Advanced control algorithms for building systems have significant potential in reducing energy consumption while optimizing thermal comfort. Success of such algorithms is critically contingent on several different types of sensor systems, which are ...

Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments. We propose a reinforcement learning controller with surprise minimizing modifications in its architecture. We suggest that ...

Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly ...

Energy demand forecasting at the household level is an important issue in smart energy grids to facilitate applications such as residential Demand Response (DR). However, if a separate machine learning model is trained for each house, the erratic nature ...

Availability of high fidelity timeseries data is imperative for critical power grid operational tasks such as state estimation, DER scheduling, etc. However, the data obtained from the metering infrastructure is prone to disruptions due to communication ...

In this paper, we address the problem of Data Centers (DCs) energy efficiency considering their integration into the electrical and thermal grids by emphasizing the role of the DC Digital Twin model in DC flexibility management. Due to their high ...

The energy required to cool a data center (DC) depends on both thermal and workload management. Although existing energy- and temperature-aware workload assignment approaches reduce operational expenditure by minimizing cooling energy consumption, they ...

The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection ...

In data centres, there exist several techniques for energy efficiency purposes. When applied, most of those techniques have impact on the quality (e.g. performance) of the underlying services. A careful study is required in order to optimise such an ...

Artificial Intelligence (AI) has changed our daily lives. The evolution from centralised cloud-hosted services towards embedded and mobile devices has shifted the focus from quality-related aspects towards the resource demand of machine learning. Its ...

The shift towards an advanced electricity metering infrastructure has gained traction because of several smart meter roll-outs. This accelerated research in Non-Intrusive Load Monitoring techniques. These techniques highly benefit from the temporal ...

Cyber-physical microgrids hold the key to a carbon-neutral power sector since they enable renewable and distributed energy resource integration, can alleviate overloaded distribution systems, and provide economic energy by generating and consuming power ...

Future smart grids can and will be subject of systematic attacks that can result in monetary costs and reduced system stability. These attacks are not necessarily malicious, but can be economically motivated as well. Emerging flexibility markets are of ...

Residential load data generated by smart meters usually contain valuable information on household electricity consumption behaviors. Identifying load patterns and establishing typical consumer profiles will benefit stakeholders, including consumers, ...

The need for reliable and accessible electric vehicle (EV) charging data is becoming increasingly important as governments and industries aim to create low-carbon transport systems. Without careful grid management, the security of supply could be ...

Modern computing systems need to execute applications in an energy-efficient manner. To this end, operating systems, middleware, and run-time systems offer plenty of parameters that support fine-tuning their behaviour. However, their individual and ...

While long-term wide-range blackouts have been studied extensively from a power systems perspective, the role of ICT in the recovery of smart energy systems has not been investigated to the same extent. This paper presents a flexible blackstart service ...

The emergence of low-power accelerators has enabled deep learning models to be executed on mobile or embedded edge devices without relying on cloud resources. The energy-constrained nature of these devices requires a judicious choice of a deep learning ...

In this paper, we analyze the relationship between generation dispatch solutions produced by the DC optimal power flow (DC OPF) problem by the AC optimal power flow (AC OPF) problem. While there has been much previous work in analyzing the approximation ...

While reinforcement learning (RL) is gaining popularity in energy systems control, its real-world applications are limited due to the fact that the actions from learned policies may not satisfy functional requirements or be feasible for the underlying ...