear programming (MILP) and genetic algorithms to perform intelligent optimization of the coal supply chain and generate more economical and efficient production, inventory and transportation scheduling plans. It minimizes coal inventory and transportation costs while meeting electricity demand, and dynamically adjusts the scheduling plan through the particle swarm optimization algorithm (PSO) in emergencies to ensure the stability and flexibility of coal supply.

[0047] The feedback learning module of this embodiment analyzes the prediction error and optimization deviation through the reinforcement learning mechanism to achieve adaptive adjustment of the model. The hyperparameter setting of the power demand forecasting model is optimized through the Q-learning algorithm, so that the system can quickly adapt to the fluctuation of power demand and trigger local optimization when the demand deviation increases due to emergencies (such as extreme weather or policy changes), thereby improving the real-time response capability of the system.

[0048] The intelligent decision-making module of this embodiment provides control suggestions and visual display of multi-dimensional data by analyzing the power demand forecast results and coal supply chain scheduling plans. Managers can obtain dynamic control suggestions generated by the system by real-time monitoring of supply and demand balance and scheduling status, and make manual adjustments according to demand, so as to achieve supply and demand matching and decision optimization more accurately. The system of this application effectively overcomes the problems of the prior art in terms of insufficient accuracy of power demand forecasting, lack of responsiveness of coal supply chain scheduling, and weak support for intelligent decision-making, and realizes efficient resource scheduling and power supply and demand balance in complex scenarios.

[0049] In one embodiment, the data acquisition module includes:

[0050] The power data collection submodule collects real-time power consumption and time-sharing load data of power user terminals through IoT devices and smart meters;

[0051] The external data collection submodule obtains unstructured data such as policy information and holiday schedules through crawlers, and collects meteorological data and economic indicators through API interfaces to form an input feature set for power demand forecasting.

[0052] The "data acquisition module" described in this embodiment collects power data and external influencing factor data respectively through two sub-modules, thereby achieving comprehensive multi-dimensional coverage of factors affecting power demand, and effectively improving the accuracy and adaptability of power demand forecasting. Specifically, the fluctuation of power demand is not only affected by the real-time load changes of users, but also by factors such as meteorological conditions, policy changes, holiday effects, and economic development levels. Traditional power demand forecasting methods often only use historical data on power loads, while ignoring these external influencing factors, resulting in easy deviations in forecast results. Therefore, the "data acquisition module" of this application constructs a comprehensive multi-dimensional data input set by refining the functions of the sub-modules, providing richer information support for subsequent forecasting and optimization. The specific technical effects are as follows:

[0053] The power data acquisition submodule monitors the power consumption and time-sharing load data of user terminals in real time through IoT devices and smart meters. With the introduction of IoT devices, the system can collect real-time data at a higher frequency, thereby quickly responding to immediate fluctuations in power consumption. Real-time acquisition ensures the timeliness of input data and provides the power demand forecasting module with the latest and most accurate load information, so that the forecast results can more accurately reflect current power consumption trends and demand changes. Compared with traditional periodic data collection methods, the data acquisition module of this application effectively shortens the time delay of data transmission and greatly improves the response speed of the system under load fluctuations.

[0054] The external data acquisition submodule obtains external factor data such as meteorological data, policy information, holiday arrangements and economic indicators through crawler technology and API interfaces. The design of this module enables the system to dynamically obtain non-power load factors that affect power demand. For example, temperature and humidity in meteorological data will directly affect air conditioning load and heating load, while policy information and holiday effects will significantly change short-term power demand. By updating these data in real time through crawler technology and API interfaces, the system can adjust the input of the prediction model when external conditions change, improve the ability to capture actual demand, and ensure the effectiveness and accuracy of the prediction results.

[0055] Through the comprehensive collection of power data and external data, the system can obtain a more comprehensive data set of factors affecting power demand, providing multi-dimensional data support for the power demand forecasting module. Multi-dimensional data input not only enhances the adaptability of the forecasting model, but also improves the generalization ability of the forecast. The system can flexibly combine historical load, weather, policy and other factors to predict future demand, which is particularly suitable for power demand forecasting in complex scenarios, so that it can still maintain high accuracy when power demand fluctuates greatly or there is sudden demand.

