activity
20122022
most citedSpatio-temporal graph neural networks for multi-site PV power forecasting

260 citations · 1.1k across the 23 of their papers we have counts for

collaborators

23 papers

eess.SY2022

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Paul Scharnhorst, Baptiste Schubnel, Rafael E. Carrillo +2

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution…

eess.SY2022★ 57 cited

DRT-based modelling framework for Li-ion cells

Pietro Iurilli, Claudio Brivio, Rafael E. Carrillo +1

The correct assessment of battery states is essential to maximize battery pack performances while ensuring reliable and safe operation. This work introduces EIS2MOD, a novel modell…

eess.SY2021★ 12 cited

Smart sensors network for accurate indirect heat accounting in apartment buildings

Yves Stauffer, Fabio Saba, Rafael E. Carrillo +3

A new method for accurate indirect heat accounting in apartment buildings has been recently developed by the Centre Suisse d'Electronique et de Microtechnique (CSEM). It is based o…

cs.LG2021★ 260 cited

Spatio-temporal graph neural networks for multi-site PV power forecasting

Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet +1

Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that com…

eess.SY2020★ 17 cited

State space models for building control: how deep should you go?

Baptiste Schubnel, Rafael E. Carrillo, Paolo Taddeo +4

Power consumption in buildings show non-linear behaviors that linear models cannot capture whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternat…

eess.SY2020★ 17 cited

A hybrid learning method for system identification and optimal control

Baptiste Schubnel, Rafael E. Carrillo, Pierre-Jean Alet +1

We present a three-step method to perform system identification and optimal control of non-linear systems. Our approach is mainly data driven and does not require active excitation…