Short-term industrial load prediction method

Abstract

The invention relates to the technical field of industrial loads, in particular to a short-term industrial load prediction method, which comprises the following steps of: acquiring industrial related multi-source input data, and performing standardized preprocessing on the multi-source input data to obtain a standardized data set; based on the standardized data set, screening and optimizing features through a multi-layer screening mechanism, and determining a core input feature set; and constructing a hybrid prediction model comprising a local feature extraction module, a time sequence dependency processing module and an integrated learning structure, extracting local features of time sequence data in the core input feature set through the local feature extraction module, and capturing a long-term dependency relationship of the data by using the time sequence dependency processing module. According to the method, the hybrid prediction model comprising the local feature extraction module, the time sequence dependence processing module and the integrated learning structure is constructed, so that the problem that the accuracy of a prediction result is insufficient due to the fact that time sequence data is processed by adopting a single model in most traditional short-term industrial load prediction methods is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial load, and in particular to a short-term industrial load prediction method. BACKGROUND

[0002] With the continuous advancement of industrialization, short-term industrial load prediction plays an increasingly important role in energy management and scheduling. Short-term industrial load prediction refers to predicting the industrial load in the future period according to historical load data, production plans, meteorological information and other factors. This technology can help enterprises optimize energy consumption, improve production efficiency, reduce energy costs, and ensure that system load does not exceed the safe operating range.

[0003] Traditional short-term industrial load prediction methods mostly use a single model to process time series data. Since a single model cannot simultaneously consider local feature extraction and long-term dependence capture, the prediction results are not accurate enough. SUMMARY

[0004] In order to make up for the above shortcomings, the present application provides a short-term industrial load prediction method, which aims to improve the problem that traditional short-term industrial load prediction methods mostly use a single model to process time series data, and since a single model cannot simultaneously consider local feature extraction and long-term dependence capture, the prediction results are not accurate enough.

[0005] In a first aspect, the present application provides the following technical solution, a short-term industrial load prediction method, comprising the following steps: Collecting industrial-related multi-source input data, standardizing and preprocessing the multi-source input data to obtain a standardized data set; Based on the standardized data set, filtering and optimizing features through a multi-layer filtering mechanism to determine a core input feature set; Constructing a hybrid prediction model containing a local feature extraction module, a time series dependence processing module and an ensemble learning structure, extracting local features of time series data in the core input feature set through the local feature extraction module, capturing long-term dependence relationships of data using the time series dependence processing module, and generating a prediction result through the ensemble learning structure; Quantifying the uncertainty in the prediction process, and adjusting the key parameters of the hybrid prediction model through a meta-heuristic optimization algorithm; Based on newly added industrial-related multi-source input data, dynamically updating the hybrid prediction model through an online learning mechanism; Outputting the short-term industrial load prediction result to the energy scheduling system, and cooperatively optimizing prediction and scheduling through digital twin technology.

[0006] By adopting the technical scheme, the hybrid prediction model comprising a local feature extraction module, a time sequence dependence processing module and an integrated learning structure is constructed, the local feature extraction module is used to extract local features of time sequence data in a core input feature set, the time sequence dependence processing module is used to capture long-term dependence of data, and the integrated learning structure is used to process and generate a prediction result, thereby realizing collaborative capture of local fluctuations and long-term trends of short-term industrial load data, and improving the problem that the traditional short-term industrial load prediction method mostly uses a single model to process time sequence data, and the single model is difficult to simultaneously consider local feature extraction and long-term dependence capture, thereby causing insufficient accuracy of a prediction result.

[0007] Further, the standardization preprocessing comprises: adopting mean filling or deleting sample processing missing values; detecting and processing abnormal values by Z-score or box plot; calculating feature mean and standard deviation to convert to zero mean unit variance, or mapping to [0, 1] interval through maximum value scaling.

[0008] Further, the multi-layer screening mechanism comprises: removing high correlation redundant features by Pearson correlation coefficient screening; based on the de-redundant features, screening high importance features by random forest evaluation; using particle swarm optimization to globally optimize the feature subset after screening, and selecting an optimal feature set; screening important features by integrated learning weighted evaluation, and dynamically adjusting feature priority in training according to model feedback.