[0056] The data collection module of this embodiment provides a high-quality input data set for the power demand forecasting module by collecting all-round power load data and external factor data. Its real-time, breadth and multi-dimensional characteristics effectively improve the accuracy and adaptability of power demand forecasting, and make up for the defects of insufficient data collection, poor real-time performance and single reference factors in the existing technology.

[0057] In one embodiment, the big data storage and processing module includes:

[0058] The distributed storage submodule uses a distributed file system to store raw data and a NoSQL database to store processed structured data.

[0059] The data cleaning and dimensionality reduction submodule performs outlier detection, missing value filling and deduplication operations, and uses principal component analysis to reduce the dimensionality of external data and optimize feature extraction efficiency.

[0060] The "big data storage and processing module" described in this embodiment provides efficient and reliable data management and processing support for the system's power demand forecasting and coal supply optimization through distributed storage and data cleaning and dimensionality reduction processing technology. Specifically, this module has made innovative designs in storage architecture and data preprocessing, significantly improving the storage efficiency, computing efficiency and processing accuracy of data. The specific technical effects are as follows: This module uses a distributed file system (HDFS) to store raw data, and uses a NoSQL database to store processed structured data. This distributed storage architecture ensures the system's high concurrent data access capability, and can ensure the stability and processing efficiency of the system when the amount of data increases significantly. By storing raw data in a distributed file system, the system can store and manage large-scale data in parallel on different nodes, avoiding the bottleneck problem of single-node storage. The use of NoSQL database provides a fast query function for structured data, which is particularly suitable for structured data storage of power load and external factors. The distributed design of this module effectively improves storage efficiency and data access speed, and provides efficient data support for power demand forecasting and supply chain optimization modules.

[0061] The data cleaning and dimensionality reduction submodule effectively ensures the accuracy and consistency of input data through outlier detection, missing value filling and deduplication operations. There is often noise or abnormal data in the power load and external factor data, and uncleaned data may lead to deviations in prediction results. This module uses data cleaning technology to filter and process the original data, effectively remove noise data and fill missing values, thereby improving the reliability and integrity of the data. This data preprocessing process greatly improves the input quality of the prediction and optimization model, ensuring that the model can generate reliable prediction results based on more realistic data.

[0062] Dimensionality reduction plays an important role in system data preprocessing. This module uses the principal component analysis (PCA) method to reduce the dimensionality of external factor data. By removing redundant features and retaining key features, it effectively reduces the dimension of the model input data, thereby reducing the computational complexity and improving the model's operational efficiency. The reduced-dimensional data can reduce the impact of redundant features on model calculations during the calculation process, improve the model's operational efficiency and prediction speed, and is particularly suitable for batch processing and streaming processing of large-scale data.

[0063] The big data storage and processing module provides efficient data management and processing support for the system through distributed storage and data cleaning and dimensionality reduction technology. Its distributed architecture ensures storage and computing efficiency in a big data environment, data cleaning technology improves data accuracy and reliability, and dimensionality reduction technology optimizes the computing efficiency of the model. The design of this module not only effectively solves the problem of low data storage and processing efficiency in existing technologies, but also provides high-quality input data for subsequent power demand forecasting and coal supply optimization, enhancing the overall performance of the system.

[0064] In one embodiment, the power demand prediction module includes:

[0065] The time series forecasting model submodule trains the power load and external data based on the LSTM or GRU model to generate short-term and long-term power demand forecasts;

[0066] The model optimization submodule improves the generalization ability of the model through stratified K-fold cross-validation, and uses Bayesian optimization or genetic algorithm to tune the model hyperparameters to ensure the accuracy of the prediction results.

