Publications

@article{Botman2022,

title = {A scalable ensemble approach to forecast the electricity consumption of households},

author = { Lola Botman and Jonas Soenen and Konstantinos Theodorakos and Aras Yurtman and Jessa Bekker and Koen Vanthournout and Hendrik Blockeel and Bart De Moor and Jesus Lago},

url = {https://jesuslago.com/wp-content/uploads/A_scalable_ensemble_approach_to_forecast_the_electricity_consumption_of_households.pdf},

doi = {10.1109/TSG.2022.3191399},

year  = {2022},

date = {2022-07-15},

urldate = {2022-07-15},

journal = {IEEE Transactions on Smart Grid},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jkedrzejewski2022electricity,

title = {Electricity Price Forecasting: The Dawn of Machine Learning},

author = {Arkadiusz Jędrzejewski and Jesus Lago and Grzegorz Marcjasz and Rafał Weron},

url = {https://arxiv.org/ftp/arxiv/papers/2204/2204.00883.pdf},

doi = {10.1109/MPE.2022.3150809},

year  = {2022},

date = {2022-04-21},

urldate = {2022-01-01},

journal = {IEEE Power and Energy Magazine},

volume = {20},

number = {3},

pages = {24--31},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Soares2022,

title = {Efficient temperature estimation for thermally stratified storage tanks with buoyancy and mixing effects},

author = {Ana Soares and Juliano Camargo and Jad Al-Koussa and Jan Diriken and Johan Van Bael and Jesus Lago},

doi = {10.1016/j.est.2022.104488},

year  = {2022},

date = {2022-04-01},

urldate = {2022-06-01},

journal = {Journal of Energy Storage},

volume = {50},

pages = {104488},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Poplavskaya2021,

title = {Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design},

author = {Ksenia Poplavskaya and Jesus Lago and Stefan Strömer and Laurens Vries},

url = {https://jesuslago.com/wp-content/uploads/1-s2.0-S030142152100392X-main.pdf},

doi = {10.1016/j.enpol.2021.112522},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

journal = {Energy Policy},

volume = {158},

pages = {112522},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pippia2021,

title = {Scenario-based nonlinear model predictive control for building heating systems},

author = {Tomas Pippia and Jesus Lago and Roel De Coninck and Bart De Schutter},

url = {https://jesuslago.com/wp-content/uploads/1-s2.0-S0378778821003923-main.pdf},

doi = {10.1016/j.enbuild.2021.111108},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

journal = {Energy and Buildings},

volume = {247},

pages = {111108},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{VanDerHeijden2021,

title = {Electricity price forecasting in European Day Ahead Markets: a greedy consideration of market integration},

author = {Ties Van Der Heijden and Jesus Lago and Peter Palensky and Edo Abraham},

url = {https://jesuslago.com/wp-content/uploads/Electricity_Price_Forecasting_in_European_Day_Ahead_Markets_A_Greedy_Consideration_of_Market_Integration.pdf},

doi = {10.1109/ACCESS.2021.3108629},

year  = {2021},

date = {2021-08-30},

urldate = {2021-08-30},

journal = {IEEE Access},

volume = {9},

pages = {119954--119966},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Suryanarayana2021,

title = {A data driven method for optimal sensor placement in multi-zone buildings},

author = {Gowri Suryanarayana and Javier Arroyo and Lieve Helsen and Jesus Lago},

url = {https://jesuslago.com/wp-content/uploads/1-s2.0-S0378778821002401-main.pdf},

doi = {10.1016/j.enbuild.2021.110956},

year  = {2021},

date = {2021-07-15},

urldate = {2021-07-15},

journal = {Energy and Buildings},

volume = {243},

pages = {110956},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lago2021b,

title = {Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark},

author = {Jesus Lago and Grzegorz Marcjasz and Bart De Schutter and Rafał Weron},

url = {https://jesuslago.com/wp-content/uploads/1-s2.0-S0306261921004529-main-1.pdf},

doi = {10.1016/j.apenergy.2021.116983},

year  = {2021},

date = {2021-07-01},

urldate = {2021-07-01},

journal = {Applied Energy},

volume = {293},

pages = {116983},

abstract = {While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}