A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm

A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm

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Solar irradiance fluctuates within a very short period of time that creates a lot 3 piece horse wall art of hindrances to estimate the injection of output power into the grid.During the operation of solar power plant, short-term PV power forecasting supports load dispatching, planning, and also the regulatory actions.But this short term PV power forecasting is a very complicated problem in order to solve it.This paper represents short-term PV power forecasting by constructing a 3-stage approach which is formed by combining empirical mode decomposition (EMD) technique, sine cosine algorithm (SCA), and extreme learning machine (ELM) technique.At the initial phase of the proposed technique, a de-noised series is obtained by adopting a signal filtering strategy based on EMD decomposition technique.

Next three different time interval data series are opted for the training and forecasting stage.The selected sets of data are quarterly, half-hourly and hourly PV data observations.The simulation results signify that the recommended technique performs in an out-standing manner than the conventional ones while addressing short term PV power forecasting.Keywords: PV system, Single hidden layer feedforward neural network click here (SLFN), Extreme learning machine (ELM), Empirical mode decomposition (EMD), Sine cosine algorithm (SCA), Optimization.