Description
The electrical power industry is undergoing radical change due to the push for renewable energy that makes energy supply less predictable. Smart meters along with analytics software can grant insights into customer-specific consumption and thereby enable a better match between the demand and supply side for an electric utility. However, the vast amount of allocatable smart metering data and complexity of analytics pose challenges to database system. We address the implementation of an analytics ap-proach to optimize customer portfolios, eventually preventing excess energy procurement. Using real-world and simulated data, we test the suitability of big data approaches as well as traditional relational database technology. Furthermore, we present solutions based on big data platforms and demonstrate their cost effectiveness and performance. Our findings suggest economic feasibility of big data solutions for large utilities. Small and medium-sized utilities are advised to invest in more cost-effective solutions such as cluster-based systems.
Recommended Citation
Fritz, Manuel; Albrecht, Simon; Ziekow, Holger; and Strüker, Jens, "Benchmarking Big Data Technologies for Energy Procurement Efficiency" (2017). AMCIS 2017 Proceedings. 5.
https://aisel.aisnet.org/amcis2017/GreenIS/Presentations/5
Benchmarking Big Data Technologies for Energy Procurement Efficiency
The electrical power industry is undergoing radical change due to the push for renewable energy that makes energy supply less predictable. Smart meters along with analytics software can grant insights into customer-specific consumption and thereby enable a better match between the demand and supply side for an electric utility. However, the vast amount of allocatable smart metering data and complexity of analytics pose challenges to database system. We address the implementation of an analytics ap-proach to optimize customer portfolios, eventually preventing excess energy procurement. Using real-world and simulated data, we test the suitability of big data approaches as well as traditional relational database technology. Furthermore, we present solutions based on big data platforms and demonstrate their cost effectiveness and performance. Our findings suggest economic feasibility of big data solutions for large utilities. Small and medium-sized utilities are advised to invest in more cost-effective solutions such as cluster-based systems.