[0067] The "power demand forecasting module" of this embodiment uses the long short-term memory network (LSTM) and gated recurrent unit (GRU) of deep learning to perform time series forecasting of power demand, which significantly improves the accuracy and adaptability of power demand forecasting. The specific technical effects are as follows: This module uses the LSTM network to perform deep learning training on power load data and external factor data. LSTM, as a deep learning model suitable for time series data, can effectively memorize long-term series information and process long-term dependencies. Since power demand usually has complex time series fluctuation characteristics, traditional statistical methods or machine learning models are difficult to capture long-term change trends. Through its memory unit and gated structure, LSTM deeply processes load data with a long time span, allowing the model to more accurately capture the trend changes in power demand, thereby improving the accuracy of power demand forecasting.

[0068] The GRU model of this module is particularly suitable for short-term prediction scenarios. Compared with the LSTM structure, the GRU is simpler and can reduce the computational complexity and speed up the prediction speed while ensuring the prediction accuracy. For short-term fluctuations in power demand (such as intraday load changes), the GRU can quickly respond to load fluctuations through its efficient memory and forget mechanism, improving the real-time and accuracy of short-term predictions. This advantage makes the system excellent in real-time load prediction and rapid response, especially suitable for real-time demand prediction scenarios with high-frequency sampling.

[0069] This module improves the generalization ability of the model through stratified K-fold cross-validation, and uses Bayesian optimization or genetic algorithm to tune the model's hyperparameters, making the prediction model more stable and robust. Stratified K-fold cross-validation effectively avoids model overfitting and improves the prediction accuracy of the model under new data. The introduction of Bayesian optimization and genetic algorithm provides the best parameter settings for LSTM and GRU models to maintain high prediction accuracy under different load fluctuations and external factors.

[0070] The power demand forecasting module significantly improves the accuracy and response speed of power demand forecasting through the combined application of LSTM and GRU. Through deep learning analysis of multi-dimensional data, this module can accurately capture the long-term and short-term trends of power demand, meet the demand forecasting needs under complex load fluctuation scenarios, and enhance the system's adaptability to uncertainty.

[0071] In this embodiment, the coal supply optimization module includes:

[0072] The optimization algorithm submodule optimizes the scheduling of the coal supply chain based on the MILP algorithm, with the goal of minimizing inventory and transportation costs;

[0073] The solution generation and screening submodule uses genetic algorithms to generate alternative coal supply chain solutions and uses particle swarm optimization algorithms to screen the optimal solution to meet the elastic response requirements of demand fluctuations.

[0074] The "coal supply optimization module" described in this embodiment realizes the global optimization of the production, inventory and transportation links of the coal supply chain through the comprehensive optimization of mixed integer linear programming (MILP) and genetic algorithm, greatly improving the efficiency and economy of the supply chain. The specific technical effects are as follows: In the coal supply chain management, production, inventory and transportation scheduling involve multiple constraints, such as production cost, inventory capacity, transportation time and cost. Traditional optimization algorithms often find it difficult to find a solution with good economy and response speed under the consideration of multiple constraints. The "coal supply optimization module" described in this embodiment realizes the precise optimization of resource allocation under multi-dimensional constraints through MILP. The MILP algorithm can optimize and solve different constraints to ensure that the coal production, transportation and inventory solutions meet the dynamic changes of electricity demand while achieving the lowest cost consumption. This precise resource allocation capability not only improves the economic benefits of the system, but also enhances the adaptability of the supply chain to demand fluctuations. In order to adapt to the complex and changing demand environment, the module introduces a genetic algorithm to iterate and optimize the solution in the optimization scheduling process. The genetic algorithm simulates the natural selection process and iterates the initial solution multiple times to approach the optimal supply chain solution. When there are large fluctuations or emergencies in the power demand forecast, the system can use genetic algorithms to quickly generate alternative solutions to ensure that the supply chain has high flexibility and adaptability. This dynamic adjustment capability is particularly effective in dealing with scenarios such as seasonal peak electricity consumption, climate change and policy changes, ensuring that the supply chain can still maintain efficient operation in an uncertain environment. The traditional coal supply chain management model relies more on manual experience decision-making, and the adjustment speed is slow, especially when facing sudden changes in demand. The coal supply optimization module of this application can perform a global analysis of the coal supply chain and generate the optimal scheduling plan in a short time through the integrated optimization of MILP and genetic algorithms, effectively improving the response speed of the supply chain. Fast automated decision-making not only reduces the time cost of manual participation, but also improves the scientific nature of decision-making, avoiding resource waste and supply shortages caused by human factors. This module comprehensively considers the costs of production, inventory and transportation, and minimizes the overall cost through optimization algorithms. When the power demand is high, the system can appropriately increase coal production and inventory reserves according to demand forecasts to avoid shortages; when demand decreases, production and inventory can be adjusted according to forecasts to reduce the cost burden of excess inventory. The dynamic adjustment of transportation routes and plans also effectively reduces logistics costs and achieves an overall reduction in supply chain operating costs. The coal supply optimization module solves the resource allocation and response speed problems under multi-dimensional constraints in the coal supply chain through the integrated application of MILP and genetic algorithms, and achieves efficient management of the supply chain. This module not only improves the adaptability and flexibility of the supply chain, but also significantly reduces operating costs, providing reliable resource guarantees for the stable operation of the power system.

