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Førsteamanuensis Institutt for elektroteknologi nils.j.johannesen@uit.no

Nils Jakob Johannesen



  • Oleksandra Ishchenko, Nils Jakob Johannesen :
    Leveraging the Delphi Method for Demand Response Aggregation in the Energy Market
    IEEE (Institute of Electrical and Electronics Engineers) 2024 DOI
  • Alf-Kristian Fladby, Nils Jakob Johannesen :
    Semi-systematic review of the feasibility for predictive analysis of railway power supply and electrical trains
    IEEE (Institute of Electrical and Electronics Engineers) 2024 DOI
  • Svein Olav Glesaaen Nyberg, Nils Jakob Johannesen :
    Time Series Modelling for Risk Analysis in Frequency Containment Reserves Market
    IEEE (Institute of Electrical and Electronics Engineers) 2024 DOI
  • Alf-Kristian Fladby, Nils Jakob Johannesen :
    Comparison of Dimensional Reduction Methods for Predictive Analysis of Railway System Data
    IEEE (Institute of Electrical and Electronics Engineers) 2024 DOI
  • Amrit Chapagain, Emil Ghieh Melfald, Nils Jakob Johannesen, Bishal Silwald :
    Analysis of Single Phase to Ground Fault in Synchronous Generator Using Ansys Co-Simulation
    IEEE (Institute of Electrical and Electronics Engineers) 2024 DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Vertical Approach Anomaly Detection Using Local Outlier Factor
    Springer 2023 ARKIV / FULLTEKST / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Andreas Dolven Jacobsen :
    Correlation Analysis of Potential Solar Photovoltaic Power Plant Integration at Wind Farm with Grid Connection Limits
    IEEE Press 2023 DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Load prediction of rural area Nordic holiday resorts for microgrid development
    Academic Press 2022 FULLTEKST / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Recurrent neural networks for electrical load forecasting to use in demand response
    IET Digital Library 2022 FULLTEKST / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor
    IEEE conference proceedings 2022 ARKIV / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe :
    Application of Regression Tools for Load Prediction in Distributed Network for Flexible Analysis
    CRC Press 2021 DATA / SAMMENDRAG / FULLTEKST / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Comparing recurrent neural networks using principal component analysis for electrical load predictions
    IEEE (Institute of Electrical and Electronics Engineers) 2021 FULLTEKST / ARKIV / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area
    Journal of Cleaner Production 2020 ARKIV / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Load Demand Analysis of Nordic Rural Area with Holiday Resorts for Network Capacity Planning
    IEEE (Institute of Electrical and Electronics Engineers) 2019 DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Relative evaluation of regression tools for urban area electrical energy demand forecasting
    Journal of Cleaner Production 2019 ARKIV / DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Deregulated Electric Energy Price Forecasting in NordPool Market using Regression Techniques
    IEEE conference proceedings 2019 DOI
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Comparison of Regression Tools for Regional Electric Load Forecasting
    IEEE (Institute of Electrical and Electronics Engineers) 2018 FULLTEKST
  • Nils Jakob Johannesen :
    Load Forecasting analysis in Power Distribution Networks and usefulness for Electricity Market
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Machine Learning Applications for Load Predictions in Electrical Energy Network
    2022 ARKIV
  • Nils Jakob Johannesen, Mohan Lal Kolhe, Morten Goodwin :
    Urban Area Load Forecasting using k-Nearest Neighbour Regression Tool Considering Weather Parameters
    2018

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