An interpretable prediction of FCM driven by small samples for energy analysis based on air quality prediction

ABSTRACT

In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance.

Implications: At present, China’s air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions.

Data Availability Statement

Please refer to the National Bureau of statistics (https://data.stats.gov.cn/) for energy production and consumptions data; Beijing Municipal Ecology and Environment Bureau (sthjj.beijing.gov.cn/) for air pollution.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by key project of science and technology Planning of Beijing Municipal Education Commission “Interpretable prediction of urban air quality driven by data, grant number No.Science and Technology Planning of Beijing Municipal Education Commission KZ202110017025, National Natural Science Foundation Project “Exploring the construction of fuzzy cognitive map and its data mining method for haze formation,” grant number No. National Natural Science Foundation of China 71601022.

Notes on contributors

Zhen Peng

Zhen Peng received the Ph.D. degree in computer application technology from the University of Science and Technology. She is currently a Professor with Beijing Institute of Petrochemical Technology. Her current research interests include data mining and fuzzy cognitive maps, with special interests in air pollution analytics.

Caixiao Zhang

Caixiao Zhang’s main research direction is data analysis and data mining of air pollution.

Boyang Cao

Boyang Cao’s main research direction is data analysis and game anlaysis of air pollution.

Zitao Hong

Zitao Hong mainly studies the field of air pollution and is good at analyzing air pollution through machine learning or deep learning methods.

Xue Han

Xue Han graduated from research Center for Eco-Environmental Sciences, Chinese Academy of Sciences and is good at eco-environmental analysis and research.