2021
|
| 17. | Lago, Jesus; Poplavskaya, Ksenia; Suryanarayana, Gowri; De Schutter, Bart A market framework for grid balancing support through imbalances trading Journal Article Renewable and Sustainable Energy Reviews, 137 , pp. 110467, 2021. BibTeX | Links: @article{Lago2021,
title = {A market framework for grid balancing support through imbalances trading},
author = {Jesus Lago and Ksenia Poplavskaya and Gowri Suryanarayana and Bart {De Schutter}},
url = {https://jesuslago.com/wp-content/uploads/Lago2021.pdf},
doi = {10.1016/j.rser.2020.110467},
year = {2021},
date = {2021-03-27},
journal = {Renewable and Sustainable Energy Reviews},
volume = {137},
pages = {110467},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 16. | De Jaeger, Ina ; Lago, Jesus; Saelens., Dirk A probabilistic building characterization method for district energy simulations Journal Article Energy and Buildings, 230 , pp. 110566, 2021. BibTeX | Links: @article{DeJaeger2021,
title = {A probabilistic building characterization method for district energy simulations},
author = {Ina {De Jaeger} and Jesus Lago and Dirk Saelens.},
doi = {10.1016/j.enbuild.2020.110566},
year = {2021},
date = {2021-01-01},
journal = {Energy and Buildings},
volume = {230},
pages = {110566},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2020
|
| 15. | Sapountzoglou, Nikolaos; Lago, Jesus; De Schutter, Bart ; Raison, Bertrand A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids Journal Article Applied Energy, 276 , pp. 115299, 2020. BibTeX | Links: @article{Sapountzoglou2020a,
title = {A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids},
author = {Nikolaos Sapountzoglou and Jesus Lago and Bart {De Schutter} and Bertrand Raison},
url = {https://jesuslago.com/wp-content/uploads/Sapountzoglou2020b.pdf},
doi = {10.1016/j.apenergy.2020.115299},
year = {2020},
date = {2020-10-01},
journal = {Applied Energy},
volume = {276},
pages = {115299},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 14. | Lago, Jesus Incentivizing renewables and reducing grid imbalances through market interaction: A forecasting and control approach PhD Thesis Delft University of Technology, 2020, ISBN: 978-94-6402-444-9. BibTeX | Links: @phdthesis{Lago2020a,
title = {Incentivizing renewables and reducing grid imbalances through market interaction: A forecasting and control approach},
author = {Jesus Lago},
url = {https://jesuslago.com/wp-content/uploads/Lago2020a.pdf},
doi = {https://doi.org/10.4233/uuid:db706644-1397-4e97-99a1-6ed748fe4ed4},
isbn = {978-94-6402-444-9},
year = {2020},
date = {2020-09-28},
school = {Delft University of Technology},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
|
| 13. | Lago, Jesus; Suryanarayana, Gowri; Sogancioglu, Ecem; Schutter, Bart De Optimal Control Strategies for Seasonal Thermal Energy Storage Systems with Market Interaction Journal Article IEEE Transactions on Control Systems Technology, pp. Early Access, 2020. BibTeX | Links: @article{Lago2020,
title = {Optimal Control Strategies for Seasonal Thermal Energy Storage Systems with Market Interaction},
author = {Jesus Lago and Gowri Suryanarayana and Ecem Sogancioglu and Bart De Schutter},
url = {https://jesuslago.com/wp-content/uploads/Lago2020.pdf},
doi = {10.1109/TCST.2020.3016077},
year = {2020},
date = {2020-09-01},
journal = {IEEE Transactions on Control Systems Technology},
pages = {Early Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 12. | Poplavskaya, Ksenia; Lago, Jesus; De Vries, Laurens Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets Journal Article Applied Energy, 270 , pp. 115130, 2020. BibTeX | Links: @article{Poplavskaya2020,
title = {Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets},
author = {Ksenia Poplavskaya and Jesus Lago and Laurens {De Vries}},
url = {https://jesuslago.com/wp-content/uploads/Poplavskaya2020.pdf},
doi = {10.1016/j.apenergy.2020.115130},
year = {2020},
date = {2020-07-15},
