Sacramento Municipal Utility District (SMUD)’s Data Analytics Initiatives
Time: Nov 28, 2017 (This Tuesday) at 2:00 PM EST (1:00 PM CST, 12:00 PM MST or 11:00 AM PST)
Abstract: SMUD implemented $350 million worth of smart grid projects that resulted in dramatic increases in the amount of available data. SMUD used the data to implement several initiatives in the areas of workforce and asset management, operational efficiencies, distributed energy resource management and customer programs. SMUD continues to use the data to improve grid performance and to develop programs focused on the customer side—SMUD’s customer analytics team worked on a load disaggregation model to identify customers with large HVAC usage, base loads and variable loads. The model empowered three data products: 1) a rank list of high potential customers who can benefit from SMUD’s offerings; 2) an internal app for energy auditors to provide personalized educational talking points when conduct audits; and 3) personalized marketing messages. By providing relevant and personalized messages and offerings, SMUD is building a better customer experience.
Bio: Jim Parks is a program manager in the Energy Research and Development department at the Sacramento Municipal Utility District (SMUD). He just completed a $308 million smart grid initiative (SmartSacramento®) with over 40 individual projects ranging from smart meters and distribution automation to customer programs including demand response and energy efficiency. He currently oversees energy efficiency and smart grid R&D projects. Prior to his current assignment, he worked with emerging energy efficiency technologies, electric transportation, energy efficiency program development, energy efficiency program operations and transmission planning. Yifan Lu is a project manager leading the customer analytic team at SMUD.
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An Energy IoT Platform For Real-Time Production and Delivery of Wind Power Generation Forecasts
NSF Big Data Hub will host the first webinar from our IEEE PES Subcommittee on Big Data & Analytics for Power Systems at 2:00 PM, June 28, EST. We will have 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 will be holding a webinar on Thursday, April 13th, 2017. The webinar, “Big Data Applications in Smart Grids: Benefits and Challenges” will take place from 1:00-2:00 p.m. ET. The focus of this webinar is 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.
You can register for the webinar here>