Modeling The Efficiency Of Implementation Of Energy Saving Policy At Enterprises Under Uncertainty Conditions
- DOI
- 10.2991/smtesm-19.2019.3How to use a DOI?
- Keywords
- energy saving, efficiency, uncertainty, hybrid neural networks, neuro-fuzzy neural network
- Abstract
In the article a mathematical model of the intellectual support of decision making for the evaluation of the efficiency of implementation and operation of energy saving policy of the enterprise under uncertainty conditions with the help of Mat Lab’s mathematical package has been developed. The main factors influencing the decision making process regarding the feasibility of implementing energy saving policy at the enterprise have been determined. The aim of the work is to develop expert-modeling system of decision making support for evaluation of the efficiency of energy saving policy under conditions of uncertainty and risk. With the help of the developed system, there has been made the evaluation of the efficiency of the implementation of energy saving policy at Ukrainian enterprises. It has been revealed that the most important factors determining the efficiency of implementation and realization of energy saving policy at the enterprises belonging to the same industry are internal factors and the cost of external sources of financing.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Mykhaylo Voynarenko AU - Viacheslav Dzhedzhula AU - Iryna Yepifanova PY - 2019/09 DA - 2019/09 TI - Modeling The Efficiency Of Implementation Of Energy Saving Policy At Enterprises Under Uncertainty Conditions BT - Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019) PB - Atlantis Press SP - 10 EP - 14 SN - 2352-5428 UR - https://doi.org/10.2991/smtesm-19.2019.3 DO - 10.2991/smtesm-19.2019.3 ID - Voynarenko2019/09 ER -