The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV
Abstract
Distributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high.
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Photovoltaic and solar power forecasting for smart grid energy management
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Deep learning for estimating building energy consumption
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Long Term Probabilistic Load Forecasting and Normalization With Hourly Information
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Metadata
- Authors
- Wang, Y., Zhang, N., Chen, Q., Kirschen, D. S., Li, P., & Xia, Q.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2018-05
- Volume
- 33
- Issue
- 3
- Pages
- 3255-3264
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2764018574
- Cited by
- 292
- References
- 28
- FWCI
- 9.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, Y., Zhang, N., Chen, Q., Kirschen, D. S., Li, P., & Xia, Q. (2018). Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2017.2762599
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