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: 12Citation - Scopus: 12The Role of Energy Efficiency, Renewable Resources, Green Innovation, and Fiscal Decentralization in Sustainable Development: Evidence From OECD Countries(Elsevier Sci Ltd, 2025-08) Binsaeed, Rima H.; Khan, Zeeshan; Dogan, Eyup; Rahim, SyedEnergy efficiency and renewable resources for sustainable development are novel discussion areas for academics and researchers. Similarly, most developed and emerging countries are experiencing fiscal decentralization to enhance regional development. However, the importance of these sectors in sustainable development is still unclear in the literature. This research investigates the influence of energy efficiency, renewable energy, green innovation, and fiscal decentralization on sustainable development. Using the data for 18 fiscally decentralized OECD countries from 1995 to 2020, the roles of linear and nonlinear green innovation and renewable energy are also considered. This study uses novel moment quantile regression and finds that revenue decentralization, expenditure decentralization, and fiscal decentralization are significant drivers of sustainable development. Additionally, energy efficiency and value-added manufacturing significantly enhance sustainability in the region. However, green innovation and renewables are resources that exhibit a U-shaped association with sustainable development. The robustness of these results is validated via a series of parametric and nonparametric approaches. From the policy perspective, this research suggests improved research and development on renewable energy, green innovation, and energy efficiency could significantly encourage the OECD's journey towards sustainable development. Additionally, subnational governments should be given more fiscal autonomy, which may encourage regional level investments and boost the confidence of clean energy producing sectors to accelerate sustainable regional development.Conference Object Citation - WoS: 7Citation - Scopus: 10PI-Controlled ANN-Based Energy Consumption Forecasting for Smart Grids(SciTePress, 2015) Gezer, Gülsüm; Tuna, Gürkan; Κogias, DImitrios G.; Gülez, Kayhan; Güngör, Vehbi Çağrı; Kogias, DimitrisAlthough Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 5Enerji Hasadı ve Sıkıştırmalı Algılama Yapan Gizlilik Odaklı Sualtı Kablosuz Ağlarında Ömür Analizi(Institute of Electrical and Electronics Engineers Inc., 2019-04) Uyan, Osman Gokhan; Güngör, Vehbi ÇağrıUnderwater sensor networks (UWSN) are a division of classical wireless sensor networks (WSN), which are designed to accomplish both military and civil operations, such as invasion detection and underwater life monitoring. Underwater sensor nodes operate using the energy provided by integrated limited batteries, and it is a serious challenge to replace the battery under the water especially in harsh conditions with a high number of sensor nodes. Here, energy efficiency confronts as a very important issue. Besides energy efficiency, data privacy is another essential topic since UWSN typically generate delicate sensing data. UWSN can be vulnerable to silent positioning and listening, which is injecting similar adversary nodes into close locations to the network to sniff transmitted data. In this paper, we discuss the usage of compressive sensing (CS) and energy harvesting (EH) to improve the lifetime of the network whilst we suggest a novel encryption decision method to maintain privacy of UWSN. We also deploy a Mixed Integer Programming (MIP) model to optimize the encryption decision cases which leads to an improved network lifetime. © 2020 Elsevier B.V., All rights reserved.