Energy consumption prediction for households in a society with an ageing population
Social aging significantly impacts household energy consumption patterns and demand, particularly in megacities like Shanghai. This study addresses the gap in understanding high-frequency impacts of aging on energy use by employing advanced machine learning techniques. Using Gaussian Mixture Models...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X24003316 |
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| author | Yan Zou Chen Wang Hina Najam Abdelmohsen A. Nassani Gozal Djuraeva David Oscar |
| author_facet | Yan Zou Chen Wang Hina Najam Abdelmohsen A. Nassani Gozal Djuraeva David Oscar |
| author_sort | Yan Zou |
| collection | DOAJ |
| description | Social aging significantly impacts household energy consumption patterns and demand, particularly in megacities like Shanghai. This study addresses the gap in understanding high-frequency impacts of aging on energy use by employing advanced machine learning techniques. Using Gaussian Mixture Models (GMM) and Finite Mixture Models (FMM), we analyze high-frequency hourly energy consumption data from 14,000 households in Shanghai (2016–2023) to identify distinct consumption patterns and their relationship with household characteristics. The study also simulates future scenarios incorporating demographic aging and income growth. The results reveal that an aging society not only increases overall energy demand but also significantly alters hourly consumption patterns, amplifying disparities between peak and non-peak hours. These shifts, compounded by income growth, highlight the need for tailored energy policies addressing demographic transitions. This research contributes to sustainable energy planning by providing actionable insights into the intersection of aging demographics, economic development, and urban energy consumption. The findings align with the United Nations Sustainable Development Goals (SDGs) by promoting efficient and inclusive energy strategies. |
| format | Article |
| id | doaj-art-c262085d0910418aadc0c45f0d0166ff |
| institution | Kabale University |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-c262085d0910418aadc0c45f0d0166ff2025-01-04T04:56:18ZengElsevierEnergy Strategy Reviews2211-467X2025-01-0157101622Energy consumption prediction for households in a society with an ageing populationYan Zou0Chen Wang1Hina Najam2Abdelmohsen A. Nassani3Gozal Djuraeva4David Oscar5Nantong Institute of Technology, ChinaInstitute of Food and Strategic Reserves, Nanjing University of Finance & Economics, Nanjing, Jiangsu, 210003, China; Corresponding author.Department of Business Studies, Air University, Islamabad, PakistanDepartment of Management, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587, Saudi ArabiaSenior lecturer at Innovative Management Department, Tashkent state university of economics, ChinaYanshan University China, ChinaSocial aging significantly impacts household energy consumption patterns and demand, particularly in megacities like Shanghai. This study addresses the gap in understanding high-frequency impacts of aging on energy use by employing advanced machine learning techniques. Using Gaussian Mixture Models (GMM) and Finite Mixture Models (FMM), we analyze high-frequency hourly energy consumption data from 14,000 households in Shanghai (2016–2023) to identify distinct consumption patterns and their relationship with household characteristics. The study also simulates future scenarios incorporating demographic aging and income growth. The results reveal that an aging society not only increases overall energy demand but also significantly alters hourly consumption patterns, amplifying disparities between peak and non-peak hours. These shifts, compounded by income growth, highlight the need for tailored energy policies addressing demographic transitions. This research contributes to sustainable energy planning by providing actionable insights into the intersection of aging demographics, economic development, and urban energy consumption. The findings align with the United Nations Sustainable Development Goals (SDGs) by promoting efficient and inclusive energy strategies.http://www.sciencedirect.com/science/article/pii/S2211467X24003316Energy consumptionAging societyHousehold structureShanghaiDemographic shiftsExtreme weather |
| spellingShingle | Yan Zou Chen Wang Hina Najam Abdelmohsen A. Nassani Gozal Djuraeva David Oscar Energy consumption prediction for households in a society with an ageing population Energy Strategy Reviews Energy consumption Aging society Household structure Shanghai Demographic shifts Extreme weather |
| title | Energy consumption prediction for households in a society with an ageing population |
| title_full | Energy consumption prediction for households in a society with an ageing population |
| title_fullStr | Energy consumption prediction for households in a society with an ageing population |
| title_full_unstemmed | Energy consumption prediction for households in a society with an ageing population |
| title_short | Energy consumption prediction for households in a society with an ageing population |
| title_sort | energy consumption prediction for households in a society with an ageing population |
| topic | Energy consumption Aging society Household structure Shanghai Demographic shifts Extreme weather |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X24003316 |
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