[0009] Further, the local feature extraction comprises: extracting time sequence data from the core input feature set and organizing according to a fixed time window; selecting a convolution kernel to convolve the time sequence data and extract local features; reducing calculation amount by down-sampling through a pooling layer; applying a ReLU activation function to introduce nonlinearity; outputting local features by multi-layer convolution and pooling for subsequent processing.

[0010] Further, the capturing long-term dependence comprises: the time sequence dependence processing module is selected from a standard recurrent neural network, a long short-term memory network or a gated recurrent unit, and industrial load or other time sequence data is input; if the standard recurrent neural network, learning time dependence; if the long short-term memory network, capturing long-term dependence through an input gate, a forget gate and an output gate; If it is a gated recurrent unit, the information flow is captured through update gate and reset gate to capture long-term dependencies; the input sequence is encoded and the predicted output is generated through the Seq2Seq architecture.

[0011] Further, the integrated learning structure processing includes: Selecting the base learner including support vector machine, random forest and gradient boosting tree, respectively training the core input feature set; Generating multiple prediction results by the base learner; Inputting the base learner output into the meta-learner to generate the final prediction result.

[0012] Further, the uncertainty quantification includes: Analyzing the uncertainty sources including data noise, model error and external disturbance; Generating multiple sets of input samples by the Monte Carlo method to simulate the prediction results under different conditions; Updating the model posterior distribution using the Bayesian theorem to sample and generate multiple prediction results; Calculating the confidence interval of the prediction result based on Monte Carlo simulation or Bayesian inference to quantify the uncertainty degree.

[0013] Further, the model dynamic update includes: Updating the model parameters step by step through incremental learning; Using online gradient descent method to update in small steps, calculating the gradient to adjust the model weight; Setting a memory module or a forgetting mechanism to dynamically adjust the weight of historical data; According to the real-time data and model error, dynamically adjusting the learning rate, and real-time verifying the prediction accuracy to monitor the model performance.

[0014] Further, the collaborative optimization includes: Based on the physical structure and dynamic characteristics of the industrial production system, a corresponding digital twin model is constructed; Inputting the short-term industrial load prediction result into the digital twin model and comparing it with the actual system state for analysis; Using the digital twin model to optimize the energy scheduling scheme in real time, and adjusting the energy distribution and production scheduling according to the prediction and actual feedback; Monitoring the difference between prediction and scheduling execution, and adjusting the strategy through the digital twin model; Combining historical and real-time data, and optimizing the digital twin model using machine learning.

[0015] In the second aspect, the present application provides the following technical scheme, a short-term industrial load prediction system, the system includes: A multi-source data processing module is configured to collect industrial-related multi-source input data, standardize and preprocess the multi-source input data, and obtain a standardized data set; A feature screening optimization module is configured to screen and optimize features based on the standardized data set through a multi-layer screening mechanism, and determine a core input feature set; A hybrid model construction module is configured to construct a hybrid prediction model comprising a local feature extraction module, a time series dependency processing module, and an ensemble learning structure, extract local features of time series data in the core input feature set through the local feature extraction module, capture long-term dependency relationships of data through the time series dependency processing module, and generate a prediction result through the ensemble learning structure; An uncertainty processing and parameter optimization module is configured to quantify uncertainty in a prediction process, and adjust key parameters of the hybrid prediction model through a meta-heuristic optimization algorithm; A model dynamic updating module is configured to dynamically update the hybrid prediction model through an online learning mechanism based on newly added industrial-related multi-source input data; A prediction and scheduling coordination module is configured to output short-term industrial load prediction results to an energy scheduling system, and cooperatively optimize prediction and scheduling through digital twin technology.

[0016] The present application has the following advantages: 1、In the present application, a hybrid prediction model comprising a local feature extraction module, a time series dependency processing module, and an ensemble learning structure is constructed, local features of time series data in the core input feature set are extracted through the local feature extraction module, long-term dependency relationships of data are captured through the time series dependency processing module, and a prediction result is generated through the ensemble learning structure, thereby realizing the cooperative capture of local fluctuations and long-term trends of short-term industrial load data, and improving the problem that traditional short-term industrial load prediction methods mostly use a single model to process time series data, which is difficult to simultaneously consider local feature extraction and long-term dependency capture, thereby causing insufficient prediction accuracy.