journal = {Applied Energy},
volume = {270},
pages = {115130},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 11. | Sapountzoglou, Nikolaos; Lago, Jesus; Raison, Bertrand Fault diagnosis in low voltage smart distribution grids using gradient boosting trees Journal Article Electric Power Systems Research, 182 , 2020, ISSN: 0378-7796. BibTeX | Links: @article{Sapountzoglou2020,
title = {Fault diagnosis in low voltage smart distribution grids using gradient boosting trees},
author = {Nikolaos Sapountzoglou and Jesus Lago and Bertrand Raison},
url = {https://jesuslago.com/wp-content/uploads/Sapountzoglou2020.pdf},
doi = {10.1016/j.epsr.2020.106254},
issn = {0378-7796},
year = {2020},
date = {2020-05-01},
journal = {Electric Power Systems Research},
volume = {182},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2019
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| 10. | Lago, Jesus; De Ridder, Fjo ; Mazairac, Wiet; De Schutter, Bart A 1-dimensional continuous and smooth model for thermally stratified storage tanks including mixing and buoyancy Journal Article Applied Energy, 248 , pp. 640–655, 2019. BibTeX | Links: @article{Lago2019,
title = {A 1-dimensional continuous and smooth model for thermally stratified storage tanks including mixing and buoyancy},
author = {Jesus Lago and Fjo {De Ridder} and Wiet Mazairac and Bart {De Schutter}},
url = {https://jesuslago.com/wp-content/uploads/Lago2019a.pdf},
doi = {10.1016/j.apenergy.2019.04.139},
year = {2019},
date = {2019-08-01},
journal = {Applied Energy},
volume = {248},
pages = {640--655},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 9. | Lago, Jesus; Sogancioglu, Ecem; Suryanarayana, Gowri; De Ridder, Fjo ; De Schutter, Bart Building day-ahead bidding functions for seasonal storage systems: A reinforcement learning approach Conference Proceedings of the IFAC Workshop on Control of Smart Grid and Renewable Energy Systems, 2019. BibTeX | Links: @conference{Lago2019a,
title = {Building day-ahead bidding functions for seasonal storage systems: A reinforcement learning approach},
author = {Jesus Lago and Ecem Sogancioglu and Gowri Suryanarayana and Fjo {De Ridder} and Bart {De Schutter}},
url = {https://jesuslago.com/wp-content/uploads/Lago2019.pdf},
doi = {10.1016/j.ifacol.2019.08.258},
year = {2019},
date = {2019-06-10},
booktitle = {Proceedings of the IFAC Workshop on Control of Smart Grid and Renewable Energy Systems},
pages = {488--493},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 8. | De Jaeger, Ina ; Lago, Jesus; Saelens., Dirk A probabilistic approach to allocate building parameters within district energy simulations Conference Proceedings of the Urban Energy Simulation Conference, 2019. BibTeX | Links: @conference{DeJaeger2019,
title = {A probabilistic approach to allocate building parameters within district energy simulations},
author = {Ina {De Jaeger} and Jesus Lago and Dirk Saelens.},
url = {https://jesuslago.com/wp-content/uploads/DeJaeger2018.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the Urban Energy Simulation Conference},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 7. | Pippia, Tomas; Lago, Jesus; De Coninck, Roel ; Sijs, Joris; De Schutter, Bart Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building Conference Proceedings of the 15th IEEE International Conference on Automation Science and Engineering, IEEE, 2019. BibTeX | Links: @conference{Pippia2019,
title = {Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building},
author = {Tomas Pippia and Jesus Lago and Roel {De Coninck} and Joris Sijs and Bart {De Schutter}},
url = {https://jesuslago.com/wp-content/uploads/Pippia2019.pdf},
doi = {10.1109/coase.2019.8842846},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 15th IEEE International Conference on Automation Science and Engineering},
pages = {1243--1248},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
2018
|
