Context Of Decision Trees Research Articles

Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees

In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.

Decision Trees with Single and Multiple Interval-Valued Objectives

Important to decision making is recognition of what today's decisions have on future options, and an oft-used tool to aid in this problem is the decision tree. This paper addresses situations in which uncertainty arises in the objectives associated with different sequential decision paths and provides a decision tree for uncertain parameters where only bounds, not distributions, are known. Single- and multiple-objective interval-valued decision trees are introduced. Interval arithmetic is used for the decision tree rollback process. To address the difficulty of an interval-valued comparison of alternatives, several decision rules, as well as a probabilistic approach, are discussed in the decision tree context. The interval-valued decision trees are deployed in a simple maintenance, repair, and overhaul decision-making illustration.