Related papers: Data Driven Chiller Plant Energy Optimization with…
In this paper, we study a problem of controlling cooling facilities and computational equipments for energy-efficient operations of data centers. Although a plethora of approaches have been proposed in previous literatures, there is a lack…
We address the problem of temperature regulation and optimal energy sharing in district heating systems (DHSs) where the demand and system parameters are unknown. We propose a temperature regulation scheme that employs data-driven on-policy…
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a promising paradigm for training control policies in simulation.…
We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy…
Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which was found to be the major…
Efficient management of storage resources in big data and cloud computing environments requires accurate identification of data's "cold" and "hot" states. Traditional methods, such as rule-based algorithms and early AI techniques, often…
Leveraging electrochemical and thermal energy storage systems has been proposed as a strategy to reduce peak power in data centers. Thermal energy storage systems, such as chilled water tanks, have gained increasing attention in data…
This work presents a fully data-driven, black-box pipeline to obtain an optimal control policy for a multi-loop building control problem based on historical building and weather data, thus without the need for complex physics-based…
Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components…
Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement…
Energy costs are quickly rising in large-scale data centers and are soon projected to overtake the cost of hardware. As a result, data center operators have recently started turning into using more energy-friendly hardware. Despite the…
Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement learning, but traditional reinforcement learning methods…
Effective data center cooling is crucial for reliable operation; however, cooling systems often exhibit inefficiencies that result in excessive energy consumption. This paper presents a three-stage, physics-guided machine learning framework…
This study proposes a scalable Digital Twin framework for energy optimization in data centers.The framework integrates IoT-based data acquisition, cloud computing, and machine learning techniques to enable real-time monitoring, forecasting,…
Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is…
The optimal management of a building's microclimate to satisfy the occupants' needs and objectives in terms of comfort, energy efficiency, and costs is particularly challenging. This complexity arises from the non-linear, time-dependent…
Global digitalization has given birth to the explosion of digital services in approximately every sector of contemporary life. Applications of artificial intelligence, blockchain technologies, and internet of things are promising to…
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort. Data-driven approaches based on neural networks have been proposed to facilitate system modelling.…
In this paper, an energy conservation measure that optimizes the planning of heating and cooling systems for tertiary sector buildings is proposed. It consists of a model-based predictive control approach that employs a grey-box model built…
Industrial refrigeration systems have substantial energy needs, but optimizing their operation remains challenging due to the tension between minimizing energy costs and meeting strict cooling requirements. Load shifting--strategic…