A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually in...

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Bibliographic Details
Published inEnergies (Basel) Vol. 11; no. 7; p. 1750
Main Authors Wang, Fei, Li, Kangping, Wang, Xinkang, Jiang, Lihui, Ren, Jianguo, Mi, Zengqiang, Shafie-khah, Miadreza, Catalão, João
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2018
Subjects
capacity estimation
Case studies
Customers
Datasets
distributed photovoltaic
Electric power
Electricity distribution
Energy storage
Laboratories
Methods
Photovoltaics
Solar cells
Specific capacity
Stress concentration
support vector machine
Support vector machines
Utilities
weather status driven difference features
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Summary:Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.
ISSN:1996-1073
1996-1073
DOI:10.3390/en11071750