通过代技术和家电趋势分析,优化加纳的住宅需求响应
Timothy King Avordeh1, Samuel Gyamfi2
1Research and Consultancy Centre, University of Professional Studies, Accra (UPSA), P.O. Box 149, Legon, Accra, Ghana.
Heliyon
|February 21, 2024
概括
这项研究使用K-means集群识别了加纳的高能耗家庭. 了解电器使用优化需求响应策略,以节省电力消耗.
科学领域:
- 能源管理 能源管理
- 消费者行为分析 消费者行为分析
- 在能源系统中数据挖掘.
背景情况:
- 家庭电力消费模式对于制定有效的需求响应 (DR) 策略至关重要.
- 了解影响消费者行为因素是优化能源使用的关键.
- 加纳大阿克拉地区在管理电力需求方面面临挑战.
研究的目的:
- 探索影响家庭电力消费行为的因素.
- 根据使用K-means集群的消费模式对家庭进行分组.
- 为加纳制定有针对性的需求响应策略提供信息.
主要方法:
- K-意味着对家庭数据应用的集群算法.
- 图形得分和主要组件分析 (PCA) 用于集群验证.
- 整合定性 (调查) 和定量 (智能电表) 数据.
主要成果:
- 确定了具有不同电力消费模式的不同家庭集群.
- 集群1和3被确定为高能耗,特别是水和冷却设备.
- 在集群中观察到的电压,强度,功耗和智能电表数据的差异.
结论:
- 消费者行为洞察对于提高需求响应计划至关重要.
- 基于已识别的消费集群,可以制定有针对性的能源管理策略.
- 该研究为加纳的能源利益相关者提供了指导,以优化电力消耗和实施DR倡议.
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