City Gas Load Forecasting Based on PCCs-CNN-LSTM Model

Zai Guangjun, Zhang Yuwei, Wu Sen, Tian Zhao
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:246-252, 2024.

Abstract

The forecast of urban gas load is of great significance for the safety and stability of gas supply, to ensure the normal production activities of residents. The influence factors of sunshine duration were introduced, and the nine identified influencing factors were analyzed by Pearson correlation coefficient (PCCs). According to the correlation, the optimal input was selected one by one. The influencing factors with high correlation were used as the input of Convolutional Neural Networks (CNN) and Long short-term memory (LSTM), respectively, to forecast the daily load, monthly load and quarterly load of urban gas, and verify their accuracy and effectiveness. The results show that the optimal number of input factors for daily load forecasting and monthly load forecasting is 5, and the optimal number of input factors for quarterly load forecasting is 8. For daily load forecasting, the absolute percentage errors of monthly load forecasting and quarterly load forecasting of PCS- CNN-LSTM model are 3.94%, 4.61% and 5.73% respectively. The root mean square error and mean absolute error of PCS-CNN-LSTM model are better than that of a single LSTM model.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-guangjun24a, title = {City Gas Load Forecasting Based on PCCs-CNN-LSTM Model}, author = {Guangjun, Zai and Yuwei, Zhang and Sen, Wu and Zhao, Tian}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {246--252}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/guangjun24a/guangjun24a.pdf}, url = {https://proceedings.mlr.press/v245/guangjun24a.html}, abstract = {The forecast of urban gas load is of great significance for the safety and stability of gas supply, to ensure the normal production activities of residents. The influence factors of sunshine duration were introduced, and the nine identified influencing factors were analyzed by Pearson correlation coefficient (PCCs). According to the correlation, the optimal input was selected one by one. The influencing factors with high correlation were used as the input of Convolutional Neural Networks (CNN) and Long short-term memory (LSTM), respectively, to forecast the daily load, monthly load and quarterly load of urban gas, and verify their accuracy and effectiveness. The results show that the optimal number of input factors for daily load forecasting and monthly load forecasting is 5, and the optimal number of input factors for quarterly load forecasting is 8. For daily load forecasting, the absolute percentage errors of monthly load forecasting and quarterly load forecasting of PCS- CNN-LSTM model are 3.94%, 4.61% and 5.73% respectively. The root mean square error and mean absolute error of PCS-CNN-LSTM model are better than that of a single LSTM model.} }
Endnote
%0 Conference Paper %T City Gas Load Forecasting Based on PCCs-CNN-LSTM Model %A Zai Guangjun %A Zhang Yuwei %A Wu Sen %A Tian Zhao %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-guangjun24a %I PMLR %P 246--252 %U https://proceedings.mlr.press/v245/guangjun24a.html %V 245 %X The forecast of urban gas load is of great significance for the safety and stability of gas supply, to ensure the normal production activities of residents. The influence factors of sunshine duration were introduced, and the nine identified influencing factors were analyzed by Pearson correlation coefficient (PCCs). According to the correlation, the optimal input was selected one by one. The influencing factors with high correlation were used as the input of Convolutional Neural Networks (CNN) and Long short-term memory (LSTM), respectively, to forecast the daily load, monthly load and quarterly load of urban gas, and verify their accuracy and effectiveness. The results show that the optimal number of input factors for daily load forecasting and monthly load forecasting is 5, and the optimal number of input factors for quarterly load forecasting is 8. For daily load forecasting, the absolute percentage errors of monthly load forecasting and quarterly load forecasting of PCS- CNN-LSTM model are 3.94%, 4.61% and 5.73% respectively. The root mean square error and mean absolute error of PCS-CNN-LSTM model are better than that of a single LSTM model.
APA
Guangjun, Z., Yuwei, Z., Sen, W. & Zhao, T.. (2024). City Gas Load Forecasting Based on PCCs-CNN-LSTM Model. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:246-252 Available from https://proceedings.mlr.press/v245/guangjun24a.html.

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