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.

Electricity generation from renewable energy sources (RES) has a common feature, that is, it is fluctuating, available in certain amounts and only for some periods of time. Consuming this electricity when it is available should be a primary goal to enhance operation of the RES-powered generating units which are particularly operating in microgrids. Heavily influenced by weather parameters, RES-powered systems can benefit from implementation of sensors and fuzzy logic systems to dynamically adapt electric loads to the volatility of RES. This study attempts to answer the following question: How to efficiently integrate RES to power systems by means of sustainable energy solutions that involve sensors, fuzzy logic, and categorization of loads? A Smart Adaptive Switching Module (SASM) architecture, which efficiently uses electricity generation of local available RES by gradually switching electric appliances based on weather sensors, power forecast, storage system constraints and other parameters, is proposed. It is demonstrated that, without SASM, the RES generation is supposed to be curtailed in some cases, e.g., when batteries are fully charged, even though the weather conditions are favourable. In such cases, fuzzy rules of SASM securely mitigate curtailment of RES generation by supplying high power non-traditional storage appliances. A numerical case study is performed to demonstrate effectiveness of the proposed SASM architecture for a RES system located in Hulubești (Dâmbovița), Romania.

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