[0075] In one embodiment, the intelligent decision-making module includes:

[0076] The rule base analysis submodule generates control suggestions based on power demand forecast and supply optimization results, including inventory replenishment, transportation optimization and scheduling plans;

[0077] The data visualization submodule displays supply and demand forecast trends, inventory status, and scheduling suggestions through a visual interface, allowing managers to monitor the supply and demand balance in real time and make manual adjustments.

[0078] The "intelligent decision-making module" described in this embodiment provides data-driven decision support for power system managers through rule base analysis and data visualization, significantly improving the real-time, accuracy and intelligence level of decision-making. The specific technical effects are as follows: the built-in rule base analysis submodule of the intelligent decision-making module can convert the power demand forecast and coal supply chain optimization results into specific operational suggestions, reducing the time cost of manual analysis and judgment. The rule base contains a variety of rules for power demand and supply chain regulation, and automatically generates countermeasures through dynamic matching with system data. For example, when the peak power consumption period is predicted, the system can automatically suggest increasing coal inventory reserves or optimizing transportation scheduling according to the rules. This automated decision support realizes the intelligent decision-making auxiliary function, so that managers can quickly obtain professional operational suggestions when demand changes. The data visualization submodule of this module intuitively presents information such as forecast trends, inventory status, and scheduling plans in the form of charts or graphics, so that managers can understand the current system status at a glance. Through the graphical display of data, managers can easily identify key information such as supply and demand trends and forecast errors, so as to analyze data and make decisions more efficiently. Data visualization reduces the complexity of data analysis and improves the efficiency of information communication. Especially in situations that require rapid response, data visualization significantly improves the efficiency of management decision-making.

[0079] In the intelligent decision-making module, managers can monitor the system's forecast and optimization results in real time, and manually adjust the system's scheduling recommendations based on actual conditions. This function is particularly suitable for dealing with emergencies or large fluctuations in demand. For example, if coal transportation is delayed due to extreme weather, managers can see the scheduling status in time on the monitoring interface and manually adjust the scheduling priority or transportation route. The combination of real-time monitoring and manual adjustment greatly improves the flexibility of the system, allowing managers to maintain control over the supply chain in a dynamic environment.

[0080] Through rule base analysis and data visualization, the intelligent decision-making module can effectively prevent the problem of imbalance between power supply and demand. The system automatically weighs the balance between supply chain costs and power demand during the decision-making process, and ensures economic efficiency by rationally allocating resources. For example, during low-peak demand periods, the system can reduce the amount of coal transported to reduce costs; during peak periods, it can increase inventory to ensure demand. This intelligent decision-making mechanism not only improves the economy of the system, but also ensures the stable operation of the power system.

[0081] The intelligent decision-making module of this embodiment realizes intelligent and flexible decision support through functions such as rule base analysis, data visualization and real-time monitoring. This module effectively reduces the decision-making burden of managers, enhances the flexibility of the system to cope with complex environments, and ensures the stability and economy of the power system in a dynamic environment.