[0017] 2、In the present application, features are screened and optimized through a multi-layer screening mechanism, and a core input feature set is determined, thereby improving the problem that traditional short-term industrial load prediction methods mostly use a fixed feature set, since industrial load data has high dimensionality and complex features, and traditional methods are difficult to extract effective information from a large number of irrelevant or redundant features, thereby causing low prediction accuracy.

[0018] 3. In this invention, by quantifying the uncertainty in the prediction process and using a metaheuristic optimization algorithm to adjust the key parameters of the hybrid prediction model, the rationality of the prediction model parameter settings and its ability to cope with uncertainty factors are improved. This improves the problem that most traditional short-term industrial load prediction methods do not quantify uncertainty and rely on manual adjustment of model parameters. Due to the lack of consideration for uncertainty and the subjectivity of parameter adjustment, the prediction results are not stable enough. Attached Figure Description

[0019] Figure 1 This is a flowchart of a short-term industrial load forecasting method proposed in this invention; Figure 2 This is a system architecture diagram of a short-term industrial load forecasting system proposed in this invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 In a first embodiment of the present invention, the present invention provides a short-term industrial load forecasting method, such as... Figure 1 As shown, the process includes the following steps: collecting multi-source input data related to industry, performing standardized preprocessing on the multi-source input data, and obtaining a standardized dataset; Furthermore, standardized preprocessing includes: Missing values ​​can be handled by filling in the mean or deleting samples. Detect and handle outliers using Z-score or box plot; The calculated characteristic mean and standard deviation are converted to zero mean and unit variance, or mapped to the [0,1] interval by scaling to the extreme values.

[0022] Specifically, the first step of the short-term industrial load prediction method is to collect industrial-related multi-source input data, and to standardize and preprocess these data to ensure that different characteristic data have a uniform scale, which is conducive to subsequent feature selection and modeling. The steps of standardization and preprocessing include missing value processing, outlier detection, and feature value scaling. First, for missing value processing, the mean filling or deletion of samples containing missing values is used. Mean filling fills missing data by calculating the mean value of the feature, avoiding the deviation caused by missing data. If the missing value sample is deleted, the sample containing missing data is directly deleted to ensure the integrity of the data for subsequent operations. Second, for outlier detection and processing, the Z-score method or box plot method is used for analysis. The formula of the Z-score method is: ; wherein, is the data point, is the mean value of the feature, is the standard deviation of the feature, if the absolute value of Z-score is greater than the set threshold, the data point is considered to be an outlier and is processed. The box plot method detects outliers by calculating the quartiles of the data and defining upper and lower limits. After processing, the data is filtered, and the outliers are replaced or deleted to ensure data quality. The third step is feature value scaling, which uses Z-score standardization or maximum scaling method. Z-score standardization converts each feature to a form with zero mean and unit variance. The formula is: ; wherein, is the original data, is the mean value of the data, is the standard deviation of the data. Through this operation, the data has zero mean and unit variance, making the scales of each feature consistent. Alternatively, the maximum scaling method linearly maps the data to the [0, 1] interval. The formula is: ; wherein, is the minimum value in the data, is the maximum value in the data. The standardized data set can be used for subsequent feature selection, modeling, and prediction. The final standardized data set can effectively reduce the dimensional difference between different features, thereby improving the stability and accuracy of model training and avoiding training bias caused by data scale difference.

[0023] By standardizing the preprocessing of multi-source input data, the scale difference in the data can be eliminated, so that each feature is in the same dimension, thereby avoiding the influence of different feature scales on subsequent modeling and analysis. Adopting mean filling processing and abnormal value detection can improve data quality, stability and prediction accuracy of model training. The standardized data helps to optimize feature selection and improve the performance of the prediction model.

