Autors: Tsakoumis A.C., Vladov, S. S., Mladenov, V. M.
Title: Daily Load Forecasting Based on Previous Day Load
Keywords: daily load forecast; nearest neighbor; self-organizing map;

Abstract: In this paper we consider daily load forecast problem and explore the idea that similar conditions to those at the forecasting moment have normally existed. before. If the load conditions change relatively slowly, then the yesterday's load curve can be used as an indicator of the load conditions of the present day; so it is assumed the robustness of the model. To test the idea of the robustness two models are considered. The first model uses the self-organizing map (SOM) to form network weights. The map is trained on the load data of ten months. The forecast is received by connecting load data of the previous day to a weight vector that contains a forecast for the target day. The second model that we suggest here is a considerable simplification of the first one and is based on the idea of the nearest neighbor.

References

    Issue

    Proceedings of the 6th Seminar on Neural Network Applications in Electrical Engineering, NEUREL 2002, pp. 83-86, 2002, Serbia, IEEE, DOI: 10.1109/NEUREL.2002.1057973

    Цитирания (Citation/s):
    1. M. Ulagammai, "Short Term Load Forecasting Using ANN and WNN," 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bengaluru, India, 2023, pp. 612-616, doi: 10.1109/IITCEE57236.2023.10091081. (Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science
    2. Al-Qahtani F. H., Crone S. F., Multivariate k-nearest neighbour regression for time series data — A novel algorithm for forecasting UK electricity demand, The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, pp. 1-8, doi: 10.1109/IJCNN.2013.6706742. (Scopus) - 2013 - в издания, индексирани в Scopus или Web of Science
    3. Valgaev O., Kupzog F., Schmeck H., Low-voltage power demand forecasting using K-nearest neighbors approach, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016, pp. 1019-1024, doi: 10.1109/ISGT-Asia.2016.7796525. (Scopus) - 2016 - в издания, индексирани в Scopus или Web of Science
    4. Guirelli C., Previsão da carga de curto prazo de áreas elétricas através de técnicas de inteligência artificial. Doctoral Thesis, Escola Politécnica, University of São Paulo, São Paulo. doi:10.11606/T.3.2006.tde-19042007-142653., 2006, Retrieved 2021-05-25, from www.teses.usp.br (Google Scholar) - 2006 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    5. Valgaev O., Kupzog F., Building power demand forecasting using K-nearest neighbors model - initial approach, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2016, pp. 1055-1060, doi: 10.1109/APPEEC.2016.7779700. (Scopus) - 2016 - в издания, индексирани в Scopus или Web of Science
    6. Gopakumar S., Tran T., Luo W., Phung D., Venkatesh S., Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data JMIR Med Inform 2016; 4(3):e25 doi: 10.2196/medinform.5650 (Scopus) - 2016 - в издания, индексирани в Scopus или Web of Science
    7. Gopakumar, S., Machine learning in healthcare : an investigation into model stability, PhD thesis, School of Information Technology, Deakin University, 2017, (Google Scholar) - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    8. Krommydakis P., Karampelas K., Xilogiannopoulos l., Ekonomou, Functional Requirements for a Collaborative Platform for Power Transmission System Operators: The Case of South Eastern Europe, Proceedings of the European Computing Conference, ISBN: 978-960-474-297-4, 2011. (Scopus) - 2011 - в издания, индексирани в Scopus или Web of Science
    9. Emami, P., Sahu, A. and Graf, P., 2023. BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting. arXiv preprint arXiv:2307.00142. https://doi.org/10.48550/arXiv.2307.00142 (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science