Sensitive parameter analysis for solar irradiance short-term forecasting: application to LoRa-based monitoring technology
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AuthorBueso Sánchez, María del Carmen; Paredes Parra, José Miguel; Mateo Aroca, Antonio; Molina García, Ángel
Knowledge AreaTecnologías del Medio Ambiente
SponsorsThis research was funded by the Fondo Europeo de Desarrollo Regional/Ministerio de Ciencia e Innovación–Agencia Estatal de Investigación (FEDER/MICINN-AEI), project RTI2018–099139–B–C21.
Realizado en/conUniversidad Politécnica de Cartagena
Bibliographic CitationBueso, M.C.; Paredes-Parra, J.M.; Mateo-Aroca, A.; Molina-García, A. Sensitive Parameter Analysis for Solar Irradiance Short-Term Forecasting: Application to LoRa-Based Monitoring Technology. Sensors 2022, 22, 1499. https:// doi.org/10.3390/s22041499
Sensitive parameter analysis
Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case ...
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