Details
Presenter(s)
![Daniele Linaro Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/11551.jpg?h=5aafe909&itok=gqQmHcFk)
Display Name
Daniele Linaro
- Affiliation
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AffiliationPolitecnico di Milano
- Country
Abstract
Load forecasting plays a crucial role in the operations of electric utilities and so several algorithms have been developed over the years to tackle this problem: most recent solutions use machine learning techniques to increase the granularity of the prediction. Here, we employ a framework based on long short-term memory networks to estimate the average power consumption of a single building equipped with solar panels. We show which measurements are more important for an accurate forecast and test several prediction horizons in order to find the best trade-off between training speed and prediction accuracy.