[0024] Based on the standardized data set, the features are screened and optimized through a multi-layer screening mechanism to determine the core input feature set; Further, the multi-layer screening mechanism includes: Highly correlated redundant features are removed by Pearson correlation coefficient screening; Based on the de-redundant features, high importance features are screened by random forest evaluation; The selected feature set is globally optimized by particle swarm optimization; The important features are screened by integrated learning weighted evaluation, and the feature priority is dynamically adjusted according to the model feedback in training.

[0025] Specifically, the standardized data set is screened and optimized by the multi-layer screening mechanism. First, the correlation between features is analyzed by using Pearson correlation coefficient, the correlation coefficient between each pair of features is calculated, and the highly correlated redundant features are screened out, the formula is: ; Where, is the Pearson correlation coefficient, and are the sample values of features and , and and are the means of and , respectively. Then, the importance of the de-redundant features is evaluated by using the random forest algorithm, and the features with high influence on load prediction are screened out. The contribution of each feature is determined by calculating the Gini importance or information gain, then the particle swarm optimization algorithm is applied to globally optimize the selected feature subset, and the optimal feature set is selected. This algorithm simulates the search behavior of particle swarm to find the global optimal solution, and the update formula of particle swarm optimization is: ; Where, is the speed of the particle in the th generation, is the particle position, is the historical optimal position of the particle, is the historical optimal position of the group. and is an acceleration constant, and is a random number, finally, an integrated learning method is used for weighted evaluation, the prediction results of multiple algorithms are combined to dynamically adjust the priority of the features, and the final result is the feature set screened and optimized through the above steps, which is used for training and prediction of subsequent models, to ensure that the selected feature set can better reflect the change rule of industrial load and improve the prediction accuracy and generalization ability of the model.

[0026] Through the multi-layer screening mechanism, redundant features can be effectively removed, the feature set is optimized, and the quality of the core input features and the accuracy of the prediction model are improved. First, the Pearson correlation coefficient is used to remove highly correlated redundant features, then the important features are further selected through random forest and particle swarm optimization, and finally the integrated learning weighted evaluation is combined to dynamically adjust the feature priority, thereby improving the prediction ability of the model for industrial load.

[0027] A hybrid prediction model including a local feature extraction module, a time series dependency processing module, and an integrated learning structure is constructed, the local features of the time series data in the core input feature set are extracted through the local feature extraction module, the long-term dependency relationship of the data is captured through the time series dependency processing module, and the prediction result is generated through the integrated learning structure; Further, the local feature extraction includes: Extracting time series data from the core input feature set and organizing it according to a fixed time window; Selecting a convolution kernel to convolve the time series data and extract local features; Reducing the amount of calculation through down-sampling by a pooling layer; Introducing nonlinearity by applying a ReLU activation function; Outputting local features through multiple layers of convolution and pooling for subsequent processing.

[0028] Specifically, the accuracy of short-term industrial load prediction is improved by constructing a hybrid prediction model. First, time series data is extracted from the core input feature set and organized according to a fixed time window to extract time series features. The time window is set to 1 hour. Then, an appropriate convolution kernel is selected for convolution operation to extract local features in the time series data. The features are down-sampled by the pooling layer to reduce the amount of calculation and improve processing efficiency. The ReLU activation function is applied to the output of the convolution layer to introduce non-linear characteristics and enhance the expression ability of the model. Through multi-layer convolution and pooling structure, higher-level local features are extracted for subsequent modules. The data used in this process includes the core input feature set, each of which represents an important variable in the industrial load prediction process. After this process, the output is the extracted local features, which will be used as the input of the time series dependence processing module to further capture long-term dependencies in the data and generate the final prediction result through the ensemble learning structure.

[0029] The local features of the time series data are extracted by the local feature extraction module, and the long-term dependence is captured by the time series dependence processing module. The prediction result is generated by combining the ensemble learning structure, which effectively improves the accuracy of short-term industrial load prediction and the generalization ability of the model, and reduces the demand for computing resources.

