Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

Alexandru Pîrjan, George Căruțașu and Dana-Mihaela Petroșanu
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Alexandru Pîrjan: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania
George Căruțașu: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania
Dana-Mihaela Petroșanu: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania
Authors registered in the RePEc Author Service: George Căruţaşu ()

Energies, 2018, vol. 11, issue 10, 1-42

Abstract: Accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, with the main purpose of overcoming the low accuracy limitations arising from the wind deflection, caused by the quite complex hilly terrain. A specific aspect of our devised forecasting method consists of incorporating advantages of recurrent long short-term memory (LSTM) neural networks, benefiting from their long-term dependencies, learning capabilities, and the advantages of feed-forward function fitting neural networks (FITNETs) that have the ability to map between a dataset of numeric inputs and a set of numeric targets. Another specific element of our approach consists of improving forecasting accuracy by means of refining the accuracy of the weather data input parameters within the same weather forecast resolution area. The developed method has power plant operators as main beneficiaries, but it can also be successfully applied in order to assess the energy potential of hilly areas with deflected wind, being useful for potential investors who want to build this type of wind farms. The method can be compiled and incorporated in the development of a wide range of customized applications targeting electricity forecasting for small wind farms situated on hilly terrain with deflected wind. The experimental results, the implementation of the developed method in a real production environment, its validation, and the comparison between our proposed method and other ones from the literature, confirm that the developed forecasting method represents an accurate, useful, and viable tool that addresses a gap in the current state of knowledge regarding the necessity for an accurate forecasting method that is able to predict with a high degree of accuracy both the produced and consumed electricity for small wind power plants situated on quite complex hilly terrain with deflected wind.

Keywords: forecasting method; produced and consumed electricity; small wind farms; quite complex hilly terrain; long short-term memory neural networks; feed-forward function fitting neural networks (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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