...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Japan Architectural Review >Electricity load forecasting using clustering and ARIMA model for energy management in buildings
【24h】

Electricity load forecasting using clustering and ARIMA model for energy management in buildings

机译：使用集群和Arima模型在建筑物中能源管理的电力负荷预测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Understanding the energy consumption patterns of buildings and investing efforts toward energy load reduction is important for optimizing resources and conserving energy in buildings. In this research, we proposed a forecasting method for the electricity load of university buildings using a hybrid model comprising a clustering technique and the autoregressive integrated moving average (ARIMA) model. The novel approach includes clustering data of an entire year, including the forecasting day using K‐means clustering, and using the result to forecast the electricity peak load of university buildings. The combination of clustering and the ARIMA model has proved to increase the performance of forecasting rather than that using the ARIMA model alone. Forecasting electricity peak load with appreciable accuracy several hours before peak hours can provide the management authorities with sufficient time to design strategies for peak load reduction. This method can also be implemented in the demand response for reducing electricity bills by avoiding electricity usage during the high electricity rate hours. In this research, we proposed a method for forecasting the electricity load of university buildings using a hybrid model of clustering technique and autoregressive integrated moving average (ARIMA) model. The novel approach discussed in this paper includes clustering one whole year data including the forecasting day using K‐means clustering and using the result to forecast the electricity peak load of university buildings. The combination of clustering and ARIMA model has proved to increase the performance of forecasting rather than ARIMA model alone. This method can be used for energy conservation in buildings.
机译：了解建筑物的能耗模式和投资能力减少的投资努力对于优化建筑物中的资源和节约能源来说是重要的。在这项研究中，我们提出了一种使用包括聚类技术和自回归集成移动平均（ARIMA）模型的混合模型的大学建筑电量的预测方法。新颖的方法包括整年的聚类数据，包括使用K-Means聚类的预测日，并使用结果预测大学建筑的电峰值负荷。集群和ARIMA模型的组合已经证明了增加预测而不是使用Arima模型的性能。预测电力峰值负荷在高峰时段前几小时以明显的精度提供了明显的准确性，可以为管理机构提供足够的时间来设计峰值负荷减少的策略。该方法也可以在需求响应中实施，以通过避免在高电率小时内避免电力票据来减少电费。在这项研究中，我们提出了一种使用聚类技术的混合模型和自回归综合移动平均（ARIMA）模型预测大学建筑电量的方法。本文讨论的新方法包括使用K-Means聚类和使用该结果来聚类包括预测日的一整年数据，以预测大学建筑的电力峰值负荷。集群和ARIMA模型的组合已经证明，仅仅增加了预测而不是Arima模型的性能。该方法可用于建筑物中的节能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号