Publications

(2020). Evaluating Ensemble Post-Processing for Wind Power Forecasts.

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(2020). Probabilistic Load Forecasting Using Post-Processed Weather Ensemble Predictions. Journal of the Operational Research Society (submitted).

(2020). Forecasting energy time series with profile neural networks. Proceedings of the Eleventh ACM International Conference on Future Energy Systems.

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(2020). Data analytics in the electricity sector -- A quantitative and qualitative literature review. Energy and AI.

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(2019). Industrial Demand-Side Flexibility: A Benchmark Data Set. Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy ‘19.

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(2019). Benchmark Dataset for textquotedblIndustrial Demand-Side Flexibility: A Benchmark Data Set textquotedbl.

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(2019). A Method for Sizing Centralised Energy Storage Systems Using Standard Patterns. 2019 IEEE Milan PowerTech.

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(2018). SCiBER. Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy ‘18.

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(2018). How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes. Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy ‘18.

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(2018). Concept and benchmark results for Big Data energy forecasting based on Apache Spark. Journal of Big Data.

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(2018). Auction Design to Use Flexibility Potentials in the Energy - Intensive Industry. 2018 15th International Conference on the European Energy Market (EEM).

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(2018). Assessment of Unsupervised Standard Pattern Recognition Methods for Industrial Energy Time Series. Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy ‘18.

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(2018). A comprehensive modelling framework for demand side flexibility in smart grids. Computer Science - Research and Development.

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(2017). Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0. Proceedings of the Genetic and Evolutionary Computation Conference Companion.

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(2017). Mining Flexibility Patterns in Energy Time Series from Industrial Processes. Proceedings 27. Workshop Computational Intelligence.

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(2015). Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests. Journal of Decision Systems.

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