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

By M. Bouzerdoum, Adel Mellit, A. M. Pavan. 2013

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e9ee5461e7eacde69d8e20bcb816b93df472600d

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques (2019, unknown): “These methods include different types of Artificial Neural Networks (ANN) such as Multilayer Perceptron (MLP) [22–24], Support Vector Machine (SVM) [25], Markov chain, Fourier Series, regression methods, persistence methods (mainly used as a benchmark to test other models), etc.”
Accurate photovoltaic power forecasting models using deep LSTM-RNN (2017, methodology): “The authors of [6] combined two traditional methods, auto-regressive integrated moving average and support vector machines to forecast PV power.”
Review of photovoltaic power forecasting (2016, methodology): “Bouzerdoum et al. (2013) combined SARIMA with SVM in their hour ahead predictions, whereas Ramsami and Oree (2015) used ARIMA with ANN, Vaz et al. (2016) applied an ensemble technique of ANN and Non-linear ARX (NARX) and Hossain et al. (2013) developed a methodology to select the best combination of”
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, background): “The proposed SVR model was able to predict with the same accuracy as statistically enhanced predictions of the PV simulation model [25].”
Photovoltaic and solar power forecasting for smart grid energy management (2015, methodology): “The seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines method (SVMs) are combined for hourly solar power prediction of a small-scale GCPV plant with 20 kWp [52].”