Distribution System Losses Evaluation by ANN Approach

Leal, Adriano Galindo; Jardini, J.A.; Magrini, L.C.; Ahn, Se Un; Schmidt, Hernán Prieto; Casolari, Ronaldo Pedro

doi:10.1109/psce.2006.296161

Cited by 4 publications

(2 citation statements)

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“…(Glauner et al 2017) provides an overview over AI techniques for detecting non‐technical loss. (Kang et al 2006) and (Leal et al 2006) use artificial neural networks to perform analysis and evaluation of losses in distribution systems. (Agüero 2012) reviews technologies, methodologies and operational approaches aimed at improving the efficiency of power distribution systems.…”

Section: Related Workmentioning

confidence: 99%

Day‐ahead forecasting of losses in the distribution network

et al. 2021

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Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead nomination of the expected grid loss. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle.

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Section: Related Workmentioning

confidence: 99%

Day‐ahead forecasting of losses in the distribution network

et al. 2021

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“…(Glauner et al 2017) provides an overview over AI techniques for detecting non-technical loss. (Kang et al 2006) and (Leal et al 2006) use artificial neural networks to perform analysis and evaluation of losses in distribution systems. (Agüero 2012) review technologies, methodologies and operational approaches aimed at improving the efficiency of power distribution systems.…”

Section: Related Workmentioning

confidence: 99%

Day-Ahead Forecasting of Losses in the Distribution Network

Dalal

Mølnå

Herrem

et al. 2020

AAAI

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We present a commercially deployed machine learning system that automates the day-ahead nomination of the expected grid loss for a Norwegian utility company. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduces the MAE with 41% from 3.68 MW to 2.17 MW per hour from mid July to mid October. It is robust and reduces manual work.

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