Title :
“Back-to-basics”: Operationalizing data mining and visualization techniques for utilities
Author :
Nakafuji, Dora ; Aukai, Thomas ; Dangelmaier, Lisa ; Reynolds, Chris ; Yoshimura, Jennifer ; Hu, Ying
Author_Institution :
Hawaiian Electr. Co., Honolulu, HI, USA
fDate :
July 31 2011-Aug. 5 2011
Abstract :
Today, the family of Hawaiian Electric utilities, consisting of Hawaii Electric Light Company (HELCO) on the Big Island of Hawaii, Maui Electric Company (MECO) on the islands of Maui, Molokai and Lanai and Hawaiian Electric Company (HECO) on the island of Oahu, are contending with PV penetrations in excess of 20 percent during high electricity demand days (e.g. weekdays) and over 60 percent penetration during light load demand days (e.g. weekends) on certain distribution circuits. With the emergence of more, low-cost photovoltaic (PV) systems and consumer self-generation programs, such as net energy metering and feed-in-tariffs, today´s utilities are facing a fundamental shift towards a need to get more visibility to customer-sited, distributed generating resources (DG). The Hawaiian utilities are among an emerging set of utilities around the world leading the nation in contending with high levels of renewable penetration on their distribution systems. Hawaiian Electric Utilities are pursuing efforts to gather, evaluate and target (GET) relevant resource datasets in conjunction with time synchronized system data to enable planning, forecasting and operations with high penetration of variable, distributed renewable resources.
Keywords :
data mining; data visualisation; electricity supply industry; Hawaii Electric Light Company; Hawaiian Electric Company; Hawaiian Electric utilities; Maui Electric Company; consumer self-generation programs; data mining; distributed renewable resources; distribution circuits; distribution systems; feed-in-tariffs; low-cost photovoltaic systems; net energy metering; visualization technique; Companies; Forecasting; Monitoring; Planning; Sensors; Substations; Wind;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033630