@inproceedings{scholars4902,
           title = {A comparative analysis of PSO and LM based NN short term load forecast with exogenous variables for smart power generation},
             doi = {10.1109/ICIAS.2014.6869451},
         address = {Kuala Lumpur},
            year = {2014},
         journal = {2014 5th International Conference on Intelligent and Advanced Systems: Technological Convergence for Sustainable Future, ICIAS 2014 - Proceedings},
            note = {cited By 12; Conference of 2014 5th International Conference on Intelligent and Advanced Systems, ICIAS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:107042},
       publisher = {IEEE Computer Society},
            isbn = {9781479946549},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906351328&doi=10.1109\%2fICIAS.2014.6869451&partnerID=40&md5=df8b07e5355792156a77790d1e95b077},
        abstract = {Accurate short term load forecasting is essential for reliable operation and several decision making processes of the power system. However, forecast model selection, network training issues and improper input selection of forecast model may significantly decrease the prediction accuracy of forecast model. As a result operational cost and reliability of system affected dramatically. In this paper, particle swarm optimization (PSO) based neural network (NN) forecast model is presented and compared with Levenberg Marquardt (LM) based NN forecast model for 168 hours ahead load forecast case studies. The impact of day type, day of the week, time of day and holidays on load demand are also analyzed. The mean absolute percentage errors (MAPE) and regression analysis of NN training are used to measure the forecast model performance. Moreover, PSONN based forecast model produces higher forecast accuracy for all test case studies with confidence interval of 99. In this research ISO-New England grid load and respective weather data is used to train and test the forecast model. {\^A}{\copyright} 2014 IEEE.},
          author = {Raza, M. Q. and Baharudin, Z. and Nallagownden, P. and Badar-Ul-Islam, {}},
        keywords = {Particle swarm optimization (PSO); Regression analysis; Statistical tests, Comparative analysis; Confidence interval; Decision making process; Levenberg-Marquardt; Mean absolute percentage error; Neural network (nn); Short term load forecast; Short term load forecasting, Forecasting}
}