Abstract
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.
Highlights
Due to the constantly increasing need for energy in the last decade and climate change issues, Renewable energy systems (RES) are vital
In order to describe how it is used in regression, we initially describe the Support Vector Machine (SVM) for classification; basically, the SVM regression is derived from the SVM classification
RES monitoring has tended to evolve in the direction of monitoring any element that generates energy
Summary
Due to the constantly increasing need for energy in the last decade and climate change issues, RES are vital. Any electric power generation system needs to balance the generating and the loading components in order to be reliable and to operate safely at quality standards in terms of voltage, frequency and stability regulation [1]. This is available for smaller scale systems such as prosumers, communities, aggregators or micro-grids that deal with local generation and consumption. RES, in comparison with other energy resources, do not fail unexpectedly, but they are characterized by uncertainty and variability To compensate for these issues, other power system reserves are involved to generate additional power or reduce power when needed
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