BIGDATA 2014, IEEE International Conference on Big Data, 27-30 October 2014, Washington DC, USA
In this paper, we analyze a public dataset of electricity consumption collected over 3,800 households for one year and half. We show that some socio-economic factors are critical indicators to forecast households' daily peak (and total) load. By using a random forests model, we show that the daily load can be predicted accurately at a fine temporal granularity. Differently from many state-of-the-art techniques based on support vector machines, our model allows to derive a set of heuristic rules that are highly interpretable and easy to fuse with human experts domain knowledge. Lastly, we quantify the different importance of each socio-economic feature in the prediction task.
Type:
Conference
City:
Washington
Date:
2014-10-27
Department:
Data Science
Eurecom Ref:
4461
Copyright:
© 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
See also:
