Data-driven model for rooftop excess electricity generation. Kiguchi, Y., Heo, Y., & Choudhary, R. In 14th International Conference of IBPSA, Building Simulation 2015, pages 2631–2638, Hyderabad, 2015. IBPSA. ![Data-driven model for rooftop excess electricity generation [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Large scale rooftop PV deployment generates significant amounts of excess electricity (the difference between real-time PV generation and on-site consumption). Accurate forecasting of excess electricity generation becomes important for energy traders participating in a liberalised electricity market in order to avoid penalties imposed by the market in case of imbalance between actual and predicted values. This paper proposes a data driven Gaussian process model for forecasting excess generation of electricity. An illustrative study, with a-year long dataset from 287 households, is used to derive the forecasting model. Results show that a-year long data from 18 households is sufficient for accurate prediction of excess generation across a uniform geographic and socio-economic setting.
@inproceedings{kiguchi_data-driven_2015,
address = {Hyderabad},
title = {Data-driven model for rooftop excess electricity generation},
url = {http://www.bs2015.in/files/BS2015_Proceeding.pdf},
abstract = {Large scale rooftop PV deployment generates significant amounts of excess electricity (the difference between real-time PV generation and on-site consumption). Accurate forecasting of excess electricity generation becomes important for energy traders participating in a liberalised electricity market in order to avoid penalties imposed by the market in case of imbalance between actual and predicted values. This paper proposes a data driven Gaussian process model for forecasting excess generation of electricity. An illustrative study, with a-year long dataset from 287 households, is used to derive the forecasting model. Results show that a-year long data from 18 households is sufficient for accurate prediction of excess generation across a uniform geographic and socio-economic setting.},
booktitle = {14th {International} {Conference} of {IBPSA}, {Building} {Simulation} 2015},
publisher = {IBPSA},
author = {Kiguchi, Y. and Heo, Y. and Choudhary, R.},
year = {2015},
pages = {2631--2638}
}
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