Savaşcı, Alper

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Profile URL
https://hdl.handle.net/20.500.12573/2638
Name Variants
Alper Savaşçı
Alper, Savasci
Savasci, Alper
Job Title
Öğr. Gör.
Email Address
alper.savasci@agu.edu.tr
Main Affiliation
02.05. Elektrik & Elektronik Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0001-7870-9712
Scopus Author ID
Turkish CoHE Profile ID
45419
Google Scholar ID
4hZwJ4YAAAAJ&hl
WoS Researcher ID
ADR-3958-2022

Sustainable Development Goals

13

CLIMATE ACTION
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17

PARTNERSHIPS FOR THE GOALS
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1

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8

DECENT WORK AND ECONOMIC GROWTH
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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11

SUSTAINABLE CITIES AND COMMUNITIES
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1

NO POVERTY
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6

CLEAN WATER AND SANITATION
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10

REDUCED INEQUALITIES
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14

LIFE BELOW WATER
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15

LIFE ON LAND
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GENDER EQUALITY
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QUALITY EDUCATION
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AFFORDABLE AND CLEAN ENERGY
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GOOD HEALTH AND WELL-BEING
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ZERO HUNGER
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This researcher does not have a Scopus ID.
Documents

17

Citations

113

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Scholarly Output

6

Articles

1

Views / Downloads

9/0

Supervised MSc Theses

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WoS Citation Count

1

Scopus Citation Count

4

WoS h-index

1

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2

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WoS Citations per Publication

