IEEE BDA Webinar Series: Visualization and Analytics for high penetration of Distributed Energy Resources (VADER)
Time: July 6 (12:30pm-1:30pm, CST)
The webinar with the title: Visualization and Analytics for high penetration of Distributed Energy Resources (VADER), was held July 6, 2018 from 12:30 – 1:30 PM CST. The presentation was anchored by Sila Kiliccote, managing director of grid innovations at the Stanford and the leader of the Grid Integration, Systems and Mobility research at SLAC National Accelerator Laboratory.
Abstract: Distribution grid is going through a transformation. This transformation is driven partly due to adoption of DERs at the grid’s edge, increasing load flexibility and active control devices, and partly the growing availability of data from smart meters. In this webinar, we will discuss a new data analytics platform and open source analytics for better planning the distribution grid. The talk will cover the growing need for integrated platforms, the software architecture implemented for VADER, explain the choices that were made, and the top analytics developed and under deployment.
To access the announcement flyer, click here.
An Energy IoT Platform For Real-Time Production and Delivery of Wind Power Generation Forecasts
NSF Big Data Hub hosted the first webinar from our IEEE PES Subcommittee on Big Data & Analytics for Power Systems at 2:00 PM, June 28, EST. We had Dr. Chandrasekar Venkatraman and Dr. Pierre Huyn from Big Data Laboratory, Hitachi America, Ltd. The webinar is scheduled to be held approximately every month or two, with speakers from industries and academic institutions.
Time: June 28, 2017 at 2:00 PM EST (1:00 PM MST or 11:00 AM PST)
Abstract: Power generation using renewable energy resources such as wind turbines has grown increasingly popular. Because the underlying meteorological processes are highly unpredictable, it has become important to be able to provide accurate power forecasts in real-time. In this talk we will describe an end-to-end IoT platform that enables SCADA sensor data to be collected in real-time directly from a remote wind farm, securely and reliably transmitted to cloud servers where data is analyzed to create forecasting models. These models are then applied to the turbine sensor data stream to generate day-ahead power generation forecasts. We will also describe the machine learning techniques used as the basis for the forecasting models and our strategies to make the solution scalable for other big data applications.
Bio: Chandrasekar (Chandra) Venkatraman is Principal Research Scientist at Hitachi America Research and Development in the Big Data Laboratory focusing on Industrial IoT Architectures and Analytics for Energy. Prior to joining he was Chief Scientist at FogHorn Systems – Palo Alto based start-up focusing on Big Data Analytics and applications platform for Industrial Internet of Things (IoT). Chandra was with Hewlett Packard Labs, Palo Alto for almost two decades working on Information architectures, distributed computing, in-home network, ePrint architecture, sensor networks and Internet of Things. He has authored over 15 patents and a number of research papers and talks.
Pierre Huyn has over 30 years of research and advanced development experience in data management, big data analytics, and software engineering. His current interest is in big data architectures for IoT and deep learning for time series data in the domain of renewable energy.
More info can be found here>
Website link can be found here>
IEEE Big Data Webinar
Dr. Mladen Kezunovic, Regents professor and director of the Texas A&M University Smart Grid Center held a webinar on Thursday, April 13th, 2017. The webinar, “Big Data Applications in Smart Grids: Benefits and Challenges” took place from 1:00-2:00 p.m. ET. The focus of this webinar was on different BD sources in the utility industry that range from field measurements obtained by substation/feeder intelligent electronic devices, to specialized commercial and/or government/state databases: weather data of different types, lightning detection data, seismic data, fire detection data, electricity market data, vegetation and soil data, etc.