Lasya Priya Kotu

• Lien, Synne Krekling; Kotu, Lasya Priya; Sørensen, Åse Lekang & Rajasekharan, Jayaprakash (2026). Discover, detect, disaggregate: A data-driven approach to extract electric vehicle charging information from household smart meter data in cold climate. Smart Energy. 22, p. 1–20. doi: 10.1016/j.segy.2026.100241. Full text in Research Archive Show summary

  Electric vehicle (EV) charging at home is transforming the electric load profiles of residential buildings, creating both challenges and opportunities for the grid. Using data-driven methods to extract EV charging information from smart meters can provide valuable insights for grid management. However, prior research has mainly focused on developing methods tailored to residential buildings without electric heating, where EV charging dominates peak electricity use, and seasonal variation in the load is limited. This article presents a case study that addresses this gap by combining smart meter data from 296 single-family houses in Norway with EV charging data from 82 residential chargers, creating semi-synthetic load profiles for households with and without EV charging and electric heating. A three-step supervised method with domain-informed features is proposed for: (1) estimating EV charger capacity, (2) classifying charging events (on/off), and (3) estimating electricity use for EV charging from hourly household smart meter data. Results show that Step 1 estimates charger capacity with 93% accuracy, Step 2 detects charging events with 81% recall/84% F1-score and Step 3 estimates charging electricity use with an average R2 of 0.80–0.86/NMAE of 0.005–0.006. Applying a physics-informed filter to Step 3 can further improve estimates of electricity use for charging by eliminating physically impossible solutions, improve real-world applicability and significantly reduce MAE. Each step can be used independently for applications such as demand-side flexibility estimations and user nudging, making the framework flexible and useful for a range of stakeholders in the energy sector.

• Kotu, Lasya Priya; Rørvik, Ella-Lovise Hammervold; Rajasekharan, Jayaprakash & Lindberg, Karen Byskov (2025). Estimation of supermarket flexibility through control of cooling machine with and without pre-cooling. Sustainable Energy, Grids and Networks. 44. doi: 10.1016/j.segan.2025.102019. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Borhani, Reza; Katsaggelos, Aggelos K.; Ørn, Stein & Woie, Leik [Show all 7 contributors for this article] (2015). Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients. Artificial Intelligence in Medicine. ISSN 0933-3657. 64(3), p. 205–215. doi: 10.1016/j.artmed.2015.06.001. Full text in Research Archive
• Eftestøl, Trygve; Måløy, Frode; Engan, Kjersti; Kotu, Lasya Priya; Woie, Leik & Ørn, Stein (2014). A Texture-based Probability Mapping for Localisation of Clinically Important Cardiac Segments in the Myocardium in Cardiac Magnetic Resonance Images from Myocardial Infarction Patients. Proceedings of IEEE International Conference on Image Processing. ISSN 1522-4880. p. 2227–2231. doi: 10.1109/ICIP.2014.7025451. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Skretting, Karl; Måløy, Frode; Ørn, Stein & Woie, Leik [Show all 7 contributors for this article] (2013). Probability mapping of scarred myocardium using texture and intensity features in CMR images. Biomedical engineering online. 12(91). doi: 10.1186/1475-925X-12-91. Full text in Research Archive Show summary

  The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium. In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function. In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image. The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardium

• Kotu, Lasya Priya; Engan, Kjersti; Skretting, Karl; Ørn, Stein; Woie, Leik & Eftestøl, Trygve (2013). Segmentation of Scarred Myocardium in Cardiac Magnetic Resonance Images. ISRN Biomedical Imaging. doi: 10.1155/2013/504594. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Woie, Leik; Ørn, Stein & Katsaggelos, Aggelos K. (2012). LOCAL BINARY PATTERNS USED ON CARDIAC MRI TO CLASSIFY HIGH AND LOW RISK PATIENT GROUPS. European Signal Processing Conference. ISSN 2076-1465. 20, p. 2586–2590. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Ørn, Stein & Woie, Leik (2011). TEXTURE CLASSIFICATION OF SCARRED AND NON-SCARRED MYOCARDIUM IN CARDIAC MRI USING LEARNED DICTIONARIES. Proceedings of IEEE International Conference on Image Processing. ISSN 1522-4880. 18, p. 65–68. doi: 10.1109/ICIP.2011.6116638. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Ørn, Stein & Woie, Leik (2011). Segmentation of Scarred and Non-Scarred Myocardium in LG Enhanced CMR Images using Intensity-Based Textural Analysis. IEEE Engineering in Medicine and Biology Society. Conference Proceedings. ISSN 1557-170X. p. 5698–5701. doi: 10.1109/IEMBS.2011.6091379. Full text in Research Archive

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• Eftestøl, Trygve; Måløy, Frode; Engan, Kjersti; Kotu, Lasya Priya; Woie, Leik & Ørn, Stein (2014). A Texture-based Probability Mapping for Localisation of Clinically Important Cardiac Segments in the Myocardium in Cardiac Magnetic Resonance Images from Myocardial Infarction Patients. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Woie, Leik; Ørn, Stein & Katsaggelos, Aggelos (2012). LOCAL BINARY PATTERNS USED ON CARDIAC MRI TO CLASSIFY HIGH AND LOW RISK PATIENT GROUPS. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Ørn, Stein & Woie, Leik (2011). TEXTURE CLASSIFICATION OF SCARRED AND NON-SCARRED MYOCARDIUM IN CARDIAC MRI USING LEARNED DICTIONARIES. Full text in Research Archive
• Kotu, Lasya Priya; Engan, Kjersti; Eftestøl, Trygve; Ørn, Stein & Woie, Leik (2011). Segmentation of Scarred and Non-Scarred Myocardium in LG Enhanced CMR Images using Intensity-Based Textural Analysis. Full text in Research Archive

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