SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus
To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/3/194 |
| _version_ | 1797471112222736384 |
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| author | Changfeng Jing Shasha Guo Hongyang Zhang Xinxin Lv Dongliang Wang |
| author_facet | Changfeng Jing Shasha Guo Hongyang Zhang Xinxin Lv Dongliang Wang |
| author_sort | Changfeng Jing |
| collection | DOAJ |
| description | To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management. |
| first_indexed | 2024-03-09T19:44:55Z |
| format | Article |
| id | doaj.art-f831b8d9566645278ff74a70112bd449 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T19:44:55Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-f831b8d9566645278ff74a70112bd4492023-11-24T01:28:38ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-03-0111319410.3390/ijgi11030194SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University CampusChangfeng Jing0Shasha Guo1Hongyang Zhang2Xinxin Lv3Dongliang Wang4School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaLaboratory Management Section, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaTo achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management.https://www.mdpi.com/2220-9964/11/3/194dashboardelectricity managementvisualizationheterogeneous user group |
| spellingShingle | Changfeng Jing Shasha Guo Hongyang Zhang Xinxin Lv Dongliang Wang SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus ISPRS International Journal of Geo-Information dashboard electricity management visualization heterogeneous user group |
| title | SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus |
| title_full | SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus |
| title_fullStr | SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus |
| title_full_unstemmed | SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus |
| title_short | SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus |
| title_sort | smartele smart electricity dashboard for detecting consumption patterns a case study at a university campus |
| topic | dashboard electricity management visualization heterogeneous user group |
| url | https://www.mdpi.com/2220-9964/11/3/194 |
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