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@article{146996,
        author = {Rashmi Mishra  and Amit Gupta },
        title = {SHORT TERM LOAD FORECASTING USING ADAPTIVE NEURO - FUZZY INFERENCE SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {3},
        pages = {142-147},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146996},
        abstract = {In today world, load forecasting is very important for the power system operation. The meaning of Load forecasting is to predicting the future load with the help of historical load data available. For the Power system management scheduling and dispatching operations load forecasting plays an important role and it also concerns the prediction of energy demand in different time spans. Data for the present work includes the Load data and the weather data that affects the load forecasting. These data are obtained from reliable and genuine source such as the  Madhya Pradesh Purva Kshetra Vidyut Vitran Company Ltd. (MPPKVVCL) Jabalpur of the winter season i.e. from recent month January 2018(every 15min) and weather data from www.worldweatheronline.com. From the analysis carried out on the ANFIS based Model mean absolute percentage error for a typical Wednesday was found to be 2.23%},
        keywords = {Short Term Load forecasting, ANFIS.},
        month = {},
        }