Customer demand mining model optimization method based on deep learning

Abstract

Disclosed in the present invention is a customer demand mining model optimization method based on deep learning. The method comprises: collecting and preprocessing data; converting the data into a time sequence model; using historical power consumption data to train the model; using grid search to optimize hyper-parameters of the model; using a model interpretation tool to assess the contribution of different features to model prediction; using a Bayesian method to quantify the uncertainty of prediction; deploying the trained model into a production environment; and continuously collecting new user power consumption data and regularly using the new data to retrain the model so as to adapt to a changing user demand. The problem in the prior art that evaluation results do not satisfy actual situations is solved.

Description

Optimization method of customer demand mining model based on deep learning Technical Field

[0001] The present invention relates to an optimization method for a customer demand mining model based on deep learning, and belongs to the technical field of machine learning. Background Art

[0002] In the power industry, accurately understanding and assessing customer needs is crucial for improving service quality, increasing user satisfaction, and optimizing resource allocation. With the development of smart grid technology, the power industry has accumulated a vast amount of customer electricity usage data, which contains a rich collection of customer behavior characteristics and demand information. However, due to the diversity and uncertainty of customer needs and the complexity of electricity usage data, traditional demand analysis methods struggle to capture the full picture of customer needs, making accurate evaluation and forecasting even more challenging. Therefore, the power industry urgently needs a new demand mining and assessment method to better adapt to the development needs of modern power grids.

[0003] Deep learning-based customer demand mining utilizes advanced machine learning techniques and big data analysis to effectively extract user behavior patterns and potential needs from massive amounts of electricity consumption data. Deep learning models, with their exceptional feature learning capabilities, can automatically identify nonlinear relationships and complex patterns in data, providing deeper and more accurate insights than traditional statistical analysis methods.

[0004] However, deep learning models still face challenges when processing customer needs, such as insufficient interpretability and difficulty handling information uncertainty when evaluating customer needs. Therefore, how to make evaluation results more realistic is an urgent problem that existing technologies need to solve.

[0005] Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an optimization method for a customer demand mining model based on deep learning to overcome the shortcomings of the existing technology.

[0007] The technical solution of the present invention is:

[0008] An optimization method for a customer demand mining model based on deep learning, the method comprising:

[0009] Data collection and preprocessing;

[0010] Convert the data into a time series model;

[0011] Use historical electricity consumption data to train the model;

[0012] Optimize model hyperparameters using grid search;

[0013] Use model interpretation tools to assess the contribution of different features to model predictions;

[0014] Apply Bayesian methods to quantify forecast uncertainty;

[0015] Deploy the trained model to a production environment;

[0016] Continuously collect new user electricity usage data and regularly retrain the model with new data to adapt to changing user needs.

[0017] Furthermore, converting the data into a time series model comprises the following steps:

[0018] Extracting temporal features from timestamps;

[0019] Create lagged features to capture the autocorrelation of the time series;

[0020] Calculate electricity usage statistics for a certain window in the past;

[0021] Divide time series data into a series of supervised learning datasets;

[0022] Convert the data format into a format suitable for the long short-term memory network deep learning model.

[0023] Furthermore, the method for converting the data format into a format suitable for a deep learning model of a long short-term memory network is: converting the data format into a three-dimensional array including the number of samples, time steps and number of features.

[0024] Furthermore, the data collection includes the user's electricity usage data, including electricity usage, timestamp, user information and weather data.

[0025] Furthermore, the historical electricity consumption data used to train the model is used to capture long-term dependencies in time series through long short-term memory networks.

[0026] Furthermore, the method of optimizing the hyperparameters of the model using grid search includes the following steps:

[0027] Define an evaluation function for model training, use a set of hyperparameters to train the model, and return the evaluation metrics;

[0028] Create a grid search object, passing the model, hyperparameter space, and evaluation metric

[0029] Call the methods of the grid search object to execute the search process;

[0030] After the grid search is complete, check the search results to obtain the best hyperparameter combination and obtain the performance indicators of the corresponding model;

[0031] Retrain the model using the best hyperparameter combination found and evaluate the model's performance on an independent test set.

[0032] Furthermore, the method of using the model interpretation tool to evaluate the contribution of different features to model prediction is:

[0033] Create an interpreter object and pass the model and data to the interpreter;

[0034] The interpreter calculates the importance value, which is calculated as follows:

[0035] Where N is the set of features; S is a subset of N that does not contain feature i; f x (S) is the model prediction value when considering the feature set S; is the average marginal contribution of feature i to the model prediction.

[0036] Furthermore, it also includes presenting the importance values ​​calculated by the explainer to business experts and other stakeholders to gain insights into the data and reveal potential biases and deficiencies in the model.

[0037] The beneficial effects of the present invention are as follows: Compared with existing technologies, the present invention significantly improves the accuracy and predictive capabilities of electricity demand analysis for power industry customers by comprehensively utilizing advanced technologies such as multi-dimensional data collection, efficient data preprocessing, time series analysis, long short-term memory (LSTM) model training, grid search hyperparameter optimization, model interpretation tools, and Bayesian uncertainty quantification. This approach not only improves the shortcomings of traditional methods in fully understanding and accurately evaluating demand, but also provides more transparent and reliable data analysis and decision support through model interpretability and uncertainty assessment, thereby optimizing resource allocation, improving user satisfaction, and better adapting to the development needs of modern power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG1 is a flow chart of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0040] Example 1:

[0041] As shown in FIG1 , a method for optimizing a customer demand mining model based on deep learning includes:

[0042] Data collection and preprocessing;

[0043] Convert the data into a time series model;

[0044] Use historical electricity consumption data to train the model;

[0045] Optimize model hyperparameters using grid search;

[0046] Use model interpretation tools to assess the contribution of different features to model predictions;

[0047] Apply Bayesian methods to quantify forecast uncertainty;

[0048] Deploy the trained model to a production environment;

[0049] Continuously collect new user electricity usage data and regularly retrain the model with new data to adapt to changing user needs.

[0050] Furthermore, converting the data into a time series model comprises the following steps:

[0051] Extracting temporal features from timestamps;

[0052] Create lagged features to capture the autocorrelation of the time series;

[0053] Calculate electricity usage statistics for a certain window in the past;

[0054] Divide time series data into a series of supervised learning datasets;

[0055] Convert the data format into a format suitable for the long short-term memory network deep learning model.

[0056] Furthermore, the method for converting the data format into a format suitable for a deep learning model of a long short-term memory network is: converting the data format into a three-dimensional array including the number of samples, time steps and number of features.

[0057] Furthermore, the data collection includes the user's electricity usage data, including electricity usage, timestamp, user information and weather data.

[0058] Furthermore, the historical electricity consumption data used to train the model is used to capture long-term dependencies in time series through long short-term memory networks.

[0059] Furthermore, the method of optimizing the hyperparameters of the model using grid search includes the following steps:

[0060] Define an evaluation function for model training, use a set of hyperparameters to train the model, and return the evaluation metrics;

[0061] Create a grid search object, passing the model, hyperparameter space, and evaluation metric

[0062] Call the methods of the grid search object to execute the search process;

[0063] After the grid search is complete, check the search results to obtain the best hyperparameter combination and obtain the performance indicators of the corresponding model;

[0064] Retrain the model using the best hyperparameter combination found and evaluate the model's performance on an independent test set.

[0065] Furthermore, the method of using the model interpretation tool to evaluate the contribution of different features to model prediction is:

[0066] Create an interpreter object and pass the model and data to the interpreter;

[0067] The interpreter calculates the importance value, which is calculated as follows:

[0068] Where N is the set of features; S is a subset of N that does not contain feature i; f x (S) is the model prediction value when considering the feature set S; is the average marginal contribution of feature i to the model prediction.

[0069] Furthermore, it also includes presenting the importance values ​​calculated by the explainer to business experts and other stakeholders to gain insights into the data and reveal potential biases and deficiencies in the model.

[0070] Any details not described in detail herein are well known to those skilled in the art. Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the technical solutions of the present invention, and such modifications or equivalents should be encompassed by the claims of the present invention.

Claims

1. An optimization method for a customer demand mining model based on deep learning, characterized in that: The method comprises: Data collection and preprocessing; Convert data into a time series model; Use historical electricity usage data to train the model; Use grid search to optimize the model’s hyperparameters; Use model interpretation tools to assess the contribution of different features to model predictions; Apply Bayesian methods to quantify forecast uncertainty; Deploy the trained model to the production environment; Continuously collect new user electricity usage data and regularly retrain the model with new data to adapt to changing user needs.

2. The optimization method of the customer demand mining model based on deep learning according to claim 1 is characterized in that: The conversion of data into a time series model includes the following steps: Extracting temporal features from timestamps; Create lagged features to capture the autocorrelation of the time series; Calculate electricity consumption statistics for a certain period of time; Divide the time series data into a series of supervised learning datasets; Convert the data format into a format suitable for the deep learning model of Long Short-Term Memory Network.

3. The optimization method of the customer demand mining model based on deep learning according to claim 1 is characterized in that: The method for converting the data format into a format suitable for a deep learning model of a long short-term memory network is: converting the data format into a three-dimensional array including the number of samples, time steps and number of features.

4. The optimization method of the customer demand mining model based on deep learning according to claim 1 is characterized in that: The data collection includes the user's electricity usage data, including power usage, timestamp, user information and weather data.

5. The method for optimizing the customer demand mining model based on deep learning according to claim 1, characterized in that: The historical electricity consumption data used to train the model is used to capture long-term dependencies in time series through a long short-term memory network.

6. The method for optimizing the customer demand mining model based on deep learning according to claim 1, characterized in that: The method of optimizing the hyperparameters of the model using grid search includes the following steps: Define an evaluation function for model training, use a set of hyperparameters to train the model, and return the evaluation metrics; Create a grid search object, pass the model, hyperparameter space and evaluation index, and call the method of the grid search object to execute the search process; After the grid search is complete, check the search results to obtain the best hyperparameter combination and obtain the performance indicators of the corresponding model; Retrain the model using the best hyperparameter combination found and evaluate the model’s performance on an independent test set.

7. The method for optimizing the customer demand mining model based on deep learning according to claim 1, characterized in that: The method of using the model interpretation tool to evaluate the contribution of different features to model prediction is: Create an interpreter object and pass the model and data to the interpreter; The interpreter calculates the importance value, which is calculated as: Where N is the set of features; S is a subset of N that does not contain feature i; f x (S) is the model prediction value when considering the feature set S; is the average marginal contribution of feature i to the model prediction.

8. The method for optimizing the customer demand mining model based on deep learning according to claim 7, characterized in that: This also includes presenting the importance values ​​calculated by the explainer to business experts and other stakeholders to gain insights into the data and reveal potential biases and deficiencies in the model.

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