First Advisor

Charles Weber

Term of Graduation

Fall 2025

Date of Publication

12-5-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Technology Management

Department

Engineering and Technology Management

Language

English

Subjects

Alternative Energy, Decision Making

Physical Description

1 online resource (xviii, 356 pages)

Abstract

Renewable Heat Energy projects exhibit numerous unique, complex, and often conflicting characteristics, including varying project sizes, diverse energy resources, stakeholder demands, and significant challenges in energy conversion applications. Traditionally, cash flow analysis and return on investment are the most widely recognized metrics for evaluating remote energy projects. However, numerous quantitative and qualitative factors influence the overall performance and success of Renewable Heat Energy System (RHES) projects. A comprehensive approach using advanced tools is essential for assessing the factors and criteria that significantly impact these projects.

This research investigates the key quantitative and qualitative factors that contribute to the successful implementation of renewable heat energy projects. It also analyzes these factors using a hierarchical decision model (HDM), emphasizing the importance of renewable heat energy resources in RHES projects for utility energy.

A comprehensive literature review was conducted to identify appropriate and effective methodologies for developing a model that identifies, recognizes, and scores complex factors.

Four utilities were selected as the off-take uses for the heat energy resource alternatives. These utilities include electricity generation, heat generation, cooling production, and water purification or desalination. Six perspectives have been identified to evaluate the attributes necessary for successful RHES projects. These perspectives are RESTEP – resource, economic, social/cultural, technical, environmental, and political. Each perspective comprises multiple criteria that are interconnected through complex processes and unique challenges. The alternative heat energy resources assessed at the project location include solar thermal systems, geothermal heat systems, biomass systems, heat pump technologies, and waste heat recovery.

A hierarchical decision model was developed to evaluate Renewable Heat Energy Utility conversion technologies for Hawai’i, rank specific resources, and enhance project implementation decisions. The model was assessed for the significance of pairwise ranking differences and bias evaluation by each category of expert: Academia (universities), Regulated Regional Utilities (Hawaiian Electric), National Laboratories (NREL, PNWL), National Energy Organizations (DOE, BPA, TVA), social media (LinkedIn), Consultants, Regional Government, and Stakeholders. Location experts include project-specific individuals with local knowledge, off-site experts with little or no understanding of site-specific requirements and challenges, and Generative AI experts.

My research shows that model results can vary significantly depending on local knowledge and expertise. Typical experts have insights and biases related to the subject, shaped by their prior work, research, and experience. However, they may lack a complete understanding of local challenges or constraints when making decisions in that context.

A model was created that uses two expert panels to assist with its development, validation, and measurement. It also checks whether experts from each panel evaluate the pairwise comparisons consistently or if the panels produce different results for the decision-making model. The two expert panels include:

1. Expert Panel 1, Experts with extensive knowledge of the subject relevant to the decision, along with a specific understanding of local social norms, cultural customs, location-specific laws and ordinances, and politically driven policies.

2. Expert Panel 2, Experts with extensive knowledge of the decision-making subject who are not situated in the area or lack expertise in the location.

During the research, a lack of qualified experts with local knowledge was identified. A pilot study was initiated that uses Perplexity Pro, a Generative AI, to mimic Expert Panel 1. The prompt for the study Panel 3 utilizes specific data sources, such as peer-reviewed articles, government publications, and scientific databases. It offers pairwise comparisons to support decision-making and model results as a comparison to Experts with local knowledge and expertise.

A second Panel 4 also uses Generative AI, but was instructed to use all sources to mimic Expert Panel 2, and provide pairwise comparisons for decision-making and modeling outcomes.

The HDM model, through pairwise comparisons among the first two expert panels, reveals significant variance in the rankings of perspectives and criteria. This variance is based on the model's quantification, which employs subject matter experts with and without local or location-specific knowledge, alongside those who possess such expertise and understand specific challenges. The model's results are substantial, leading to different rankings of alternatives and utilities.

The third and fourth panels, Generative AI, provide insights derived from digital sources. They were not used in the results.

The decision-making rankings from the third panel yielded results similar to those of Expert Panel 1. They incorporate local knowledge that can significantly enhance models with broader perspectives, criteria, and complexities, thereby providing substantially improved decision-making. Meanwhile, the fourth panel, tasked with evaluating open-source data, produced decision-making rankings akin to those of Expert Panel 2 but without incorporating local knowledge.

New knowledge from this research includes an innovative decision-making model called RESTEP, which is introduced. This model provides a resource perspective, along with related criteria, to enhance decision-making frameworks. In this context, the RESTEP model was employed to assess heat energy resources for primary utilities. Throughout the development of the RESTEP model, it became clear that a comprehensive understanding of location perspectives and their underlying criteria is crucial for effective decision-making.

Furthermore, by conducting further research on developing and utilizing enhanced Generative AI and Digital Technologies as tools for model development, validation, quantification, and interpretation, decision-makers could significantly improve their decision-making models.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

https://archives.pdx.edu/ds/psu/44353

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