An in-depth study of forecasting household electricity demand using realistic datasets

Chien Yu Kuo, Ming Feng Lee, Chia Lin Fu, Yao-Hua Ho, Ling-Jyh Chen

研究成果: 書貢獻/報告類型會議貢獻

4 引文 斯高帕斯(Scopus)

摘要

Data analysis and accurate forecasts of electricity demand are crucial to help both suppliers and consumers understand their detailed electricity footprints and improve their awareness about their impacts to the ecosystem. Several studies of the subject have been conducted in recent years, but they are either comprehension-oriented without practical merits; or they are forecast-oriented and do not consider per-consumer cases. To address this gap, in this paper, we conduct data analysis and evaluate the forecasting of household electricity demand using three realistic datasets of geospatial and lifestyle diversity. We investigate the correlations between household electricity demand and different external factors, and perform cluster analysis on the datasets using an exhaustive set of parameter settings. To evaluate the accuracy of electricity demand forecasts in different datasets, we use the support vector regression method. The results demonstrate that the medium mean absolute percentage error (MAPE) can be reduced to 15.6% for household electricity demand forecasts when proper configurations are used.

原文英語
主出版物標題e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems
發行者Association for Computing Machinery
頁面145-155
頁數11
ISBN(列印)9781450328197
DOIs
出版狀態已發佈 - 2014 一月 1
事件5th ACM International Conference on Future Energy Systems, e-Energy 2014 - Cambridge, 英国
持續時間: 2014 六月 112014 六月 13

出版系列

名字e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems

其他

其他5th ACM International Conference on Future Energy Systems, e-Energy 2014
國家英国
城市Cambridge
期間14/6/1114/6/13

    指紋

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

引用此

Kuo, C. Y., Lee, M. F., Fu, C. L., Ho, Y-H., & Chen, L-J. (2014). An in-depth study of forecasting household electricity demand using realistic datasets. 於 e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems (頁 145-155). (e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems). Association for Computing Machinery. https://doi.org/10.1145/2602044.2602055