Statistical Learning for Short-Term Photovoltaic Power Predictions

Autor:	Elke Lorenz, Oliver Kramer, Björn Wolff
Rok vydání:	2016
Předmět:	Mean squared error Computer science business.industry Photovoltaic system Big data Machine learning computer.software_genre Numerical weather prediction k-nearest neighbors algorithm Support vector machine Feature (machine learning) Range (statistics) Artificial intelligence business computer
Zdroj:	Computational Sustainability ISBN: 9783319318561 Computational Sustainability
DOI:	10.1007/978-3-319-31858-5_3
Popis:	A reliable prediction of photovoltaic (PV) power plays an important part as basis for operation and management strategies for a efficient and economical integration into the power grid. Due to changing weather conditions, e.g., clouds and fog, a precise forecast in a few hour range can be a difficult task. The growing IT infrastructure allows a fine screening of PV power. On the basis of big data sets of PV measurements, we apply methods from statistical learning for one- to six-hour ahead predictions based on data with hourly resolution. In this work, we employ nearest neighbor regression and support vector regression for PV power predictions based on measurements and numerical weather predictions. We put an emphasis on the analysis of feature combinations based on these two data sources. After optimizing the settings and comparing the employed statistical learning models, we build a hybrid predictor that uses forecasts of both employed models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0512e16fca4ad54081d84045e0de6c3a https://doi.org/10.1007/978-3-319-31858-5_3 Zobrazit plný text záznamu