Abstract—The aim of this study is to analyze how the growth
of renewable energy in the power market is affecting workers
health and what are the cost implications of having a healthier
workforce. To tackle this issue, Big Data from occupational
health surveillance carried out to over 4,000 workers in Spanish
companies is used to unveil hidden patterns and relevant factors
affecting workers health. Machine learning is used to create a
predictive Bayesian model in order to seek out relevant patterns
that allow to design more effective prevention plans. The results
obtained shed light on the positive impact that an increasing
renewable generation of electricity can produce to workers
health in the electric industry. Skin problems are the main
pathology identified, where nervous system diseases are found
to be reduced for renewable generation workers.
Index Terms—Big data, renewable generation, machine
learning, bayesian networks, occupational health.
The authors are with the Department of Natural Resources and
Environmental Engineering, School of Mining and Energy Engineering,
University of Vigo, Lagoas Marcosende, 36310 Vigo, Spain (e-mail:
sakis@uvigo.es, alabad@uvigo.es, egiraldez@uvigo.es,
jtaboada@uvigo.es).
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Cite:Saki Gerassis, Alberto Abad, Eduardo Giráldez, and Javier Taboada, "The Impact of Renewable Energy for Occupational Health in the Smart Grid Era," Journal of Clean Energy Technologies vol. 6, no. 6, pp. 405-410, 2018.
