Abstract
The continuous increase in energy costs and the volatility of energy prices are enforcing the implementation of energy efficiency measures (EEM) in companies. The choice of EEM in most cases is based on Pay-Back (PB) criteria, and in several cases on NPV and IRR criteria. In all these cases, it is necessary to estimate the price of energy in the following years so as to be able to study the profitability of the proposed EEM. Energy prices: electricity, biomass, petroleum, natural gas… change greatly throughout the period of a project, and their values are not easy to predict. If probabilistic functions are used to define the evolution of energy prices, in the period of the project, the economic parameters (PB, IRR, NPV) could also be obtained as probabilistic functions, by applying Monte Carlo Simulation Methods. This paper shows how to obtain the probabilistic functions that best describe the variation of energy prices in the period of a project, and how to apply the Monte Carlo Simulation Method to obtain a better approach to predicting future energy prices.
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Acknowledgements
This work is partially supported by FEDER funds, the DGICYT and Junta de Andalucía under projects TIN2011-27696-C02-01, TIN2014-55024-P and P11-TIC-8001, respectively.
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Grid, A.J.P., Ortuño, A., García-Cascales, M.S., Sánchez-Lozano, J.M. (2016). Predictive Probabilistic Functions for Energy Prices as an Input in Monte Carlo Simulations. In: Ayuso Muñoz, J., Yagüe Blanco, J., Capuz-Rizo, S. (eds) Project Management and Engineering Research, 2014. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-26459-2_18
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