Data on photovoltaic power forecasting models for Mediterranean climate

M Malvoni; M G De Giorgi; P M Congedo

doi:10.1016/j.dib.2016.04.063

Data on photovoltaic power forecasting models for Mediterranean climate

Data Brief. 2016 May 4:7:1639-42. doi: 10.1016/j.dib.2016.04.063. eCollection 2016 Jun.

Authors

M Malvoni¹, M G De Giorgi¹, P M Congedo¹

Affiliation

¹ Department of Engineering for Innovation, University of Salento, via per Arnesano I-73100, Italy.

Abstract

The weather data have a relevant impact on the photovoltaic (PV) power forecast, furthermore the PV power prediction methods need the historical data as input. The data presented in this article concern measured values of ambient temperature, module temperature, solar radiation in a Mediterranean climate. Hourly samples of the PV output power of 960kWP system located in Southern Italy were supplied for more 500 days. The data sets, given in , were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015) [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD) outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016) [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in .

Keywords: Forecasting errors; GLSSVM; Group Method of Data Handling (GMDH); Least Square Support Vector Machine (LS-SVM); Multi-step ahead forecast; Photovoltaic Power Forecast.