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Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data

Author

Listed:
  • Fabrizio De Caro

    (Department of Engineering, University of Sannio, 82100 Benevento, Italy)

  • Alfredo Vaccaro

    (Department of Engineering, University of Sannio, 82100 Benevento, Italy)

  • Domenico Villacci

    (Department of Engineering, University of Sannio, 82100 Benevento, Italy)

Abstract

The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance power system operation. To address these two conflicting objectives, this paper advocates the role of knowledge discovery from big-data, by proposing the integration of adaptive Case Based Reasoning models, and cardinality reduction techniques based on Partial Least Squares Regression, and Principal Component Analysis. The main idea is to learn from a large database of historical climatic observations, how to solve the windforecasting problem, avoiding complex and time-consuming computations. To assess the benefits derived by the application of the proposed methodology in complex application scenarios, the experimental results obtained in a real case study will be presented and discussed.

Suggested Citation

  • Fabrizio De Caro & Alfredo Vaccaro & Domenico Villacci, 2017. "Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data," Energies, MDPI, vol. 10(2), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:252-:d:90909
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    References listed on IDEAS

    as
    1. Skittides, Christina & Früh, Wolf-Gerrit, 2014. "Wind forecasting using Principal Component Analysis," Renewable Energy, Elsevier, vol. 69(C), pages 365-374.
    2. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    3. Qunli Wu & Chenyang Peng, 2016. "Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm," Energies, MDPI, vol. 9(4), pages 1-19, April.
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    Cited by:

    1. Fabrizio De Caro & Amedeo Andreotti & Rodolfo Araneo & Massimo Panella & Antonello Rosato & Alfredo Vaccaro & Domenico Villacci, 2020. "A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data," Energies, MDPI, vol. 13(24), pages 1-25, December.

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