Graduate School and Research Center in Digital Sciences

On the impact of socio-economic factors on power load forecasting

Han, Yufei; Sha, Xiaolan; Grover-Silva, Etta; Michiardi, Pietro

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.

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Title:On the impact of socio-economic factors on power load forecasting
Department:Data Science
Eurecom ref:4461
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Bibtex: @inproceedings{EURECOM+4461, doi = {}, year = {2014}, title = {{O}n the impact of socio-economic factors on power load forecasting}, author = {{H}an, {Y}ufei and {S}ha, {X}iaolan and {G}rover-{S}ilva, {E}tta and {M}ichiardi, {P}ietro}, booktitle = {{BIGDATA} 2014, {IEEE} {I}nternational {C}onference on {B}ig {D}ata, 27-30 {O}ctober 2014, {W}ashington {DC}, {USA} }, address = {{W}ashington , {UNITED} {STATES}}, month = {10}, url = {} }
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