[0030] Further, capturing long-term dependencies includes: The time series dependence processing module is selected from a standard recurrent neural network, a long short-term memory network, or a gated recurrent unit, and the input is industrial load or other time series data. If it is a standard recurrent neural network, the time dependence is learned. If it is a long short-term memory network, the long-term dependence is captured through the input gate, the forget gate, and the output gate. If it is a gated recurrent unit, the information flow is controlled by the update gate and the reset gate to capture long-term dependencies. The input sequence is encoded and the prediction output is generated through the Seq2Seq architecture.

[0031] Specifically, by constructing a hybrid prediction model including a time series dependency processing module, the long-term dependency relationship of the industrial load and other time series data is captured. The time series dependency processing module adopts a standard recurrent neural network, a long short-term memory network or a gated recurrent unit (GRU), and encodes and predicts the output through a Seq2Seq architecture. In this process, the input data includes time series data such as industrial load, and the input is processed through the time steps in the model. Specifically, if a long short-term memory network is used, the data will capture long-term dependencies through the input gate, the forget gate and the output gate. If a gated recurrent unit is used, the information flow is controlled through the update gate and the reset gate, thereby retaining important feature information in a long time range. In this way, the long-term correlation in the time series data is effectively extracted, thereby improving the prediction accuracy of the model. The generated prediction results are used for subsequent prediction or scheduling optimization.

[0032] By using the time series dependency processing module to capture the long-term dependency relationship of the data, the key time patterns in the time series data can be effectively mined, thereby improving the accuracy of short-term industrial load prediction. By using models such as long short-term memory networks or gated recurrent units, the long-term dependency relationship is captured, which ensures that important historical information is retained when the model processes complex dynamic data, thereby improving the prediction performance.

[0033] Further, the ensemble learning structure processing includes: Selecting basic learners including support vector machines, random forests and gradient boosting trees to train the core input feature set; Generating multiple prediction results through the basic learners; Inputting the basic learner outputs into the meta-learner to generate the final prediction result.

[0034] Specifically, the prediction results are processed by the ensemble learning structure. First, basic learners such as support vector machines, random forests and gradient boosting trees are used to train the core input feature set. First, each basic learner is trained through the input data set to generate an independent prediction result. These prediction results are then input as features into the meta-learner for comprehensive learning to obtain the final prediction result. Assuming that the input data set is , each basic learner (support vector machine, random forest or gradient boosting tree) generates a prediction output from the input data. Then, the meta-learner performs weighted evaluation and fusion on all to obtain the final prediction result ; wherein The meta-learner function is used to accurately predict short-term industrial loads and provides the input required for optimizing the energy dispatch system. The training data for each basic learner comes from a standardized core input feature set, and the parameter values ​​are obtained by training on historical data. The meta-learner dynamically adjusts the weights of the prediction results through optimization algorithms to improve prediction accuracy and robustness.

[0035] By integrating learning structures with basic learners such as support vector machines, random forests, and gradient boosting trees, the prediction results of multiple models can be effectively merged, thereby improving prediction accuracy. This method can combine the advantages of different models, reduce the bias that may exist in a single model, optimize the final prediction results, and provide a more reliable basis for industrial load forecasting.

[0036] The uncertainty in the prediction process is quantified, and the key parameters of the hybrid prediction model are adjusted through metaheuristic optimization algorithms; Furthermore, uncertainty quantification includes: The analysis includes sources of uncertainty such as data noise, model error, and external disturbances; Multiple sets of input samples are generated using the Monte Carlo method to simulate prediction results under different conditions; The posterior distribution of the model is updated using Bayes' theorem, and multiple prediction results are generated by sampling. The confidence interval of the prediction results is calculated based on Monte Carlo simulation or Bayesian inference to quantify the degree of uncertainty.

[0037] Specifically, the uncertainty in the prediction process is quantified using the Monte Carlo method and Bayesian inference. First, factors such as data noise, model error, and external disturbances are analyzed to determine the sources of uncertainty. Then, multiple sets of input samples are generated using the Monte Carlo method to simulate prediction results under different conditions. Finally, the posterior distribution of the model is updated using Bayes' theorem, as shown in the formula: ; in, It is the posterior distribution, representing the distribution given the data. After, parameters The probability distribution; It is the likelihood function, which represents the likelihood of a given set of parameters. In this case, data The probability of occurrence; It is the prior distribution of the parameter, representing the parameter The initial assumptions; It is the marginal likelihood, representing the data the overall probability of the total probability, and then sample from the posterior distribution to obtain multiple prediction results. Through these results, the confidence interval of the prediction result is calculated to quantify the uncertainty and provide a basis for model tuning. Finally, the result is used to adjust the key parameters of the model to further optimize the performance of the hybrid prediction model.

[0038] By quantifying the uncertainty in the prediction process, the influence of data noise, model error and external disturbance on the prediction result can be effectively identified and reduced. By generating multiple prediction results and calculating the confidence interval through the Monte Carlo method and Bayesian inference, a more reliable prediction interval and uncertainty evaluation can be provided, thereby optimizing the performance of the hybrid prediction model and improving the prediction accuracy.

[0039] Based on the newly added industrial-related multi-source input data, the hybrid prediction model is dynamically updated through an online learning mechanism; Further, the model dynamic updating includes: Incremental learning is used to gradually update the model parameters; Online gradient descent method is used for small step update to calculate the gradient and adjust the model weight; A memory module or forgetting mechanism is set to dynamically adjust the weight of historical data; The learning rate is dynamically adjusted according to real-time data and model error, and the prediction accuracy is verified in real time to monitor the model performance.

[0040] Specifically, through incremental learning method, the model gradually updates its parameters according to the newly added industrial-related multi-source input data. Online gradient descent method is used to adjust the model weight, and the gradient of each small step is calculated to gradually optimize the performance of the model. Memory module or forgetting mechanism helps to adjust the weight of historical data, so that the model can better adapt to new data input and reduce the influence of outdated data. Real-time data and model error are used to dynamically adjust the learning rate to ensure the effectiveness of each update, and the accuracy of the prediction result is verified in real time to monitor the model performance. The ultimate goal of these steps is to make the hybrid prediction model maintain accuracy in a dynamic environment and adapt to changing input data. The updating process of the model is optimized by real-time calculation of gradient and error to optimize the weight , and the update formula is: ; Where, is the current model parameter, is the learning rate, is the loss function, and are the input data and target output, is the gradient operator.

[0041] Through incremental learning and online gradient descent method, combined with dynamic adjustment of learning rate and historical data weight, the hybrid prediction model can be updated in real time according to the newly added industrial data, thereby improving the adaptability of the model to new situations and maintaining high prediction accuracy. This dynamic updating mechanism effectively addresses the problems of data changes and model obsolescence, improving the sustained performance and stability of the model.

[0042] The short-term industrial load prediction results are output to the energy scheduling system, and the prediction and scheduling are cooperatively optimized by combining digital twin technology; Further, the cooperative optimization includes: Based on the physical structure and dynamic characteristics of the industrial production system, a corresponding digital twin model is constructed; The short-term industrial load prediction results are input to the digital twin model for comparative analysis with the actual system state; The digital twin model is used to optimize the energy scheduling scheme in real time, and the energy distribution and production scheduling are adjusted according to the prediction and actual feedback; The difference between prediction and scheduling execution is monitored, and the strategy is adjusted by the digital twin model; Combining historical and real-time data, the digital twin model is optimized using machine learning.

[0043] Specifically, the short-term industrial load prediction results are output to the energy scheduling system and cooperatively optimized by combining digital twin technology. First, based on the physical structure and dynamic characteristics of the industrial production system, a digital twin model is constructed. The short-term load prediction results are input to the model, compared with the actual system state, and the energy scheduling scheme is optimized in real time to adjust the energy distribution and production scheduling. By monitoring the difference between prediction and scheduling execution, the digital twin model can dynamically adjust the strategy, and by combining historical and real-time data, machine learning is used to further optimize the model. The core parameters of this process include short-term load prediction results, real-time system state, energy distribution strategy, etc., which are processed by the digital twin model to achieve the optimization of energy scheduling.

[0044] By combining short-term industrial load prediction results with digital twin technology, the energy scheduling scheme can be optimized in real time, improving the accuracy and flexibility of scheduling, reducing energy waste and improving production efficiency. The real-time feedback and strategy adjustment of the digital twin model ensure the continuous optimization of energy distribution and production scheduling, enhancing the adaptability and response capability of the system.

