ISSN (Online) : 2456 - 0774

Email : ijasret@gmail.com

ISSN (Online) 2456 - 0774


AN INTELLIGENT SHORT TERM LOAD FORECASTING FORSOLAR PLANT


Abstract

Abstract: Preciseestimating of solar energy is a key issue for an expressive incorporation ofthe solar power plants into the lattice. It is know that the solar dynamism isvery asymmetrical so the result output of Solar Photovoltaic Systems (SPV)unfocussed by the distinctive nature like high temperature, moisture, windswiftness, solar irradiance and supplementary climatological facts. It’scompulsory to prediction of astrophysical energy is most important tominimalize hesitation in power connect from solar photovoltaic system.  The day-ahead Short Term Load Forecasting(STLF) is an obligatory daily task for power report of unit obligation,commercial allocation of group, maintenance plans. This work offerings a solutionorganization with the proportional investigation with Fuzzy Logic and k NearestNeighbor algorithm for short term consignment forecasting. From theexperimentation and analysis, load prediction is predicted for 4 days ofreadings taken from the given data set of BHEL in the year 2018-19, where 5thday load estimating is predicted with the above-mentioned algorithms. From theassessment Fuzzy logic approach provides better optimal result for weatherpenetrating data and historical load data for forecasting the consignment.

Keywords —FuzzyLogic, k Nearest Neighbor Algorithm, Optimal Solution, Renewable Dynamism,Solar Photovoltaic System, Short Term Load Forecasting (STLF), Solar Energy.


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