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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Syaad Patmanthara
Department of Electrical Engineering and Informatics, State University of Malang, 65145, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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A Conceptual Framework for Human AI Collaboration: Ontological and Epistemological Perspectives Meyti Eka Apriyani; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.251

Abstract

Collaboration between humans and artificial intelligence (AI) has become a pivotal phenomenon in the evolution of information systems, yet its philosophical foundations remain underexplored. This study develops an integrative conceptual framework that combines ontological and epistemological perspectives to examine how human–AI collaboration shapes knowledge creation and decision-making within sociotechnical contexts. The proposed framework identifies five ontological levels of AI agency and four epistemological processes underlying hybrid knowledge formation. It further integrates six interrelated dimensions—ontological, epistemological, technical, ethical, social, and organizational—that collectively define the dynamics of human–AI collaboration. The findings contribute to the theoretical discourse by introducing the constructs of quasi-epistemic entities and hybrid epistemology, which reconceptualize AI not merely as a computational artifact but as a participant in epistemic processes, thereby extending existing theories of distributed cognition and epistemic accountability beyond instrumental human–machine models. Practically, the framework informs the design of transparent, adaptive, and ethically aligned human–AI systems within information-intensive environments.
From THD to Causality: Epistemology of Artificial Intelligence-Based Harmonic Analysis in Hybrid Microgrids Ana Nuril Achadiyah; Arif Nur Afandi; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.252

Abstract

The increasing penetration of PV, wind turbines, battery storage (BESS), and electric vehicle charging stations (EVCS) in hybrid microgrids complicates the harmonic landscape. Common practices rely on FFT-based measurements and THD/TDD indices, but source attribution and causality assignment are often uncertain. We map how epistemological positions shape how we measure, explain, and justify technical claims about harmonics. We then propose an Epistemically-Informed Harmonic AI (EPI-HAI) framework that combines standardized measurements (IEC/IEEE), physics-constrained AI modeling (KCL/KVL, impedance), XAI (SHAP/Grad-CAM), and uncertainty management to strengthen epistemic trust. A vignette of a PV–BESS–EVCS microgrid demonstrates that triangulation of evidence (n-order patterns, operating logs, line impedance) is more valid than mere spectral correlation. The three main contributions of this article are, the compilation of a map of the relationship between epistemology and methodology in harmonic analysis, the formulation of transparent and accountable physics-based artificial intelligence (AI) design principles and a discussion of pedagogical implications that can be applied in the development of power engineering curricula.
Co-Authors Ana Nuril Achadiyah Arif Nur Afandi Meyti Eka Apriyani