Predictive-Adaptive Coordinated Optimization for Multi-Greenhouse Energy Management

Document Type : Original Article

Authors

Department of Electrical Engineering, Hamedan University of Technology, Hamedan, Iran

Abstract
Rising food demand and the push for decarbonization have made controlled-environment agriculture essential. However, its energy intensity, especially in arid regions, hinders sustainability. This paper introduces the predictive-adaptive coordinated optimization (PACO) framework, a novel control architecture for demand-side energy management in networks of grid-interfaced greenhouses. Departing from population-based metaheuristics, PACO is rigorously grounded in adaptive control theory, recursive online estimation, and distributed optimization. Each greenhouse agent employs an adaptive autoregressive exogenous model with recursive least-squares parameter updating, enabling continuous tracking of seasonal variations, equipment degradation, and sensor drift. A novel correction mechanism, with gains dynamically scheduled based on prediction error and battery state-of-charge, provides robust compensation for forecast mismatches and unforeseen disturbances. An attention-based coordination unit computes lightweight scalar signals that promote global demand flattening without imposing centralized optimization during execution. The framework explicitly enforces physical constraints through penalty-augmented objectives and entropy regularization. Extended simulations, including 30-day horizon, multi-seasonal assessments, and ablation studies on a 10-greenhouse network in a semi-arid climate, demonstrate that PACO reduces total grid import by 29.8%, shaves peak demand by 31.1%, cuts operational costs by 32.9%, and lowers CO₂ emissions by 29.7% compared to benchmark methods, while confirming the synergistic contributions of its three core components. PACO offers a transparent, scalable, and computationally efficient solution for coordinated multi-greenhouses, bridging the gap between physical modeling and online adaptation.

Keywords


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Articles in Press, Accepted Manuscript
Available Online from 30 September 2026

  • Receive Date 02 June 2026
  • Revise Date 01 July 2026
  • Accept Date 12 August 2026
  • First Publish Date 13 August 2026
  • Publish Date 30 September 2026