Photovoltaic Power Disaggregation using a Non-Intrusive Load Monitoring Regression Model

Jaramillo, Andres F. Moreno; Mohamed, Ahmed A. Raouf; Laverty, David; Rincón, Jesús Martínez del; Foley, Aoife

doi:10.1109/isgteurope52324.2021.9640183

Cited by 2 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, results presented by authors in [27] and [30] are improved in this case. The R 2 score also proves the proposed method to be more accurate than previous results presented in [21].…”

Section: Benchmarking and Validationsupporting

confidence: 57%

“…The performance of the regression model is evaluated using the mean bias error (MBE), mean average error (MAE), root‐mean‐squared error (RMSE), and coefficient of determination ( R 2 score). Definitions of the implemented metrics for regression models are shown in Equations (14) to (17) [21, 40].

M B E = \frac{1}{m} {140%true \sum}_{i = 1}^{m} (y_{i} - \overset{trueˆ}{y_{i}})

M A E = \frac{1}{m} {140%true \sum}_{i = 1}^{m} | y_{i} - \overset{trueˆ}{y_{i}} |

R M S E = \sqrt{\frac{1}{m} {140%true \sum}_{i = 1}^{m} {(y_{i} - \overset{y_{i}}{true})}^{2}}

R^{2} S c o r e = 1 - \frac{{140%true \sum}_{i = 1}^{m} {(y_{i} - {\hat{y}}_{i})}^{2}}{{140%true \sum}_{i = 1}^{m} {(y_{i} - \overset{y}{true})}^{2}}

where y i is the i th expected value,

\overset{trueˆ}{y_{i}}

is the i th prediction,

\bar{y}

is the median value of the expected values vector ( y test ), and m is the size of y test .…”

Section: Principle and Methodsmentioning

confidence: 99%

“…The performance of the regression model is evaluated using the mean bias error (MBE), mean average error (MAE), root-mean-squared error (RMSE), and coefficient of determination (R 2 score). Definitions of the implemented metrics for regression models are shown in Equations ( 14) to (17) [21,40].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…RF defines its predictions based on an agreement between estimators (called trees). Each estimator, which can be seen as an upside-down tree, works separately to make a prediction considering training records [21]. The final answer is then computed as the average of all predictions in the forest [33].…”

Section: Load Identificationmentioning

confidence: 99%

“…Additionally, the adequation of the external data to create the test set was performed as described in Section III. However, an initial step was completed for House C of Smart * Project to convert the electric current of the PV system into power, which is described in [21]. The NILM method was evaluated using ANN, kNN, and RF.…”

Section: Benchmarking and Validationmentioning

confidence: 99%

See 4 more Smart Citations

Effective identification of distributed energy resources using smart meter net‐demand data

et al. 2022

Self Cite

View full text Add to dashboard Cite

International policies and targets to globally reduce carbon dioxide emissions have contributed to increasing penetration of distributed energy resources (DER) in lowvoltage distribution networks. The growth of technologies such as rooftop photovoltaic (PV) systems and electric vehicles (EV) has, to date, not been rigorously monitored and record keeping is deficient. Non-intrusive load monitoring (NILM) methods contribute to the effective integration of clean technologies within existing distribution networks. In this study, a novel NILM method is developed for the identification of DER electrical signatures from smart meter net-demand data. Electrical profiles of EV and PV systems are allocated within aggregated measurements including conventional electrical appliances. Data from several households in the United States are used to train and test classification and regression models. The usage of conventional machine learning techniques provides the proposed algorithm with fast processing times and low system complexity, key factors needed to differentiate highly variable DER power profiles from other loads. The results confirm the effectiveness of the proposed methodology to individually classify DER with performance metrics of 96% for EV and 99% for PV. This demonstrates the potential of the proposed method as an embedded function of smart meters to increase observability in distribution networks. K E Y W O R D Sdemand-side management, distributed energy resources, non-intrusive load monitoring, smart grids, supervised machine learning methodsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Thus, results presented by authors in [27] and [30] are improved in this case. The R 2 score also proves the proposed method to be more accurate than previous results presented in [21].…”

Section: Benchmarking and Validationsupporting

confidence: 57%