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Systematic Review

Terminological Structures in Anticipatory Energy Research: A Conceptual and Bibliometric Analysis of Energy Scanning and Forecasting Approaches

Milad Pirhadi ORCID ,  Farzad Ghafoorian ORCID ,  Parham Karimi ORCID ,  Sara Mahmoodian Yonesi ORCID ,  Majid Zandi ORCID
    Received: 18 May 2026; Revised: 31 August 2026; Accepted: 8 September 2026; Published: 29 September 2026

    Abstract

    Anticipatory approaches are increasingly vital for analyzing energy systems; however, the terminology surrounding energy monitoring, scanning, forecasting, and foresight remains fragmented and inconsistently applied. To resolve this conceptual ambiguity, this study undertakes a systematic conceptual review and bibliometric analysis of contemporary literature to synthesize and reorganize the terminological framework within anticipatory energy research. We propose an integrated, four-layer architecture that delineates these functions: Layer 1 (Energy Monitoring) establishes real-time operational baselines using empirical data streams; Layer 2 (Energy Scanning) systematically tracks external structural discontinuities and emerging weak signals outside historical datasets; Layer 3 (Energy Forecasting) employs quantitative, data-driven methodologies—including machine learning (ML) and deep learning—for short- to medium-term operational optimization; and Layer 4 (Energy Foresight) integrates these quantitative outputs with qualitative approaches, such as scenario planning and backcasting, to navigate deep uncertainty in long-term energy transitions. A primary finding of this review is the critical positioning of ML techniques. While highly effective for operational forecasting based on historical data, these data-driven models are intrinsically limited in addressing the structural policy shifts and non-linear transformations required for strategic foresight. Furthermore, the bibliometric analysis identifies a distinct conceptual divergence between data-intensive predictive studies and policy-focused long-term planning. By explicitly situating ML within the forecasting domain and structurally bridging short-term operational models with long-term strategic governance, this study mitigates the risks of conceptual conflation. The proposed framework enhances methodological coherence and provides policymakers with a theoretically grounded taxonomy to ensure that anticipatory energy strategies remain both visionary and empirically substantiated.

    Keywords

    References

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