Smart Power Meters in Augmented Reality Environment for Electricity Consumption Awareness
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- Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
- Rafal Weron & Florian Ziel, 2018.
"Electricity price forecasting,"
HSC Research Reports
HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
- Katarzyna Maciejowska & Rafal Weron, 2019. "Electricity price forecasting," HSC Research Reports HSC/19/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
- Hargreaves, Tom & Nye, Michael & Burgess, Jacquelin, 2010. "Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors," Energy Policy, Elsevier, vol. 38(10), pages 6111-6119, October.
- Weron, Rafał, 2014.
"Electricity price forecasting: A review of the state-of-the-art with a look into the future,"
International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
- Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
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Keywords
power measurement; Augmented Reality; consumption awareness; IoT sensors;All these keywords.
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