[0082] In one embodiment, the feedback learning module includes:

[0083] The error analysis submodule performs real-time detection and feedback of errors in prediction and optimization results;

[0084] The reinforcement learning submodule dynamically adjusts model parameters based on error analysis results through the Q-learning algorithm to maximize the accuracy of power demand forecasting and supply chain scheduling;

[0085] The emergency response submodule automatically triggers local optimization when emergencies cause demand fluctuations to enhance the system's rapid adaptability.

[0086] In one embodiment, the training goal of the time series prediction model submodule is to minimize the power load prediction error, output short-term prediction results and long-term prediction results, and automatically trigger the adjustment of short-term prediction when the actual demand deviation exceeds the preset threshold to optimize the prediction accuracy.

[0087] In one embodiment, the optimization algorithm submodule comprehensively considers transportation time, inventory capacity and energy price when generating a coal supply plan to ensure the economy and reliability of coal supply.

[0088] In one embodiment, the reinforcement learning submodule adjusts the hyperparameters of the power demand forecasting model through the Q-learning algorithm to improve the accuracy of the forecasting results, and automatically iterates and optimizes when the error increases to achieve dynamic updating of the model.

[0089] The reinforcement learning submodule in the feedback learning module described in this embodiment dynamically optimizes the power demand forecasting model through the Q-learning algorithm, thereby realizing accurate adjustment of the forecast results and enhancing the adaptability of the system. The module design enables the system to quickly respond to changes in an uncertain market environment and automatically adjust the parameters of the forecasting model, thereby improving the accuracy and adaptability of the forecast. The specific technical effects are as follows: Q-learning is a model-free reinforcement learning algorithm based on environmental feedback, which can adjust the decision-making strategy in the process of continuous trial and error, so that the system can adapt to environmental changes. This module introduces the Q-learning algorithm in the power demand forecasting process, and dynamically adjusts the model parameters through real-time monitoring of error feedback. This adjustment mechanism enables the model to quickly adapt to demand fluctuations, especially when external factors change or demand surges, Q-learning can effectively optimize the forecasting accuracy of the model. This adaptive optimization mechanism enables the system to have efficient learning ability and significantly enhances the ability to cope with uncertain demand changes.

[0090] The reinforcement learning submodule reduces the accumulation of prediction errors by automatically iterating and optimizing model parameters. Traditional prediction models often lack a self-adjustment mechanism after parameter setting, resulting in a decrease in prediction accuracy after long-term use. This module achieves continuous accuracy of the prediction model through dynamic updates. Through repeated training of reinforcement learning, the system can maintain the optimal state of the prediction model, so that it still has high prediction accuracy in long-term use. This dynamic update mechanism ensures the prediction ability and long-term stability of the model.

[0091] The Q-learning algorithm has unique advantages in dealing with sudden demand fluctuations. Through real-time environmental feedback, the system can quickly identify the amplitude and frequency of demand fluctuations, and adjust the model's prediction parameters in a timely manner according to the situation to ensure that the prediction results are closer to actual demand. For example, in the case of a surge in electricity demand caused by extreme weather, Q-learning can perform reinforcement learning based on short-term demand fluctuations, thereby optimizing the prediction accuracy of power load and ensuring the reasonable scheduling of the supply chain. This flexible adjustment capability allows the system to quickly adapt to complex environmental changes and enhances the system's real-time response capabilities.

[0092] The reinforcement learning process of this module is completely completed automatically based on data feedback, reducing the reliance on manual parameter adjustment. This not only improves the automation level of the prediction and optimization process, but also improves the overall operation and maintenance efficiency of the system. Managers do not need to frequently adjust model parameters to ensure the accuracy and adaptability of predictions, which greatly reduces the maintenance cost of the system.

[0093] The feedback learning module realizes dynamic optimization and adaptive adjustment of the power demand forecasting model through Q-learning reinforcement learning, effectively improving the forecasting accuracy, response speed and system automation level. This module design significantly enhances the adaptability of the system on the basis of reducing human intervention, and provides intelligent technical support for the efficient management of the power system.

[0094] The specific implementation content of each module can be found in the above definition of the electricity demand forecasting and coal supply optimization system based on big data analysis, which will not be repeated here.