[0045] Embodiment two: In the production process of a certain cement plant, the existing energy scheduling system faces the problems of large load fluctuation, energy waste and low production efficiency, especially during high load periods. The uneven distribution of energy leads to excessive consumption of electricity and instability of load in the production process. To solve the above problems, the short-term industrial load prediction system provided by the present application is adopted, and its structure is as followsFigure 2 The system is implemented as follows:

[0046] First, the multi-source data processing module collects the production data of the cement plant, including temperature, pressure, load and historical power consumption data, and performs standardized preprocessing to generate a standardized dataset, ensuring the consistency of input data and providing accurate data foundation for subsequent analysis.

[0047] Next, the feature selection and optimization module optimizes the features in the standardized dataset through a multi-layer filtering mechanism, removes redundant features and selects an important feature set related to energy load, further improving the accuracy of prediction.

[0048] Subsequently, the hybrid model construction module constructs a hybrid prediction model including local feature extraction, time series dependency processing and ensemble learning structure based on the filtered features. The local feature extraction module extracts local features in time series data through convolution operation, and the time series dependency processing module captures the long-term dependency relationship of power demand in the production process, thereby generating more accurate short-term load prediction results.

[0049] Next, the uncertainty processing and parameter optimization module analyzes data noise, model error and external disturbance, quantifies the uncertainty of prediction using Monte Carlo simulation and Bayesian inference, and adjusts the key parameters of the prediction model using meta-heuristic optimization algorithm, further improving the stability and accuracy of the system under different conditions.

[0050] After that, the model dynamic updating module updates the model parameters using online learning mechanism through real-time data input, and dynamically adjusts the learning rate according to real-time error, ensuring that the model maintains high prediction accuracy in the continuously changing production environment.

[0051] Finally, the prediction and scheduling coordination module inputs the prediction results into the energy scheduling system, and optimizes energy distribution and production scheduling combined with digital twin technology, dynamically adjusts the energy scheduling scheme through real-time feedback mechanism, and ensures efficient and energy-saving production. The system significantly improves the energy utilization efficiency of the cement plant, reduces energy waste, and improves the production stability.

[0052] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for the limitation of the present application, although the above-mentioned embodiments of the present application are described in detail, for the person skilled in the art, it still can modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement to part of the technical features, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A short-term industrial load forecasting method, characterized in that, Includes the following steps: Collect multi-source input data related to industry, and perform standardization preprocessing on the multi-source input data to obtain a standardized dataset; Based on the standardized dataset, features are filtered and optimized through a multi-layer screening mechanism to determine the core input feature set; A hybrid prediction model is constructed, comprising a local feature extraction module, a temporal dependency processing module, and an ensemble learning structure. The local feature extraction module extracts local features of temporal data in the core input feature set, the temporal dependency processing module captures long-term dependencies of the data, and the ensemble learning structure processes the data to generate prediction results. The uncertainty in the prediction process is quantified, and the key parameters of the hybrid prediction model are adjusted through a metaheuristic optimization algorithm; Based on the newly added industrial-related multi-source input data, the hybrid prediction model is dynamically updated through an online learning mechanism; The short-term industrial load forecast results are output to the energy dispatch system, and the forecast and dispatch are optimized in a coordinated manner by combining digital twin technology.

2. The short-term industrial load forecasting method according to claim 1, characterized in that, The standardized preprocessing includes: Missing values ​​can be handled by filling in the mean or deleting samples. Detect and handle outliers using Z-score or box plot; The calculated characteristic mean and standard deviation are converted to zero mean and unit variance, or mapped to the [0,1] interval by scaling to the extreme values.

3. The short-term industrial load forecasting method according to claim 1, characterized in that, The multi-layered screening mechanism includes: Highly correlated redundant features were removed by screening using the Pearson correlation coefficient. Based on the redundancy removal features, high-importance features are selected through random forest evaluation; Particle swarm optimization is used to perform global optimization on the filtered feature subset to select the optimal feature set. Important features are selected through weighted evaluation using ensemble learning, and feature priorities are dynamically adjusted during training based on model feedback.

