2013/07/22 by Cyril Voyant, Voyant, Cyril, Christophe Paoli +5
Computer Science · Energy · Engineering · Physics and Astronomy · #Atmospheric and Oceanic Physics (physics.ao-ph) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Physical sciences #Neural and Evolutionary Computing (cs.NE) #Photovoltaic System Optimization Techniques #Solar Radiation and Photovoltaics #cs.NE #physics.ao-ph
paper · pdf · doi:10.48550/arxiv.1307.6179
Renewable and Sustainable Energy Reviews (2013)
arxiv created 2013/07/22 · openalex publication_date 2013/07/22 · arxiv updated 2013/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Considering the grid manager's point of view, needs in terms of prediction of intermittent energy like the photovoltaic resource can be distinguished according to the considered horizon: following days (d+1, d+2 and d+3), next day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.). Through this work, we have identified methodologies using time series models for the prediction horizon of global radiation and photovoltaic power. What we present here is a comparison of different predictors developed and tested to propose a hierarchy. For horizons d+1 and h+1, without advanced ad hoc time series pre-processing (stationarity) we find it is not easy to differentiate between autoregressive moving average (ARMA) and multilayer perceptron (MLP). However we observed that using exogenous variables improves significantly the results for MLP . We have shown that the MLP were more adapted for horizons h+24 and m+5. In summary, our results are complementary and improve the existing prediction techniques with innovative tools: stationarity, numerical weather prediction combination, MLP and ARMA hybridization, multivariate analysis, time index, etc.