A deep neural network approach for behind-the-meter residential PV size, tilt and azimuth estimation. (15th January 2020)

• Record Type: Journal Article
• Authors: Mason, Karl; Reno, Matthew J.; Blakely, Logan; Vejdan, Sadegh; Grijalva, Santiago
• Is Part Of: Solar energy. Volume 196(2020)
• Page Start: 260
• Page End: 269
• Publication Date: 2020-01-15
• Subjects: PV size -- Tilt -- Azimuth -- PV parameter estimation -- Solar -- Smart meters -- Smart grid -- Machine learning -- Deep neural networks; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2019.11.100
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Deep Neural Networks applied to estimate solar panel size, tilt and azimuth. Solar panel size estimate with mean percentage error of 3.98%. Tilt and azimuth estimated with absolute error of 2.55 and 4.71 degrees respectively. Net load data resolution does not heavily impact accuracy. Proposed approach is robust to mislabeled training data. Abstract: There is an ever-growing number of photovoltaic (PV) installations in the US and worldwide. Many utilities do not have complete or up-to-date information of the PVs present within their grids. This research presents a deep neural network approach for estimating PV size, tilt, and azimuth using only behind-the-meter data. It is found that the proposed deep neural network (DNN) method can estimate PV size with an error of 2.09% in a data set with fixed tilt and azimuth values and 3.98% in a data set with varying tilt and azimuths. This is a lower error than the benchmark linear regression approach. A net load data resolution of 1 min provides the lowest error when estimating the PV size. The proposed DNN is also reasonably robust to erroneous training data. When applied to estimate PV tilt and azimuth, the proposed method achieves a mean absolute percentage error of 10.1% and 2.8% respectively. These error metrics are 2.0 × and 3.7 × lower, respectively, than the benchmark linear regression achieves. It was observed that a higher data resolution (1 min) does not provide significant gains in accuracy. It is recommended that a data resolution of 60 min is used to reduce the effects of phenomena such as cloud enhancements. The proposed deep neural network approach is also highly robust, maintaining reasonable accuracy with high levels of mislabeled training data.