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

Fabrizio De Caro, Alfredo Vaccaro and Domenico Villacci
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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

Energies, 2017, vol. 10, issue 2, 1-14

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.

Keywords: wind power forecasting; knowledge discovery; big data; case-based reasoning; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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