The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors.

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident’s time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision.

This paper aims to estimate county-level CO₂ emissions across China and identify the socioeconomic and spatial factors associated with their variation. Current research lacks a consistent national county-level dataset after 2017. To address the lack of consistent national data after 2017, we developed a unified dataset covering 2735 county-level units from 1997 to 2022. Seven machine learning models were evaluated using 21,197 observations for 1997–2017, after which the best-performing model was used to estimate emissions for 2018–2022. LightGBM achieved a test R² value of 0.9529 and an MAPE of 15.32%, while SHAP identified secondary-sector value added as the most influential predictor, followed by provincial area and GDP. Estimated emissions exceeded 12,000 Mt in 2022, whereas carbon intensity declined by approximately 10% from 2018 to 2022 despite rising per-capita emissions. These findings indicate improving carbon efficiency alongside persistent regional disparities. Industrial and resource-dependent counties should prioritize structural upgrading and cleaner energy substitution, while developed regions should consolidate efficiency gains and control absolute emissions.