Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices

Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicles in future taxi industry. The cost of hydrogen production is minimized by utilizing the proposed forecasting tool to reduce the hydrogen produced during high cost on-peak hours and guide hydrogen producer to store sufficient hydrogen during low cost off-peak hours

Local Gaussian processes for efficient fine-grained traffic speed prediction

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Traffic Speed Data (2009 - 2016)

<div>The dataset includes records of the average speed in the streets of Manhattan, from 2009 to 2016, and was derived from the following paper:</div><div><br></div><div><i>Exploring Traffic Dynamics in Urban Environments using Vector-Valued Functions, Jorge Poco, Harish Doraiswamy, Huy. T. Vo, João L. D. Comba, Juliana Freire, and Cláudio. T. Silva. In Proceedings of the 2015 Eurographics Conference on Visualization (EuroVis '15). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 161-170.</i></div