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Rancilio, G.; Lucas, A.; Kotsakis, E.; Fulli, G.; Merlo, M.; Delfanti, M.; Masera, M. Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis. Energies 2019, 12, 3312. https://doi /10.3390/en12173312
Rancilio G, Lucas A, Kotsakis E, Fulli G, Merlo M, Delfanti M, Masera M. Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis. Energies. 2019; 12(17):3312. https://doi /10.3390/en12173312
Rancilio, Giuliano, Alexandre Lucas, Evangelos Kotsakis, Gianluca Fulli, Marco Merlo, Maurizio Delfanti, and Marcelo Masera. 2019. "Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis" Energies 12, no. 17: 3312. https://doi /10.3390/en12173312
Rancilio, G., Lucas, A., Kotsakis, E., Fulli, G., Merlo, M., Delfanti, M., & Masera, M. (2019). Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis. Energies, 12(17), 3312. https://doi /10.3390/en12173312
For the initial release of the RAE dataset, we consider two houses: House 1 and House 2. We are actively assessing other houses that can be monitored and added to this dataset. The monitoring system that we present here is an accurate and reliable data capture system that can be easily installed in a house to collect data in the same format and frequency. Researchers interested in installing this system and adding data to RAE can contact the lead author.
In addition to smart grid and NILM, this dataset can be used in research that looks at statistical signal processing and blind source separation, energy use behaviour, eco-feedback and eco-visualizations, application and verification of theoretical algorithms/models, appliance studies, demand forecasting, smart home frameworks, grid distribution analysis, time-series data analysis, energy-efficiency studies, occupancy detection, energy policy and socio-economic frameworks, and advanced metering infrastructure (AMI) analytics.
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