0.17

Scopus Citations per Publication

0.67

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0

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JournalCount
-- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- Ankara -- 2045621
59th International Universities Power Engineering Conference -- SEP 02-06, 2024 -- Cardiff, ENGLAND1
IEEE Power and Energy Society General Meeting -- 2024 IEEE Power and Energy Society General Meeting, PESGM 2024 -- Seattle; WA -- 2031301
International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification -- SEP 10-12, 2024 -- Torino, ITALY1
Sustainable Energy Grids & Networks1
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Now showing 1 - 6 of 6
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Optimal Dead Band Control of Occupant Thermostats for Grid-Interactive Homes
    (IEEE, 2024) Savasci, Alper; Ceylan, Oguzhan; Paudyal, Sumit
    Efficient and grid-aware management of home-scale heating, ventilation, and air conditioning (HVAC) systems is one of the key enablers of demand-side management (DSM) and associated grid services in the residential sector. HVACs regulate the indoor temperature around a set point through a thermostat operating within a closed-loop control scheme. Conventional thermostats typically have a built-in temperature dead band or differential where the thermostat is idle, and HVAC stays at the most recent state (On/Off). The temperature dead band is an important control parameter that can help save energy as well as preventing frequent On/Off switching cycles leading to excessive wear and tear on the equipment. However, strategic and dynamic adjustment of the dead band can be a challenging task for an occupant. This paper proposes a mixed-integer linear program (MILP)-based tuning scheme to optimally determine the dead band. The novelty in this formulation is the inclusion of thermostat hysteresis curve modeled by piecewise techniques for tuning the dead band accurately. The proposed formulation is solved as a receding horizon manner for normal as well as under a demand response (DR) event and has been found it can achieve up to 10% reduction in energy consumption without degrading the regulation performance significantly.
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    Conference Object
    Enhancing Intrusion Detection in Electric Networks Using Physics-Informed Random Forest
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bozdal, Mehmet; Savasci, Alper
    The increasing complexity of electric power networks has heightened their vulnerability to cyber-attacks, challenging traditional Intrusion Detection Systems (IDS) that rely on manually crafted rules. This paper introduces a novel approach that integrates physics-informed features and feature selection into a Random Forest (RF) model to enhance IDS performance. By deriving features such as complex power and impedance from fundamental electrical principles and applying SelectKBest for optimal feature selection, our method not only improves detection accuracy but also enhances efficiency by using fewer than half the features. Specifically, the feature-enriched RF model utilizing 55 features achieves an accuracy of 0.9667 and an F1-score of 0.9664, compared to 0.9576 and 0.9570 for the baseline RF model. This approach demonstrates the effectiveness of advanced feature engineering and selection techniques for improving the security and reliability of power network monitoring systems. © 2024 Elsevier B.V., All rights reserved.
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    Conference Object
    Data-Driven Local Control Design for Dead Band Control of Load Tap Changers
    (IEEE, 2024) Savasci, Alper; Ceylan, Oguzhan; Paudyal, Sumit
    This study presents an off-line optimization-guided machine learning approach for coordinating the local control rules of on-load tap changers (OLTCs) and step-voltage regulators (SVRs). Based on a bang-bang control rule, these legacy devices autonomously regulate the feeder voltage around the nominal level by varying the tap position in the lower or raise direction. The characterizing parameter of the local control rule is the dead band, which affects the number of tap switching in operation and is directly related to the economical use life of the equipment. The bandwidth is typically set within a standard voltage range and is generally kept constant in daily operation. However, adjusting the bandwidth dynamically can prevent excessive tap switching while maintaining satisfactory voltage regulation for varying loading and distributed generation conditions. Our approach aims to set the bandwidth parameter systematically and efficiently through a machine learning-based scheme, which is trained with a dataset formed by solving the distribution network optimal power flow (DOPF) problem. The performance of learning the bandwidth parameter is demonstrated on the modified 33-node feeder, which is promising for integrated voltage control schemes.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Data-Driven Methods for Optimal Setting of Legacy Control Devices in Distribution Grids
    (IEEE Computer Society, 2024) Savasci, Alper; Ceylan, Oǧuzhan; Paudyal, Sumit
    This study presents machine learning-based dispatch strategies for legacy voltage regulation devices, i.e., onload tap changers (OLTCs), step-voltage regulators (SVRs), and switched-capacitors (SCs) in modern distribution networks. The proposed approach utilizes k-nearest neighbor (KNN), random forest (RF), and neural networks (NN) to map nodal net active and reactive injections to the optimal legacy controls and resulting voltage magnitudes. To implement these strategies, first, an efficient optimal power flow (OPF) is formulated as a mixed-integer linear program that obtains optimal decisions of tap positions for OLTCs, SVRs, and on/off status of SCs. Then, training and testing datasets are generated by solving the OPF model for daily horizons with 1-hr resolution for varying loading and photovoltaic (PV) generation profile. Case studies on the 33-node feeder demonstrate high-accuracy mapping between the input feature and the output vector, which is promising for integrated Volt/VAr control schemes. © 2024 Elsevier B.V., All rights reserved.
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    Book Part
    Hosting Capacity Calculation Methods
    (Elsevier, 2025) Oguzhan, Ceylan; Alper, Savasci
    In this chapter, we focus on hosting capacity (HC) calculations, by giving the methods to determine the maximum amount of distributed energy resources (DER) that can be integrated into power distribution network(s) without compromising reliability or performance. We detail methodologies such as power flow-based approaches, probabilistic techniques, and machine learning algorithms, with sample applications of HC calculations. Initially, we focus on power flow-based methods based on simulating power distribution network(s) to assess system voltage, current flow, and stability impacts from DER installations. Then, we will give the probabilistic approaches that use uncertainties in renewable generation and consumer demand, based on statistical techniques and Monte Carlo simulations aiming to reflect these variability. Machine learning (ML) techniques will also be given based on analyzing large data sets, detecting patterns, and predicting system responses. These kinds of methods include regression analysis and neural networks trained on historical data for optimized HC predictions. It should be stated that HC is impacted by several factors, such as network topology, load profiles, and DER characteristics, and these as well will be discussed. We will provide a practical example of an HC calculation on a 141-node distribution network using a step-by-step algorithm in Matpower, with simulation results based on an iterative deterministic method. Then, we will give the broader implications of HC assessments for grid modernization and energy policy, highlighting how accurate calculations support a more decentralized, sustainable, and resilient energy future. © 2025 Elsevier B.V., All rights reserved.
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    Article
    Supervised Learning-Driven Dead Band Control of Occupant Thermostats for Energy-Efficient Residential HVAC
    (Elsevier, 2026) Savasci, Alper; Ceylan, Oguzhan; Paudyal, Sumit
    Heating, 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 %.