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Spatio-temporal resolution of irradiance samples in machine learning approaches for irradiance forecasting

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Eschenbach, Annette and Yepes, Guillermo and Tenllado van der Reijden, Christian and Gómez Pérez, José Ignacio and Piñuel Moreno, Luis and Zarzalejo, Luiis F. and Wilbert, Stefan (2020) Spatio-temporal resolution of irradiance samples in machine learning approaches for irradiance forecasting. IEEE access, 8 . pp. 51518-51531. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2020.2980775


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Abstract

Improving short term solar irradiance forecasting is crucial to increase the market share of the solar energy production. This paper analyzes the impact of using spatially distributed irradiance sensors as inputs to four machine learning algorithms: ARX, NN, RRF and RT. We used data from two different sensor networks for our experiments, the NREL dataset that includes data from 17 sensors that cover a 1 km^2 area and the InfoRiego dataset which includes data from 50 sensors that cover an area of 94 Km^2. Several studies have been published that use these datasets individually, to the author knowledge this is the flrst work that evaluates the influence of the spatially distributed data across a range from 0.5 to 17 sensors per km^2. We show that all of algorithms evaluated are able to take advantage of the data from the surroundings, from the very short forecast horizons of 10s up to a few hours, and that the wind direction and intensity plays an important role in the optimal distribution of the network and its density. We show that these machine learning methods are more effective on the short horizons when data is obtained from a dense enough network to capture the cloud movements in the prediction interval, and that in those cases complex non-linear models give better results. On the other hand, if only a sparse network is available, the simpler linear models give better results. The skills obtained with the models under test range from 13% to 70%, depending on the sensor network density, time resolution and lead time.


Item Type:Article
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©2020 IEEE
This work was supported in part by the Spanish Ministry of Science and Innovation under Grant RTI2018-093684-B-I00, and in part by the Regional Government of Madrid under Grant S2018/TCS-4423.

Uncontrolled Keywords:Solar-radiation; Cloud motion; Network; Systems; Models; Impact; Machine learning; Forecasting; Spatial resolution; Solar irradiance; Global horizontal irradiance
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:60408
Deposited On:11 May 2020 10:45
Last Modified:13 May 2020 08:32

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