We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network

Shipman, Rob; Roberts, Rebecca; Waldron, Julie; Naylor, Sophie; Pinchin, James; Rodrigues, Lucelia; Gillott, Mark

doi:10.1016/j.energy.2021.119813

All Outputs (3)

Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles (2021)
Journal Article

The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an... Read More about Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles.

Demonstrating the potential of indoor positioning for monitoring building occupancy through ecologically valid trials (2021)
Journal Article

Assessing building performance related to energy consumption in post-design-occupancy stage requires knowledge of building occupancy pattern. These occupancy data can potentially be collected from trials and used to improve the prediction capability... Read More about Demonstrating the potential of indoor positioning for monitoring building occupancy through ecologically valid trials.

We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network (2021)
Journal Article

© 2021 Elsevier Ltd Vehicle-to-grid (V2G) services utilise a population of electric vehicle batteries to provide the aggregated capacity required to participate in power and energy markets. Such participation relies on the prediction of available cap... Read More about We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network.