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The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in different phases of the algorithm. Electrical variables acquired by the sensors and the variables calculated are stored in CDL using Google Sheets (GS) to monitor and analyse the installation by means of a TLBO algorithm implemented in PFCMS, that optimizes the compensation power factor of installation and determining which capacitors are connected in real time. Moreover, the optimization of the power factor in facilities means economic and energy savings, as well as the improvement of the quality of the operation of the installation.

Smart meter roll-out in photovoltaic (PV) household-prosumers provides easy access to granular meter measurements, which enables advanced energy services. The design of these services is based on the training and validation of models. However, this requires temporal high-resolution data for generation/load profiles collected in real-world household facilities. For this purpose, this research developed and successfully calibrated a new prototype for an accurate low-cost On-time Single-Phase Power Smart Meter (OSPPSM), which corresponded to these profiles. This OSPPSM is based on the Arduino open-source electronic platform. Not only can it locally store information, but can also wirelessly send these data to cloud storage in real-time. This paper describes the hardware and software design and its implementation. The experimental results are presented and discussed. The OSPPSM demonstrated that it was capable of in situ real-time processing. Moreover, the OSPPSM was able to meet all of the calibration standard tests in terms of accuracy class 1 (measurement error ≤1%) included in the International Electrotechnical Commission (IEC) standards for smart meters. In addition, the evaluation of the uncertainty of electrical variables is provided within the context of the law of propagation of uncertainty. The approximate cost of the prototype was 60 € from eBay stores.

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