Abstract
Multiple vehicles control at an intersection is one of the most challenging scenarios in the vehicle control field due to the high number of constraints. However, regarding the control strategies of an isolated intersection, few studies have considered the longitudinal-lateral dynamics comprehensively. In the approaches that treat the turning curve as a straight line, a longitudinal dynamic model is adopted, while the lateral execution ability is ignored. The ideal decisions that ignore vehicle dynamics are not accurately executed by actual vehicles, as doing so would to be futile. In addition, due to the strong coupling and nonlinearity of the longitudinal-lateral dynamics model, the optimization problem is difficult to solve. Therefore, in this paper, a reasonable model, the simplified vehicle dynamics model coupled with longitudinal-lateral dynamics is used to describe longitudinal, lateral, and yaw motions comprehensively. Optimization of the longitudinal and lateral co
Segmint Launches Unparalleled Artificial Intelligence (AI) Predictive Modeling Platform to Drive Strategic Decision Making
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Data Scientist
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Chewy’s Supply Chain Team is seeking a Senior Supply Chain Data Scientist to join the growing Supply Chain Analytics team. Combining a background in Supply Chain with a deep understanding of analytics and data science, you will be a part of a team responsible for strategic planning and research initiatives centered around our inventory and fulfillment network. This includes optimizing the way that we predict customer demand, purchase inventory, position inventory within our network, and ship orders to our customers. The Supply Chain team operates in a fast-paced environment where every day brings new challenges and new opportunities. You will be responsible for designing and implementing solutions and technologies which will handle the growing business needs and play a key role in redefining what it means to be a world-class Data Science and Supply Chain organization.
Abstract
Virtual power plants (VPPs) have become a driving force for the decentralized energy industry, due to their efficient management and control of distributed energy resources. Most of the operation strategies for VPPs are designed based on the day-ahead forecasts. However, the prediction errors of the renewable energy sources (RES) and loads in the power dispatch schedule can lead to a sub-optimal operation. In this paper, an adaptive and predictive energy management strategy for a real-time optimal operation of VPPs is proposed based on the model predictive control technique with a feedback correction (FC) to compensate for the prediction error. This strategy has two parts: a) receding-horizon optimization (RHO), and b) FC. In the first part, a hybrid prediction algorithm based on the integration of the time series model and the Kalman filter is used to forecast the output powers of RES and the loads. Based on the prediction, the RHO model schedules the operation following t