Energy-Smart Demand Forecasting for Sustainable Stores
Keywords:
Intelligent Energy-Aware Retail, Adaptive Demand Forecasting, Smart Store Management, Local Intelligence, Cloud Computing, Multi-Horizon Forecasting, Time Series Analysis, Forecast Quality Assessment, Sustainable Retail Operations, Inventory Optimization.Abstract
Growing competitive pressure and recent crises have highlighted the need for retail systems that are efficient, resilient, and sustainable. Intelligent energy-aware retail ecosystems address this need by combining local intelligence, cloud computing, and self-aware operation to unify smart store management within a given space and context. The energy-aware approach balances minimizing operational energy use with capturing opportunities for energy gains. This framework is applied in two directions: adaptive demand forecasting—which integrates heterogeneous data sources, aligns forecasting horizons with the decisions they inform, and evaluates forecast quality—and forecasting-dependent sustainable store operations, with particular attention to operational circularity.
Demand forecasting itself is inherently a multi-horizon, multi-level problem. While statistical and machine learning time series methods are well established, and hybrid models incorporating supplementary forecasting modules have extended them further, adaptivity remains rare in practice. Adaptive Forecasting addresses this gap by promoting data-centric, foresight-driven methods that remain sensitive to model quality. To support this, Demand Level–Control Indicators are introduced to assess performance across groups of forecasting methods, time horizons, and aggregation levels. These indicators satisfy four key desiderata and yield 17 variants, distinguished by: whether predictions or meta-forecasts are used as inputs, the choice of control indicator, the trade-off between statistical rigor and accounting burden, and the behavior of moment-tolerant indicators. In practice, retailers face a dual challenge—maintaining inventory levels within defined Service Levels, and controlling costs for a given Service Level.
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