In this work, a new hybrid model for short-term power forecasting of a grid-connected photovoltaic plant is introduced. The new model combines two well-known methods: the seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines method (SVMs). An experimental database of the power produced by a small-scale 20 kWp GCPV plant is used to develop and verify the effectiveness of the proposed model in short-term forecasting. Hourly forecasts of the power produced by the plant were carried out for a few days showing a quite good accuracy. A comparative study has also been introduced showing that the developed hybrid model performs better than both the SARIMA and the SVM model.

A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant

MASSI PAVAN, ALESSANDRO
2013-01-01

Abstract

In this work, a new hybrid model for short-term power forecasting of a grid-connected photovoltaic plant is introduced. The new model combines two well-known methods: the seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines method (SVMs). An experimental database of the power produced by a small-scale 20 kWp GCPV plant is used to develop and verify the effectiveness of the proposed model in short-term forecasting. Hourly forecasts of the power produced by the plant were carried out for a few days showing a quite good accuracy. A comparative study has also been introduced showing that the developed hybrid model performs better than both the SARIMA and the SVM model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2822527
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