RESEARCH ON CLASSIFICATION AND PREDICTION METHOD OF RURAL RESIDENTS&#x02019;ACCEPTANCE FOR CLEAN HEATING BASED ON MULTI-FACTOR WEIGHT ANALYSIS

Zhu Kexin; Luo Xi; Liu Xiaojun; Gao Yaru

doi:10.19912/j.0254-0096.tynxb.2023-0977

PDF(995 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (8) : 249-255. DOI: 10.19912/j.0254-0096.tynxb.2023-0977

RESEARCH ON CLASSIFICATION AND PREDICTION METHOD OF RURAL RESIDENTS’ACCEPTANCE FOR CLEAN HEATING BASED ON MULTI-FACTOR WEIGHT ANALYSIS

Zhu Kexin¹, Luo Xi^2,3, Liu Xiaojun^1,3, Gao Yaru¹

Author information +

History +

Abstract

In this study, a classification model based on multi-factor weight analysis （K-means-EWM-BP） is proposed to forecast the acceptability of clean heating for rural residents. Firstly, rural residents are classified based on data from a field survey by taking gender, age, education level, and total annual household income as clustering characteristics. Secondly, on the basis of classification, multi-factor weight analysis is carried out on the influence factors of the acceptability of clean heating of various rural residents. Finally, the K-mean-EWM-BP model is constructed to forecast and verify the acceptability of clean heating for rural residents.The results show that： 1） the rural residents can be divided into three categories, with 31% influenced by education level（category 1）, 43% by annual household income（category 2）, and 26% by gender（category 3）. 2） The forecasted acceptance rate of clean heating for rural residents in category 1 is 95%, the forecasted acceptance rate of clean heating for rural residents in category 2 is 100%, and the forecasted acceptance rate of clean heating for rural residents in category 3 is 72%. 3） The K-means-EWM-BP model achieves an accuracy of 91.43% in forecasting the acceptance rate of clean heating by farmers, surpassing both the EWM-BP model （with an accuracy of 87.14%） and the BP model （with an accuracy of 80%）. Meanwhile, the root mean square error of the K-means-EWM-BP model declines by 0.01 and 0.06 relative to the EWM-BP model and the BP model, respectively.

Key words

renewable energy / rural areas / forecasting / multi-factor weight analysis / clean heating

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhu Kexin, Luo Xi, Liu Xiaojun, Gao Yaru. RESEARCH ON CLASSIFICATION AND PREDICTION METHOD OF RURAL RESIDENTS’ACCEPTANCE FOR CLEAN HEATING BASED ON MULTI-FACTOR WEIGHT ANALYSIS[J]. Acta Energiae Solaris Sinica. 2024, 45(8): 249-255 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0977

References

[1] 朱涛, 常向阳, 朱方林, 等. 江苏县域农村屋顶分布式光伏发电系统建设自然社会影响因素实证分析[J]. 太阳能学报, 2023, 44(5): 217-225.
ZHU T, CHANG X Y, ZHU F L, et al.Empirical analysis of natural and social factors affecting newly installed capacity of rural rooftop distributed photovoltaic system at county level in Jiangsu Province[J]. Acta energiae solaris sinica, 2023, 44(5): 217-225.
[2] 单明, 刘彦青, 孙涛, 等. 北方农村清洁取暖区域性典型案例实施方案及经验总结[J]. 环境与可持续发展, 2020, 45(3): 50-56.
SHAN M, LIU Y Q, SUN T, et al.Implementation scheme and experience summary of regional typical cases of clean heating in northern rural China[J]. Environment and sustainable development, 2020, 45(3): 50-56.
[3] LIU J H, LUO X, LIU X J, et al.Rural residents’acceptance of clean heating: an extended technology acceptance model considering rural residents’livelihood capital and perception of clean heating[J]. Energy and buildings, 2022, 267: 112154.
[4] 姚玉璧, 郑绍忠, 杨扬, 等. 中国太阳能资源评估及其利用效率研究进展与展望[J]. 太阳能学报, 2022, 43(10): 524-535.
YAO Y B, ZHENG S Z, YANG Y, et al.Progress and prospects on solar energy resource evaluation and utilization efficiency in China[J]. Acta energiae solaris sinica, 2022, 43(10): 524-535.
[5] YANG X E, LIU S L, ZOU Y L, et al.Energy-saving potential prediction models for large-scale building: a state-of-the-art review[J]. Renewable and sustainable energy reviews, 2022, 156: 111992.
[6] 赵新民, 姜蔚, 程文明. 基于计划行为理论的农村居民参与人居环境治理意愿研究: 以新疆为例[J]. 生态与农村环境学报, 2021, 37(4): 439-447.
ZHAO X M, JIANG W, CHENG W M.Study on the willingness of rural residents to participate in the environmental governance of human settlements based on the theory of planned behavior: taking Xinjiang as an example[J]. Journal of ecology and rural environment, 2021, 37(4): 439-447.
[7] 吕晨, 伍鹏程, 曹丽斌, 等. 清洁取暖政策对北方农村地区能源结构的影响: 以鹤壁市为例[J]. 环境工程, 2019, 37(7): 215-220, 165.
LYU C, WU P C, CAO L B, et al.Influence of clean heating policy on energy structure in northern rural areas: a case study of Hebi, China[J]. Environmental engineering, 2019, 37(7): 215-220, 165.
[8] 任玉珑, 刘焕, 望玉丽, 等. 基于熵权法和支持向量机的中长期电力负荷预测[J]. 统计与决策, 2009(14): 46-48.
REN Y L, LIU H, WANG Y L, et al.Medium and long-term power load forecasting based on entropy weight method and support vector machine[J]. Statistics & decision, 2009(14): 46-48.
[9] LI F, LI T.Tourism consumer demand forecasting under the background of big data[J]. Mathematical problems in engineering, 2022, 2022: 4335718.
[10] XIE L Y, ZHOU O F.What improves subjective welfare during energy transition? Evidence from the clean heating program in China[J]. Energy and buildings, 2021, 253: 111500.
[11] LI N, LUO X, LUO F Z, et al.Exploring the influencing factors of Chinese rural households’ clean heating choice considering the attitude-behavior gap based on two-level classification methods[J]. Energy and buildings, 2022, 273: 112357.
[12] 张舸, 周志鹏, 周浩. 基于数据挖掘的危房改造意愿影响因素研究[J]. 建筑节能, 2022, 50(12): 106-110, 118.
ZHANG G, ZHOU Z P, ZHOU H.Influencing factors of dangerous housing transformation willingness based on data mining[J]. Building energy efficiency, 2022, 50(12): 106-110, 118.
[13] CHEN J Q.Fault prediction of a transformer bushing based on entropy weight TOPSIS and gray theory[J]. Computing in science and engineering, 2019, 21(6): 55-62.
[14] ZOU X L.Analysis of consumer online resale behavior measurement based on machine learning and BP neural network[J]. Journal of intelligent & fuzzy systems, 2021, 40(2): 2121-2132.
[15] 孙唯, 孙佳欣, 任晓, 等. 基于BP神经网络的盐龙湖饮用水源水质预测[J]. 城镇供水, 2021(2): 69-74, 109.
SUN W, SUN J X, REN X, et al.Prediction of drinking water quality of yanlong lake based on BP neural network[J]. City and town water supply, 2021(2): 69-74, 109.