Sorry, you need to enable JavaScript to visit this website.

Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting

TitleDeterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2017
AuthorsPierro, M., Bucci F., De Felice Matteo, Maggioni E., Perotto A., Spada F., Moser D., and Cornaro C.
JournalJournal of Solar Energy Engineering, Transactions of the ASME
Volume139
ISSN01996231
KeywordsArtificial intelligence, Data-driven approach, Deterministic approach, Forecasting, Learning systems, Machine learning techniques, Numerical weather prediction, Performance indices, Photovoltaic cells, Photovoltaic effects, Physics-based models, PV power generation, Solar energy, Solar power generation, Stochastic models, Stochastic systems, Weather forecasting, Weather research and forecasting models
Abstract

Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for dayahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively. Copyright © 2017 by ASME.

Notes

cited By 0

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85001090559&doi=10.1115%2f1.4034823&partnerID=40&md5=3009609fcaa33fbc5b7cb7f01c993fb0
DOI10.1115/1.4034823
Citation KeyPierro2017