| 6. | Lago, Jesus; Ridder, Fjo De; Vrancx, Peter; Schutter, Bart De Forecasting day-ahead electricity prices in Europe: Ŧhe importance of considering market integration Journal Article Applied Energy, 211 , pp. 890–903, 2018, ISSN: 0306-2619. Abstract | BibTeX | Links: @article{Lago2018e,
title = {Forecasting day-ahead electricity prices in Europe: Ŧhe importance of considering market integration},
author = {Jesus Lago and Fjo De Ridder and Peter Vrancx and Bart De Schutter},
doi = {10.1016/j.apenergy.2017.11.098},
issn = {0306-2619},
year = {2018},
date = {2018-02-01},
urldate = {2018-01-02},
journal = {Applied Energy},
volume = {211},
pages = {890--903},
abstract = {Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features. |
| 5. | Lago, Jesus; Brabandere, Karel De; Ridder, Fjo De; Schutter, Bart De Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data Journal Article Solar Energy, 173 , pp. 566–577, 2018. BibTeX | Links: @article{Lago2018b,
title = {Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data},
author = {Jesus Lago and Karel De Brabandere and Fjo De Ridder and Bart De Schutter},
doi = {10.1016/j.solener.2018.07.050},
year = {2018},
date = {2018-01-01},
journal = {Solar Energy},
volume = {173},
pages = {566--577},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 4. | Lago, Jesus; Brabandere, Karel De; Ridder, Fjo De; Schutter, Bart De A generalized model for short-term forecasting of solar irradiance Conference Proceedings of the 2018 IEEE Conference on Decision and Control, IEEE, 2018. BibTeX | Links: @conference{Lago2018d,
title = {A generalized model for short-term forecasting of solar irradiance},
author = {Jesus Lago and Karel De Brabandere and Fjo De Ridder and Bart De Schutter},
doi = {10.1109/cdc.2018.8618693},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 2018 IEEE Conference on Decision and Control},
pages = {3165--3170},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 3. | Lago, Jesus; Erhard, Michael; Diehl, Moritz Warping model predictive control for application in control of a real airborne wind energy system Journal Article Control Engineering Practice, 78 , pp. 65 - 78, 2018, ISSN: 0967-0661. BibTeX | Links: @article{Lago2018c,
title = {Warping model predictive control for application in control of a real airborne wind energy system},
author = {Jesus Lago and Michael Erhard and Moritz Diehl},
doi = {10.1016/j.conengprac.2018.06.008},
issn = {0967-0661},
year = {2018},
date = {2018-01-01},
journal = {Control Engineering Practice},
volume = {78},
pages = {65 - 78},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 2. | Lago, Jesus; Ridder, Fjo De; Schutter, Bart De Forecasting spot electricity prices: Đeep learning approaches and empirical comparison of traditional algorithms Journal Article Applied Energy, 221 , pp. 386–405, 2018. Abstract | BibTeX | Links: @article{Lago2018a,
title = {Forecasting spot electricity prices: Đeep learning approaches and empirical comparison of traditional algorithms},
author = {Jesus Lago and Fjo De Ridder and Bart De Schutter},
doi = {10.1016/j.apenergy.2018.02.069},
year = {2018},
date = {2018-01-01},
journal = {Applied Energy},
volume = {221},
pages = {386--405},
abstract = {In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts..},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.. |
| 1. | Suryanarayana, Gowri; Lago, Jesus; Geysen, Davy; Aleksiejuk, Piotr; Johansson, Christian Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods Journal Article Energy, 157 , pp. 141–149, 2018. BibTeX | Links: @article{Suryanarayana2018,
title = {Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods},
author = {Gowri Suryanarayana and Jesus Lago and Davy Geysen and Piotr Aleksiejuk and Christian Johansson},
doi = {10.1016/j.energy.2018.05.111},
year = {2018},
date = {2018-01-01},
journal = {Energy},
volume = {157},
pages = {141--149},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|