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

Abstract : 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 socioeconomic 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 socioeconomic feature in the prediction task.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01223513
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Yufei Han, Xiaolan Sha, Etta Grover-Silva, Pietro Michiardi. On the impact of socio-economic factors on power load forecasting. IEEE BigData, 2014, ⟨10.1109/BigData.2014.7004299⟩. ⟨hal-01223513⟩

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