Industrial Problem Solution
Development of verification algorithm for city gas meter reading and usage rate prediction
2020-06-151. Company introduction
As of 2019, it has supplied city gas to about 1.43 million households in Busan, and won the first place in the city gas category for 16 consecutive years from 2003 to 2018 with a focus on customer satisfaction management.
2. Problem Background and Summary
Customer continuously complains about current meter-based methods, such as meter failure or billing errors, while gas usage is being inspected/charged every month.
To solve the problem of determining meter error and abnormal generation through historical data analysis, and obtaining reliable estimates for non-detection generation by predicting future usage
3. Solving Process
In the data preprocessing process for the development of predictive algorithms, the analysis was conducted by calculating the distance of the probability distribution, excluding those with large standard deviations or missing values.
It is almost impossible to predict by creating individual models for each generation, and predicting all 12 months for the entire generation with one model is expected to be unreliable, developing a monthly model that takes time and temperature into account to predict future usage
Developed abnormal data classification models through the application of rule-based algorithms, such as those with rapidly increasing usage, those with equal or less than ±10% usage for two years, those with less average usage and less usage, and those with higher usage overall.
4. Ripple effects and future plans
It is expected that customers' complaints will be reduced by developing reliable usage prediction algorithms through analysis of historical data patterns for each generation, rather than an integrated usage prediction method such as the previous year-on-year method.
Development of Logic using 1D CNN Deep Learning to select new estimated values. It has improved accuracy by 28% compared to current method of same month last year and obtained faster processing speed compared to other time series deep learning models such as LSTM.
Rule-based algorithms that classify abnormal generations can easily be controlled by corporate officials, enabling them to classify according to the situation.
Some of the abnormal data found during pre-processing of data will be improved after checking whether it is actually abnormal.
The developed 1D CNN Deep Learning model / rule-based algorithm will be reflected in the business system later.
1. Company introduction
As of 2019, it has supplied city gas to about 1.43 million households in Busan, and won the first place in the city gas category for 16 consecutive years from 2003 to 2018 with a focus on customer satisfaction management.
2. Problem Background and Summary
Customer continuously complains about current meter-based methods, such as meter failure or billing errors, while gas usage is being inspected/charged every month.
To solve the problem of determining meter error and abnormal generation through historical data analysis, and obtaining reliable estimates for non-detection generation by predicting future usage
3. Solving Process
In the data preprocessing process for the development of predictive algorithms, the analysis was conducted by calculating the distance of the probability distribution, excluding those with large standard deviations or missing values.
It is almost impossible to predict by creating individual models for each generation, and predicting all 12 months for the entire generation with one model is expected to be unreliable, developing a monthly model that takes time and temperature into account to predict future usage
Developed abnormal data classification models through the application of rule-based algorithms, such as those with rapidly increasing usage, those with equal or less than ±10% usage for two years, those with less average usage and less usage, and those with higher usage overall.
4. Ripple effects and future plans
It is expected that customers' complaints will be reduced by developing reliable usage prediction algorithms through analysis of historical data patterns for each generation, rather than an integrated usage prediction method such as the previous year-on-year method.
Development of Logic using 1D CNN Deep Learning to select new estimated values. It has improved accuracy by 28% compared to current method of same month last year and obtained faster processing speed compared to other time series deep learning models such as LSTM.
Rule-based algorithms that classify abnormal generations can easily be controlled by corporate officials, enabling them to classify according to the situation.
Some of the abnormal data found during pre-processing of data will be improved after checking whether it is actually abnormal.
The developed 1D CNN Deep Learning model / rule-based algorithm will be reflected in the business system later.