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Spatial autocorrelation and entropy for renewable energy forecasting

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Abstract

In renewable energy forecasting, data are typically collected by geographically distributed sensor networks, which poses several issues. (i) Data represent physical properties that are subject to concept drift, i.e., their characteristics could change over time. To address the concept drift phenomenon, adaptive online learning methods should be considered. (ii) The error distribution is typically non-Gaussian. Therefore, traditional quality performance criteria during training, like the mean-squared error, are less suitable. In the literature, entropy-based criteria have been proposed to deal with this problem. (iii) Spatially-located sensors introduce some form of autocorrelation, that is, values collected by sensors show a correlation strictly due to their relative spatial proximity. Although all these issues have already been investigated in the literature, they have not been investigated in combination. In this paper, we propose a new method which learns artificial neural networks by addressing all these issues. The method performs online adaptive training and enriches the entropy measures with spatial information of the data, in order to take into account spatial autocorrelation. Experimental results on two photovoltaic power production datasets are clearly favorable for entropy-based measures that take into account spatial autocorrelation, also when compared with state-of-the art methods.

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Notes

  1. http://re.jrc.ec.europa.eu/pvgis/.

  2. In our formulation, the neighborhood N(p) includes the considered current plant p, i.e. \(p\in N(p)\).

  3. http://www.nrel.gov/.

  4. http://forecast.io/.

  5. http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php.

  6. https://github.com/sryza/spark-timeseries.

  7. https://spark.apache.org/docs/latest/mllib-guide.html.

  8. http://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgements

The research described in this paper has been funded by the Ministry of Education, Universities and Research (MIUR) through the projects “ComESto - Community Energy Storage: Gestione Aggregata di Sistemi d’Accumulo dell’Energia in Power Cloud” (Grant No. ARS01_01259) and “Vi-POC: Virtual Power Operation Center” (Grant PAC02L1_00269). We also acknowledge the support of the European commission through the projects MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant ICT-2013-612944) and “TOREADOR - TrustwOrthy model-awaRE Analytics Data platform” (Grant 988797). The computational work has been carried out on the resources provided by the projects ReCaS (PONa3_00052) and PRISMA (PON04a2_A). The authors also wish to thank Lynn Rudd for her help in reading the manuscript.

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Authors and Affiliations

  1. Department of Computer Science, University of Bari Aldo Moro, via Orabona 4, 70125, Bari, Italy

    Michelangelo Ceci, Roberto Corizzo & Donato Malerba

  2. Consorzio Interuniversitario Nazionale per l’Informatica (CINI), Rome, Italy

    Michelangelo Ceci, Roberto Corizzo & Donato Malerba

  3. Department of Communication Systems, Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Aleksandra Rashkovska

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  1. Michelangelo Ceci
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  2. Roberto Corizzo
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Correspondence to Michelangelo Ceci or Roberto Corizzo.

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Ceci, M., Corizzo, R., Malerba, D. et al. Spatial autocorrelation and entropy for renewable energy forecasting. Data Min Knowl Disc 33, 698–729 (2019). https://doi.org/10.1007/s10618-018-0605-7

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