BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.geoinfo-lab.org//DTYBZN BEGIN:VTIMEZONE TZID:+07 BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:+07 TZOFFSETFROM:+0700 TZOFFSETTO:+0700 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-DTYBZN@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T101500 DTEND;TZID=+07:20241217T103000 DESCRIPTION:Geospatial big data plays a pivotal role in the context of smar t grids\, revolutionizing the way modern electrical grids are monitored\, managed\, and optimized. Smart grids integrate advanced sensing\, communic ation\, and control technologies to enhance the efficiency\, reliability\, and sustainability of electricity distribution. While locational informat ion of the smart meters is pivotal\, the consumption patterns combined wit h other information like consumer type\, land use and local weather condit ions can really enhance the assessment of energy requirements and usage th us leading to a Spatial Energy Management System (SEMS). This will signifi cantly enrich location-aware decision-making\, real-time monitoring\, and predictive analysis\, thus improving energy resource optimisation and achi eving the goal of SDG-7. The SEMS web application represents a critical ad vancement\, facilitating dynamic visualization of temporal clusters\, acro ss energy sources and their evolution over time. By overlaying clusters ac ross different time periods\, utility personnel gain insights into custome r categorizations vs actual utilization\, enabling understanding of demand fluctuations\, outages and faults\, fault localization within the electri c network and demand-generation assessment of renewable energy. \nThe prim ary objective is the development of a dynamic web-based SEMS application c apable of visualizing temporal changes and consumption patterns\, while al so providing alerts to facilitate proactive management.\n\nThe initial pha se of the methodology focuses on the Jeedimetla region in Hyderabad\, leve raging real-world data encompassing 6\,000 households categorized into res idential\, commercial\, and industrial segments. Quantum Geographic Inform ation System (QGIS) software is employed to establish the electric network . To store the spatial and temporal consumption data\, the data storage in frastructure employs PostgreSQL\, enhanced with the PostGIS extension. Thi s combination is selected due to its robust capabilities in accommodating and managing complex datasets. Additionally\, a comprehensive and refined data model has been established to serve as the framework for storing Adva nced Metering Infrastructure (AMI) data. Spatial data retrieved from the P ostgreSQL database is exposed through a Web GIS Server (GeoServer) as a We b Feature Service\, facilitating its integration into applications. This s patial information is utilized within the OpenLayers Software Development Kit (SDK) to visually render data within a JavaScript-based application en vironment. Non-spatial data is accessed through Application Programming In terfaces (APIs) integrated within a Node server\, operating as REST endpoi nts. Concurrently\, the React JavaScript library is employed to present th is non-spatial information to end-users in an interactive format.\n\nThe S EMS Geospatial Visualization Engine comprises three key components. The fi rst component features two primary views: the Basic View\, which presents hourly usage consumption data\, and the Combined View\, integrating map an d graph interfaces. This combined view allows users to select specific tim e periods\, customer locations\, transformers\, or feeders\, with the grap h view displaying corresponding hourly consumption data. Both views facili tate the visualization of consumption patterns for customer classes or typ es\, which are pre-defined in the system.\nThe second component of the SEM S Visualization Engine aggregates data at the daily level\, classifying us ers into low\, medium\, and high consumption categories. These classificat ions are visually represented on the map view\, providing insights into co nsumption trends across the study area.\nThe third component integrates we ather data sourced from an open-source Weather API with customer energy co nsumption data. Weather information is stored in the PostgreSQL database\, and the combined view enables the graphical representation of weather dat a changes alongside energy consumption data. This integration enhances und erstanding of how weather fluctuations impact customer energy usage patter ns.\n\nBy leveraging advanced sensing\, communication\, and control techno logies\, three components of SEMS Geospatial Visualization engine framewor k provides a comprehensive platform for understanding and visualizing ener gy consumption and demand. The spatial view aids in developing spatial clu sters\, which assist electric utility personnel in comprehending consumpti on patterns and energy demand requirements for specific areas\, facilitati ng efficient management of power shortage scenarios.\n\nThe SEMS visualiza tion engine supports various utility use cases\, including energy loss ide ntification\, detection of energy overuse\, and dynamic reclassification o f users based on current consumption data. As a prospective avenue for fut ure research\, integrating this Geospatial Visualization Engine framework with machine learning-based prediction models holds promise for forecastin g energy consumption and dynamically classifying users. Such advancements are anticipated to further empower utility personnel in decision-making\, real-time monitoring\, and predictive analytics within smart grid environm ents. DTSTAMP:20260514T195205Z LOCATION:Room34-1102 SUMMARY:Visualizing and Managing Smart Grids with Geospatial Big Data: The SEMS Approach - Venkata Satya Rama Rao Bandreddi URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/DTYBZN/ END:VEVENT END:VCALENDAR