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Integrative Data Processing and Optimized Machine Learning Architectures for Advanced Electricity and Retail Demand Forecasting in High-Dimensional Environments
Issue Vol. 2 No. 02 (2025): VOLUME 02 ISSUE 02 --- Section Articles
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Abstract
Demand forecasting has long stood at the epistemic core of operations management, energy systems planning, and data-driven economic coordination, yet its theoretical and methodological evolution has become increasingly complex with the emergence of big data, deep learning, and hybrid optimization architectures. Across electricity markets and retail ecosystems, forecasting errors no longer merely represent statistical inefficiencies but propagate systemic distortions across pricing, inventory, energy security, and algorithmic coordination mechanisms. This article develops a theoretically grounded and methodologically integrative research framework for demand forecasting that synthesizes optimized support vector machine learning, deep neural architectures, and data preprocessing pipelines across high-dimensional, nonlinear, and volatile demand environments. Drawing on a cross-sectoral corpus of literature in energy forecasting, retail analytics, machine learning, and algorithmic coordination, the study elaborates a composite modeling philosophy grounded in signal decomposition, feature purification, and optimization-based learning.
A central theoretical anchor is the composite electricity demand forecasting framework proposed by Jiang, Li, Liu, and Gao, which demonstrated that data preprocessing combined with optimized support vector machines yields structurally superior demand representations under nonlinear and noisy conditions (Jiang et al., 2020). This work extends that insight by positioning such composite modeling strategies within a broader epistemological framework that incorporates attention mechanisms, probabilistic deep learning, promotional information encoding, and algorithmic feedback effects. Rather than treating forecasting as a single-model exercise, this study conceptualizes it as a layered epistemic process in which raw data are iteratively transformed into structured predictive knowledge.
Methodologically, the research adopts a conceptual-analytical synthesis approach that integrates the theoretical foundations of time series analysis, statistical learning theory, deep recurrent architectures, and operational decision systems. This approach allows for the interpretation of empirical findings reported across the literature as components of a unified forecasting ontology. The results section articulates how composite learning systems systematically outperform monolithic models by reducing noise sensitivity, capturing inter-temporal dependencies, and enabling adaptive learning in non-stationary environments, as evidenced across electricity demand, retail sales, wind power, and fashion forecasting studies.
The discussion advances a critical theory of algorithmic forecasting, arguing that improved demand prediction is not a neutral technological advancement but a structural force that reshapes coordination, competition, and market behavior. It draws on scholarship concerning algorithmic collusion, big data analytics, and promotional forecasting to demonstrate how predictive systems now function as both analytical instruments and market actors. The article concludes by proposing a future research agenda that situates demand forecasting within a broader socio-technical system of automated decision-making, energy transition, and digital commerce.
Keywords
References
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