Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO2 emissions and the achieved savings.
CITATION STYLE
Borunda, M., Garduno, R., Nicholson, A. E., & de la Cruz, J. (2019). Assessment of Small-Scale Wind Turbines to Meet High-Energy Demand in Mexico with Bayesian Decision Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11835 LNAI, pp. 493–506). Springer. https://doi.org/10.1007/978-3-030-33749-0_40
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