Emerging Technologies for Renewable Energy Systems

Spatti, Danilo Hernane; Liboni, Luisa Helena Bartocci

doi:10.5772/65207

Cited by 1 publication

(2 citation statements)

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“…These kinds of systems alleviate the grid's difficult task of delivering energy where technical difficulties exist. When referring to the grid and RES, there are two main directions for generating wind and photovoltaic energy [7]. The first consists of encouraging and deploying large wind and photovoltaic power plants that can be smoothly integrated into the grid.…”

Section: Literature Reviewmentioning

confidence: 99%

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PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

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et al. 2018

Symmetry

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Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much” energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process.

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Section: Literature Reviewmentioning

confidence: 99%

“…In some bibliographic sources, small RES are known as micro-grids [7] that combine energy capabilities, storage, advanced technologies that use sensors and actuators, IT&C, big data, complex analytics, etc.…”

Section: Introductionmentioning

confidence: 99%