Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Sossan, Fabrizio; Nespoli, Lorenzo; Medici, Vasco; Paolone, Mario

doi:10.1109/tii.2018.2791932

Cited by 70 publications

(66 citation statements)

References 25 publications

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“…A drawback is that it will not capture any changes to the consumption pattern once the PV installation is in place. A second method is based on the assumption that the daily load curve should be as smooth as possible as suggested by Sossan et al This works well for the consumption pattern associated with households and apartments, but not for installations servicing offices with steep changes in the consumption connected to the office hours. The best performing method in the previous study was based on separating production and consumption in the frequency domain.…”

Section: Resultsmentioning

confidence: 99%

“…A second method is based on the assumption that the daily load curve should be as smooth as possible as suggested by Sossan et al This works well for the consumption pattern associated with households and apartments, but not for installations servicing offices with steep changes in the consumption connected to the office hours. The best performing method in the previous study was based on separating production and consumption in the frequency domain. However, the introduction of batteries and time‐dependant electricity tariffs will strive to make the consumption fit the production as closely as possible, rendering such a strategy less promising for the future.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the results we present here based on this method will be better than what will be possible to achieve in the general case when information about gross consumption and feed‐in will come from different time periods. The second procedure is to look for the parameter vector that results in the smoothest daily load curve. This is a version of one of the methods from the paper by Sossan et al where the smoothness is measured using a differentiated time series. Here, we differentiate the daily load curve:

true \sum_{t = 1}^{24} false| {\overset{c}{true}}_{g}^{m} false(t false) - {\overset{c}{true}}_{g}^{m} false(t - 1 false) false| .

We then want to minimize the sum in Equation given our model of the daily load curve:

{\overset{c}{true}}_{g}^{m} false(t false) = {\overset{p}{true}}_{g}^{m} false(w, t false) - {\overset{f}{true}}^{o} false(t false) .

Hence, estimation of the parameter vector w means solving the following optimization problem:

w^{⋆} = \arg \min_{w} true \sum_{t = 1}^{24} false| {\overset{p}{true}}_{g}^{m} false(w, t false) - {\overset{f}{true}}^{o} false(t false) - false({\overset{p}{true}}_{g}^{m} false(w, t - 1 false) - {\overset{f}{true}}^{o} false(t - 1 false) false) false| .

For this minimization, we need to employ an algorithm for constrained optimization since we want to restrict the parameter vector to regions where the resulting daily load curve is nonnegative.…”

Section: Load Forecast Modelsmentioning

confidence: 99%

“…The second procedure is to look for the parameter vector that results in the smoothest daily load curve. This is a version of one of the methods from the paper by Sossan et al where the smoothness is measured using a differentiated time series. Here, we differentiate the daily load curve:

true \sum_{t = 1}^{24} false| {\overset{c}{true}}_{g}^{m} false(t false) - {\overset{c}{true}}_{g}^{m} false(t - 1 false) false| .

…”

Section: Load Forecast Modelsmentioning

confidence: 99%

“…It is a first step towards the goal to predict net load at different scales in a regional electricity network, eg, in terms of total production and production per entry point or per secondary substation. Previous work has tackled the problem with the general lack of BTM measurements of PV production either by trying to disaggregate the net load into gross production and gross consumption or by upscaling model output from a few representative sites where BTM measurements are available (eg, previous studies). Not much work has been done on the former approach as confirmed by others such as van der Meer et al and Wang et al Finding representative sites as suggested by the latter approach is difficult in Sweden where there are no requirements on measuring the gross production, and the number of sites owned and supervised by the regional grid operators are few.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modelling and forecasting PV production in the absence of behind‐the‐meter measurements

Landelius

Andersson

Abrahamsson³

2019

Progress in Photovoltaics

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This paper deals with hourly day‐ahead prediction of the net electricity load at nine photovoltaic installations in a Swedish regional electricity network. The objective of the study was to develop, test, and evaluate a set of methods to predict the contribution of PV power to the grid without knowing the production and consumption “behind‐the‐meter.” An indirect and a direct approach for prediction of the net load were evaluated. For the indirect approach, a model of the gross production was first estimated based on the open‐source software PVLIB. The model was then used to predict the net load given a forecast of the gross consumption. Since we lacked a model of the latter, we used a “perfect forecast,” in terms of measured gross consumption, to estimate the performance of this approach. In the direct approach, a model of the net load was estimated using either linear regression or an artificial neural network. Here, the model was used for prediction of the net load without any information about the gross consumption. Both approaches rely on information from a numerical weather prediction model together with net load measurements from the previous day. Forecasts using the indirect approach with perfect information about the gross consumption resulted in a normalized (with installed nominal power) RMSEn of 11%. The direct approach with the artificial neural network also resulted in an RMSEn of 11%, even though it did not have any information from behind the meter. Linear regression had an RMSEn of 12%.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

true \sum_{t = 1}^{24} false| {\overset{c}{true}}_{g}^{m} false(t false) - {\overset{c}{true}}_{g}^{m} false(t - 1 false) false| .

We then want to minimize the sum in Equation given our model of the daily load curve:

{\overset{c}{true}}_{g}^{m} false(t false) = {\overset{p}{true}}_{g}^{m} false(w, t false) - {\overset{f}{true}}^{o} false(t false) .

Hence, estimation of the parameter vector w means solving the following optimization problem:

w^{⋆} = \arg \min_{w} true \sum_{t = 1}^{24} false| {\overset{p}{true}}_{g}^{m} false(w, t false) - {\overset{f}{true}}^{o} false(t false) - false({\overset{p}{true}}_{g}^{m} false(w, t - 1 false) - {\overset{f}{true}}^{o} false(t - 1 false) false) false| .

For this minimization, we need to employ an algorithm for constrained optimization since we want to restrict the parameter vector to regions where the resulting daily load curve is nonnegative.…”

Section: Load Forecast Modelsmentioning

confidence: 99%

true \sum_{t = 1}^{24} false| {\overset{c}{true}}_{g}^{m} false(t false) - {\overset{c}{true}}_{g}^{m} false(t - 1 false) false| .

…”

Section: Load Forecast Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling and forecasting PV production in the absence of behind‐the‐meter measurements

Landelius

Andersson

Abrahamsson³

2019

Progress in Photovoltaics

View full text Add to dashboard Cite

show abstract

Model-Free Detection of Distributed Solar Generation in Distribution Grids Based on Minimal Exogenous Information

Petrusev,

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Debusschere

et al. 2024

Lecture Notes in Electrical Engineering

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A hybrid data-driven online solar energy disaggregation system from the grid supply point

Zhang

Kuenzel

Guo

et al. 2022

Complex Intell. Syst.

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The integration of small-scale Photovoltaics (PV) systems (such as rooftop PVs) decreases the visibility of power systems, since the real demand load is masked. Most rooftop systems are behind the metre and cannot be measured by household smart meters. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by rooftop PV systems and the ground truth demand load from net measurements. A 1D Convolutional Neural Network (CNN) Bidirectional Long Short-Term Memory (BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (Advanced Metering Infrastructure (AMI) data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method achieves good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify that the proposed system has good robustness and can be applied to other feeders.

show abstract

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Cited by 70 publications

References 25 publications

Modelling and forecasting PV production in the absence of behind‐the‐meter measurements

Modelling and forecasting PV production in the absence of behind‐the‐meter measurements

Model-Free Detection of Distributed Solar Generation in Distribution Grids Based on Minimal Exogenous Information

A hybrid data-driven online solar energy disaggregation system from the grid supply point

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