Interactive Recommendation of Sustainable Energy Management Strategies through Diversified Rule Mining and Preference-Aware Embeddings
Keywords:
Sustainable energy management, interactive recommendation, diversified rule mining, preference-aware embeddings, socio-technical systems, fairnessAbstract
The transformation of energy infrastructures toward sustainability demands decision-support systems that can translate vast operational data into actionable strategies while accommodating diverse stakeholder preferences and dynamic grid conditions. This paper presents a system-level investigation into an interactive recommendation framework that integrates diversified rule mining with preference-aware embeddings to generate personalized energy management strategies. We examine the architecture, trade-offs, and governance dimensions of coupling interpretable rule discovery with latent preference representations in large-scale socio-technical energy systems. The discussion foregrounds the structural tension between diversity of recommendations and personalization accuracy, the computational and infrastructural requirements for real-time interactivity, and the need for robustness under concept drift and data heterogeneity. We analyze how diversified top-k rule mining, guided by user embeddings, can surface a broad set of actionable patterns from smart meter, weather, and market data, while embedding-based user models enable the system to refine suggestions through iterative feedback. The paper further addresses fairness implications across consumer segments, the role of transparency and auditability in algorithmic governance, and the alignment of such systems with energy policy objectives such as demand response, renewable integration, and energy equity. By framing recommendation as a continuous interactive dialogue rather than a static optimization, we highlight pathways for responsible deployment of AI in critical energy infrastructures. We conclude with reflections on future research directions that bridge adaptive rule learning, human-in-the-loop design, and sustainable energy transitions.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