4. The short-term industrial load forecasting method according to claim 1, characterized in that, The local feature extraction includes: Temporal data is extracted from the core input feature set and organized according to a fixed time window; Select a convolution kernel to convolve the time-series data and extract local features; Reduce computational load by downsampling through pooling layers; The ReLU activation function is used to introduce nonlinearity; Local features are output through multiple convolutions and pooling for subsequent processing.

5. The short-term industrial load forecasting method according to claim 1, characterized in that, The capture of long-term dependencies includes: The time-dependent processing module is selected from standard recurrent neural networks, long short-term memory networks, or gated recurrent units, and inputs industrial load or other time-series data; For a standard recurrent neural network, the learning time dependency exists; For long short-term memory networks, long-term dependencies are captured through input gates, forget gates, and output gates. If it is a gated recurrent unit, the information flow is controlled by updating and resetting the gates to capture long-term dependencies; the input sequence is encoded and the prediction output is generated through a Seq2Seq architecture.

6. The short-term industrial load forecasting method according to claim 1, characterized in that, The ensemble learning structure processing includes: Select basic learners including support vector machines, random forests, and gradient boosting trees, and train them on the core input feature sets respectively; Multiple prediction results are generated using a base learner; The output of the basic learner is input into the meta-learner to generate the final prediction result.

7. The short-term industrial load forecasting method according to claim 1, characterized in that, The uncertainty quantification includes: The analysis includes sources of uncertainty such as data noise, model error, and external disturbances; Multiple sets of input samples are generated using the Monte Carlo method to simulate prediction results under different conditions; The posterior distribution of the model is updated using Bayes' theorem, and multiple prediction results are generated by sampling. The confidence interval of the prediction results is calculated based on Monte Carlo simulation or Bayesian inference to quantify the degree of uncertainty.

8. The short-term industrial load forecasting method according to claim 1, characterized in that, The dynamic updating of the model includes: The model parameters are updated incrementally through incremental learning. The model weights are adjusted by calculating the gradient and updating the model in small steps using the online gradient descent method. Set up a memory module or forgetting mechanism to dynamically adjust the weight of historical data; The learning rate is dynamically adjusted based on real-time data and model error to verify prediction accuracy in real time and monitor model performance.

9. A short-term industrial load forecasting method according to claim 1, characterized in that, The collaborative optimization includes: Based on the physical structure and dynamic characteristics of industrial production systems, construct corresponding digital twin models; Input short-term industrial load forecast results into the digital twin model and compare and analyze them with the actual system status; Utilize digital twin models to optimize energy dispatching schemes in real time, and adjust energy allocation and production scheduling based on forecasts and actual feedback; Monitor the discrepancy between prediction and scheduling execution, and adjust strategies using a digital twin model; By combining historical and real-time data, machine learning is used to optimize digital twin models.

10. A short-term industrial load forecasting system, characterized in that, A short-term industrial load forecasting method according to any one of claims 1-9, the system comprising: The multi-source data processing module is used to collect industrial-related multi-source input data, perform standardized preprocessing on the multi-source input data, and obtain a standardized dataset. The feature selection and optimization module is used to select and optimize features based on the standardized dataset through a multi-layer selection mechanism to determine the core input feature set. The hybrid model building module is used to build a hybrid prediction model that includes a local feature extraction module, a temporal dependency processing module, and an ensemble learning structure. The local feature extraction module extracts local features of temporal data in the core input feature set, the temporal dependency processing module captures long-term dependencies of the data, and the ensemble learning structure processes the data to generate prediction results. The uncertainty handling and parameter optimization module is used to quantify the uncertainty in the prediction process and adjust the key parameters of the hybrid prediction model through a metaheuristic optimization algorithm. The model dynamic update module is used to dynamically update the hybrid prediction model based on newly added industrial-related multi-source input data through an online learning mechanism; The forecasting and scheduling coordination module is used to output short-term industrial load forecasting results to the energy dispatching system, and to perform coordinated optimization of forecasting and scheduling by combining digital twin technology.

Images