WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Supervised Learning-Driven Dead Band Control of Occupant Thermostats for Energy-Efficient Residential HVAC(Elsevier, 2026-03) Savasci, Alper; Ceylan, Oguzhan; Paudyal, SumitHeating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band-a range around the setpoint where no action is taken-to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to gen erate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (R2) of 95.75 %.Article Citation - WoS: 7Citation - Scopus: 11Robust Estimator-Based Optimal Control Designs for U-Tube Steam Generators(Sage Publications Ltd, 2014-05-19) Ablay, Gunyaz; Hamamci, SerdarU-tube steam generator level control systems are used to maintain the water level within prescribed narrow limits and to provide constant supply of high-quality steam during power demand variations. Traditional level control systems are often found to be unsatisfactory during low power operations and start-up conditions. Robust non-linear estimator-based optimal control systems are proposed for steam generator level control systems to solve the water level tracking problem during power (or steam) demand variations. It is shown that the proposed control strategies provide optimal and robust water level tracking with a single controller over the complete range of power operation with model and parameter uncertainties and noisy measurements.