[0095] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A power demand forecasting and coal supply optimization system based on big data analysis, characterized in that: The system comprises: The data collection module is used to collect electricity consumption data from power user terminals through IoT devices, obtain meteorological data, policy information, holiday arrangements and external data of economic indicators through crawlers and API interfaces, and build a data input set; Big data storage and processing module, which stores and processes data through distributed database and data processing framework, performs data cleaning, dimension reduction and feature extraction, and generates input data sets for power demand forecasting module and coal supply optimization module; The power demand forecasting module performs time series forecasting on power load and external influencing factor data based on the long short-term memory network model or the gated recurrent unit model to generate short-term and long-term power demand forecasting results; Coal supply optimization module, which optimizes coal production, inventory and transportation scheduling based on mixed integer linear programming and genetic algorithm; Intelligent decision-making module, which analyzes the power demand forecast results and coal supply optimization results, generates dynamic control suggestions, and displays the supply and demand status, inventory status and scheduling plan through a visual interface; The feedback learning module performs error analysis and model iterative optimization on the prediction and optimization results through the reinforcement learning mechanism.

2. The system according to claim 1, characterized in that The data acquisition module comprises: The power data collection submodule collects real-time power consumption and time-sharing load data of power user terminals through IoT devices and smart meters; The external data collection submodule obtains unstructured data such as policy information and holiday schedules through crawlers, and collects meteorological data and economic indicators through API interfaces to form an input feature set for power demand forecasting.

3. The system according to claim 1, characterized in that The big data storage and processing module includes: The distributed storage submodule uses a distributed file system to store raw data and a NoSQL database to store processed structured data. The data cleaning and dimensionality reduction submodule performs outlier detection, missing value filling and deduplication operations, and uses principal component analysis to reduce the dimensionality of external data and optimize feature extraction efficiency.

4. The system according to claim 1, characterized in that The power demand prediction module comprises: The time series forecasting model submodule trains the power load and external data based on the LSTM or GRU model to generate short-term and long-term power demand forecasts; The model optimization submodule improves the generalization ability of the model through stratified K-fold cross-validation, and uses Bayesian optimization or genetic algorithm to tune the model hyperparameters to ensure the accuracy of the prediction results.

5. The system according to claim 1, characterized in that The coal supply optimization module includes: The optimization algorithm submodule optimizes the scheduling of the coal supply chain based on the MILP algorithm, with the goal of minimizing inventory and transportation costs; The solution generation and screening submodule uses genetic algorithms to generate alternative coal supply chain solutions and uses particle swarm optimization algorithms to screen the optimal solution to meet the elastic response requirements of demand fluctuations.

6. The system according to claim 1, characterized in that The intelligent decision-making module includes: The rule base analysis submodule generates control suggestions based on power demand forecast and supply optimization results, including inventory replenishment, transportation optimization and scheduling plans; The data visualization submodule displays supply and demand forecast trends, inventory status, and scheduling suggestions through a visual interface, allowing managers to monitor the supply and demand balance in real time and make manual adjustments.

7. The system according to claim 1, characterized in that The feedback learning module includes: The error analysis submodule performs real-time detection and feedback of errors in prediction and optimization results; The reinforcement learning submodule dynamically adjusts model parameters based on error analysis results through the Q-learning algorithm to maximize the accuracy of power demand forecasting and supply chain scheduling; The emergency response submodule automatically triggers local optimization when emergencies cause demand fluctuations to enhance the system's rapid adaptability.

8. The system according to claim 4, characterized in that The training goal of the time series forecasting model submodule is to minimize the power load forecast error, output short-term forecast results and long-term forecast results, and automatically trigger the adjustment of short-term forecast when the actual demand deviation exceeds the preset threshold to optimize the forecast accuracy.

9. The system according to claim 5, characterized in that The optimization algorithm submodule comprehensively considers transportation time, inventory capacity and energy price when generating a coal supply plan to ensure the economy and reliability of coal supply.

10. The system according to claim 7, characterized in that The reinforcement learning submodule adjusts the hyperparameters of the power demand forecasting model through the Q-learning algorithm to improve the accuracy of the forecast results, and automatically iterates and optimizes when the error increases to achieve dynamic updating of the model.

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