Elektrik Güç Dağıtımında Akıllı Sayaç Verileri için Anomali Tespiti ve Tahminleme

Year 2022, Volume: 3 Issue: 2, 72 - 85, 28.02.2023

Serhat Yarat , Zeynep Orman

https://doi.org/10.54047/bibted.1224628

Abstract

Nüfus yoğunluğu ve ekonomik büyümenin etkisiyle enerji talebi hızla artmaktadır. Bu talep karşısında enerji ve elektrik şebekeleri daha fazla zorlukla karşı karşıya gelmektedir. Enerji tüketiminin sıkı bir şekilde izlenmesi ve kontrol altında tutulması önem arz etmektedir. Enerji dağılımını düşündüğümüzde akıllı sayaçlar bu enerjinin kontrolünde kilometre taşı rolü oynamaktadır. Enerji tüketim ölçümlerinin yapıldığı sayaçlarda meydana gelebilecek herhangi bir elektrik kesintisi, bir hata veya yanlış ölçüm, dağıtım şirketlerinden son kullanıcılara kadar birçok tarafı etkilemektedir. Enerji sektöründeki bu tür anomalilerin tespiti için gerçekleştirilen veri analitiği çalışmaları ve büyük veri teknolojileri, sensörlerden ve sayaçlardan toplanan zaman serisi verilerini gerçek zamanlı veya toplu olarak analiz ederek verimliliği ve tasarrufu arttırmayı amaçlayan net ve eyleme geçirilebilir çıktılar üretmede önemli rol oynamaktadır. Bu çalışmada, akıllı elektrik sayaçları ile ölçülen aylık tüketim değerlerine dayalı olarak enerji tüketimindeki olası anomalilerin tespit edilmesi ve farklı makine öğrenmesi yöntemleri kullanılarak gelecek tüketiminin tahmin edilmesi amaçlanmıştır. Sonuç olarak; enerji sektöründe genel aydınlatma sayaçları üzerinde yapılan uygulamalarda İzolasyon Ormanı (Isolation Forest-IF), Yerel Aykırı Değer Faktörü (Local Outlier Factor-LOF) ve FbProphet algoritmalarının anomali tespitinde olası uç anomali noktalarını başarılı bir şekilde tespit edebildiği ve FbProphet algoritmasının XGBoost algoritmasına göre sayaç verileri üzerinde zaman serileri ile yapılan tahminlemelerde ortalama olarak daha iyi sonuç verdiği tespit edilmiştir.

Keywords

Anomali Tespiti, Tahminleme, Zaman Serileri, Elektrik Güç Dağıtımı, Akıllı Sayaç, Anomaly Detection, Electrical Power Distribution

Anomaly Detection and Prediction for Smart Meter Data in Electrical Power Distribution

Abstract

With rapidly increasing energy demand as a result of increasing population density and economic growth, energy and electricity grids are now facing more challenges. In the face of this demand, energy consumption should be kept under strict monitoring and control. When we consider energy distribution, smart meters play a milestone role in the control of this energy. Any error or an incorrect measurement in the meters in which energy consumption measurements are made affect many stakeholders, from the distribution company to the end users. Identifying such anomalies in the energy sector, data analytics studies, and big data technologies play an important role to produce solutions. In this study, it is aimed to detect possible anomalies in energy consumption and forecasting consumption using different machine learning methods, based on monthly consumption values measured by smart meters. As a result; the experiments and observations on general lighting meters showed that Isolation Forest (IF), Local Outlier Factor (LOF), and FbProphet were successful in detecting the potential extreme anomaly points. Additionally, it has been found that when using the FbProphet and XGBoost algorithms to forecast data from time series, FbProphet outperforms XGBoost.

Keywords

Anomaly Detection, Forecasting, Time Series, Electrical Power Distribution, Smart Meters

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