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The assessment of a theory is the main objective of scientists. Theories are always introduced by models, and model selection is applied to many various fields of scientific studies in order to corroborate or verify the theory as the winning one among a set of competing hypotheses. Different criteria are taken as bases to select one model among several parallel models in both statistical and visual types. This paper proposes a new method in model selection based on the solutions of a fuzzy decision-making problem. The method enables us to apply systematically all desired validation criteria by defining a proper possibility distribution function (PDF) for each criterion. The generality of the method allows us to consider even intuitive, inaccurate, or linguistic criteria. Finally, the maximization of a utility function, rationally composed of the PDFs, will determine the best choice of competing models. The method is illustrated by two sets of linear and nonlinear parallel models. Index Terms--Fuzzy decision making (FDM), model selection, model validation, multicriteria model selection.

Iran has enormous energy resources and owns the second largest oil and gas reservoirs in the world. Cheap domestic energy prices and energy subsidies had led to a rapid increase in domestic energy consumption in the country. In 2010, increasing domestic energy prices and targeted subsidies reform were implemented. The objective of this article is to allocate optimally to each end-user a certain amount of energy to be supplied by a given resource in Iran regarding targeted subsidies reform. For this purpose, energy demands of end-users are forecasted and the outcomes are used in a fuzzy linear programing model, which determines the best allocation of energy resources of Iran from 2012 to 2020. The results would provide a scientific basis for the optimal allocation of energy resources in meeting the future energy demand in Iran. [ABSTRACT FROM AUTHOR]

Energy plays a key role in sustainable development. Formulation of an energy model will help in the proper allocation of available energy resources. The objective of this article is to allocate optimally to each end user a certain amount of energy to be supplied by a given resource in Iran to reduce greenhouse gases. For this purpose, energy demands of end users are forecasted, and the outcomes are used in a fuzzy linear programming model, which determines the best allocation of energy resources in Iran from 2012 to 2020. The result shows a large portion of crude oil and natural gas will be allocated for exports. Transportation sector will be the major consumer of the petroleum products. Higher amounts of natural gas compared with the petroleum products will be allocated to power plants. Moreover, a considerable portion of natural gas should be injected into the oil reservoirs. These analyses would provide scientific basis for the optimal allocation of energy resources in meeting the future energy demand in Iran. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 854-859, 2013 [ABSTRACT FROM AUTHOR]

This paper studies the impact of climate change on the electricity consumption by means of a fuzzy regression approach. The climate factors which have been considered in this paper are humidity and temperature, whereas the simultaneous effect of these two climate factors is considered. The impacts of other climate variables, like the wind, with a minor effect on energy consumption are ignored. The innovation which applies in this paper is the division of the year into two parts by using the temperature-day graph in the year. To index the humidity, data of the minimum humidity per day are used. For temperature, the maximum temperature of the first part of the year (warm days) and the minimum of the second part (cold days) are used. The indicator for the consumption is the daily peak load. The model results show high sensitivity to the temperature but low sensitivity to the humidity. Moreover, it is concluded that the model structure cannot be the same and for the cold par additional variables such as gas consumption should be considered. [ABSTRACT FROM AUTHOR]

The necessity of dynamic equivalents for power system analysis has been well known since the expansion of large interconnected power networks, and has been discussed during the last decades. The present paper proposes a new method for constructing dynamic equivalents of power systems. In this method, at the first step the "study system" is modeled completely via a single machineinfinite bus modeling procedure. Then the "external system" is identified as a MIMO feedback block of this model in such a manner that can include dynamic effects of the latter on the behavior of the former. The method is successfully experimented on a part of the Iranian southern network. [ABSTRACT FROM AUTHOR]

As far as the electricity market is considered, both governments and private sector are interested in energy supply industry and so consider it essential to predict energy demand. Solution of the problem depends on availability of a model. This paper proposes a systematically developed model. It is based on a previously performed exogeneity investigation of various quantified variables. A certain nonlinear model among a collection of 100 models with different inputs is chosen as the most appropriate model. Structure of all nonlinear competing models is established according to logical conjunctive and disjunctive relationships between variables. Different combinations of the exogenous variables generate these models. At first, unacceptable models are put away applying coefficient sign significance criterion and error validity. An automated fuzzy decision-making process determines the winner model, which is a hybrid nonlinear model, among the other remaining models. [ABSTRACT FROM AUTHOR]