Kadir Amasyali
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View article: A Transfer Learning Approach to Energy-Efficient Control of Small and Medium-Sized Commercial Buildings
A Transfer Learning Approach to Energy-Efficient Control of Small and Medium-Sized Commercial Buildings Open
View article: Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements
Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements Open
View article: A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings
A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings Open
Reinforcement learning (RL) is a powerful tool that has shown promising results in many domains such as robotics and game-playing. Because RL algorithms learn optimal control policies by continuously interacting with their environments, th…
View article: Anomaly detection for MPC forecast in Fleet of Water Heaters
Anomaly detection for MPC forecast in Fleet of Water Heaters Open
Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. Thi…
View article: Peak Reduction Using Mode Adjustment of Heat Pump Water Heaters in a Residential Neighborhood
Peak Reduction Using Mode Adjustment of Heat Pump Water Heaters in a Residential Neighborhood Open
Building electrification is putting pressure on distribution grid worldwide. Peak reduction is an important concern that can help reduce the growing stress and allow to defer investments in new capacity. Water heaters represent a convenien…
View article: Ten questions concerning reinforcement learning for building energy management
Ten questions concerning reinforcement learning for building energy management Open
View article: Reinforcement-learning-based Smart Water Heater Control: An Actual Deployment
Reinforcement-learning-based Smart Water Heater Control: An Actual Deployment Open
Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received inc…
View article: Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings
Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings Open
In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Oper…
View article: Deep Reinforcement Learning Based Smart Water Heater Control for Reducing Electricity Consumption and Carbon Emission
Deep Reinforcement Learning Based Smart Water Heater Control for Reducing Electricity Consumption and Carbon Emission Open
View article: Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control
Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control Open
Deep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very fe…
View article: Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services
Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services Open
Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary servic…
View article: Field Testing of a Hierarchical Model-Free Transactive Control Strategy in a Residential House
Field Testing of a Hierarchical Model-Free Transactive Control Strategy in a Residential House Open
Demand response plays an important role in addressing the uncertain, intermittent, and variable nature of renewable energy sources. However, to be effective, it requires a significant amount of demand load flexibility, in which buildings a…
View article: Robust Solution Approach for Bilevel Demand Response Game at Distribution Level
Robust Solution Approach for Bilevel Demand Response Game at Distribution Level Open
In this paper, a bilevel electricity pricing and demand response game between a distribution system operator (DSO) and load aggregators (LAs) is considered, and a robust decision model is proposed for the DSO to deal with the uncertainties…
View article: Impacts of New Sensor Types for Selected Advanced Controls
Impacts of New Sensor Types for Selected Advanced Controls Open
Sensors are critical components for controls in buildings. They collect desired information to input into controls for the completion of subsequent control actions. When sensors work in unhealthy or faulty conditions, the benefits of the c…
View article: Hierarchical Model-Free Transactive Control of Residential Building Loads: An Actual Deployment
Hierarchical Model-Free Transactive Control of Residential Building Loads: An Actual Deployment Open
The transformation of electricity systems into more sustainable configurations brought some new challenges. The uncertain, intermittent, and variable nature of renewable energy sources require a significant amount of load demand flexibilit…
View article: Hierarchical Transactive Control of Flexible Building Loads Under Distribution LMP
Hierarchical Transactive Control of Flexible Building Loads Under Distribution LMP Open
With grid modernization efforts, future distribution networks, which consist of various distributed generators and flexible loads, will be more flexible and active. All new network components of distributed energy resources (DERs) drive an…
View article: Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services
Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services Open
A transition from generation on demand to consumption on demand is one of the solutions to overcome the many limitations associated with the higher penetration of renewable energy sources. Such a transition, however, requires a considerabl…
View article: A Data-Driven, Distributed Game-Theoretic Transactional Control Approach for Hierarchical Demand Response
A Data-Driven, Distributed Game-Theoretic Transactional Control Approach for Hierarchical Demand Response Open
Modern power systems require flexible demand-side resources to maintain the balance between electricity supply and demand. Building thermostatically controlled loads (TCLs) are great flexible assets, as they account for a significant porti…
View article: Comparative analysis of model-free and model-based HVAC control for residential demand response
Comparative analysis of model-free and model-based HVAC control for residential demand response Open
In this paper, we present a comparative analysis of model-free reinforcement learning (RL) and model predictive control (MPC) approaches for intelligent control of heating, ventilation, and air-conditioning (HVAC). Deep-Q-network (DQN) is …
View article: Deep Reinforcement Learning for Autonomous Water Heater Control
Deep Reinforcement Learning for Autonomous Water Heater Control Open
Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water…
View article: Stochastic Pricing Game for Aggregated Demand Response Considering Comfort Level
Stochastic Pricing Game for Aggregated Demand Response Considering Comfort Level Open
In recent years, demand response (DR) has been explored as a fundamental strategy for demand-side management due to its advantages in mediating intermittency of renewable energy generation, load shifting, etc. To engage customers in DR pro…
View article: Gaussian Process Regression for Aggregate Baseline Load Forecasting
Gaussian Process Regression for Aggregate Baseline Load Forecasting Open
Demand response (DR) is one of the most effective ways to maintain the reliability and improve the flexibility of power systems. Accurate forecasts of baseline loads are essential for DR programs. In the era of big data, machine learning-b…
View article: Power allocation by load aggregator with heterogeneous loads using weighted projection
Power allocation by load aggregator with heterogeneous loads using weighted projection Open
View article: Double Deep Q-Networks for Optimizing Electricity Cost of a Water Heater
Double Deep Q-Networks for Optimizing Electricity Cost of a Water Heater Open
Electric water heaters represent 14% of the electricity consumption in the residential buildings and the cost associated with domestic water heating account for a good portion of the household expenses in the United States. In this context…
View article: Methodology for Interpretable Reinforcement Learning Model for HVAC Energy Control
Methodology for Interpretable Reinforcement Learning Model for HVAC Energy Control Open
Deep reinforcement learning (DRL) approaches have been used in various application areas to improve efficiency, optimization, or automation. However, very little is known about how the DRL algorithms make decisions and what features affect…
View article: Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control
Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control Open
View article: Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control
Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control Open
Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while m…
View article: Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning Open
View article: A comparison study on trading behavior and profit distribution in local energy transaction games
A comparison study on trading behavior and profit distribution in local energy transaction games Open
View article: Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses
Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses Open
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, …