Location and investment factors of hydropower plants

Abstract

ABSTRACT Location and investment selection features lakes of hydropower plant investments are presented in a proposed expert, event, and data-driven fact-based robot and platform. The traditional magical number 4 scale is tested in simple natural language process clustering at sub-factors, factors, main factors, and metafactors. The traditional Decision-Making Trial and Evaluation Laboratory methodology is tested in complex mathematical natural language process clustering at top metafactors as Political, Economic, Social, and Technological with its extensions. Nine presumptive logarithmic learning curve functions as experience curves are modeled considering time-domain effects. The functions with 0,1; 0,5; and 0,9 learning rates are tested in the current models. They cover almost the whole possibility space. Weighted arithmetic mean and weighted geometric mean as group decision-making approximation methods are tested for decision aggregation on the time domain. In the end, six clustering configurations are compared with each other at the top metafactors level. Six top metafactors, 12 metafactors, 27 main factors, 114 factors, and 10,612 sub-factors are found and clustered in location selection features. There are 11 hierarchical levels. Two top metafactors, five metafactors, 42 main factors, 136 factors, and 4276 sub-factors are found and clustered in investment selection features. There are 10 hierarchical levels.