Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO₂ and PM10 particles, with promising results and an achieved $R^2$ of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

This effort to make the power grid more intelligent is tightly coupled with the deployment of advanced metering infrastructure (AMI) as an integral part of the future vision of smart grid. The goal of AMI is to provide necessary information for the consumers and utilities to accurately monitor and manage energy consumption and pricing in real time. Immediate benefits are enhanced transparency and efficiency of energy usage and the improvement of customer services. Although the road map toward successful AMI deployment is clearly defined, many challenges and issues are to be solved regarding the design of AMI. In this paper, a multi-agent AMI based on the fog-computing approach is presented. Architecture follows structural decomposition of AMI functionalities encapsulated in a form of local and area-specific service components that reside at the different tiers of hierarchically organized AMI deployment. Fog computing concepts provide the framework to effectively solve the problems of creating refined and scalable solutions capable of meeting the requirements of the AMI as a part of future smart grid. On the other hand, agent-based design enables concurrent execution of AMI operations across the distributed system architecture, in the same time improving performance of its execution and preserving the scalability of the AMI solution. The real-time performance of the proposed AMI solution, related to the periodic and on-demand acquisition of metering data from the connected electricity meters, was successfully verified during one year of pilot project operation. The detailed analysis of the performance of AMI operation regarding data collection, communication and data availability across the deployed pilot AMI, covering several transformer station areas with diverse grid topologies, is also presented.

The proposed architecture of the Advanced Metering Infrastructure (AMI), presented in this paper, introduces new hierarchical layers in the Smart Metering System. This implies distributed implementation of the traditional data-concentrator functions as basic functionalities and advanced Smart Metering functions in the form of Transformer Station Data Concentrator (TSC) and Local Metering Concentrator (LMC) system components. This concept provides the support for real-time applications, services and algorithms. The introduction of LMC layer in the form of independent embedded system component enables on one side the real time two way command-response communication mechanism with the higher layers of AMI, while on the other side performs communication with locally connected electrical meters over standard serial communication interface. The proposed concept improves the communication dynamics with the traditional and smart electrical meters, while providing support for more complex AMI network topology. The introduced LMC layer enables real-time load profiling, energy diagnostic, and simple integration with energy management system. Interoperability, scalability and modularity are in the line with general smart grid concepts.

The methodology for the detection of under-performance operation of neighbouring photovoltaic systems is presented. The methodology compares the estimations of the total horizontal solar irradiance obtained from a group of photovoltaic systems. The estimations can be obtained from the direct measurement of the solar irradiance or indirectly by utilizing the group of methods for the processing of electrical signals at the output of the photovoltaic system. Inter-system comparison is performed solely during the system operation under the detected clear-sky conditions, as this atmospheric condition in a predictable manner affects the incident solar irradiance at the arbitrary surface area. We assume that the measurement or the estimation of the total horizontal solar irradiance in close proximity area under the particular clear-sky condition is expected to produce comparable results. In order to provide the comparable estimations, the methodology introduces the system efficiency factor to address various influences on the operation of the photovoltaic system, which are not easily addressable in analytical form. These influences are related to different physical, installation, and operational properties of the photovoltaic system, including aging, manufacturing tolerances, and other technology-related properties. The methods for the empirical assessment of the system efficiency factor as well as for the correlation-related processing are also given as a part of the proposed methodology. The utilization of the methodology for the detection of the under-performance operation is verified through the presented case study.

Recent studies have focused on accuracy as the key state of charge (SoC) estimation algorithms’ performance metrics, whereas just a few of them compare algorithms in terms of energy efficiency. Such a comparison is important when selecting an algorithm that should be implemented on a resource-constrained, low-power embedded system. In this paper, recursive model-based SoC estimation algorithms, such as the extended Kalman filter, have been identified as well-suited solutions for implementation on an embedded platform, providing a good compromise between estimation accuracy and computational complexity that is correlated to energy consumption. Assuming that a decrease in the estimation rate will result in a decrease in both accuracy and energy consumption of the estimator, a theoretical analysis has been conducted to establish how these two metrics depend on the estimation rate. To verify results obtained in theory, two extended Kalman filter-based SoC estimation algorithms of different complexities have been implemented and compared in terms of accuracy, quantified by root mean square error (RMSE), and energy consumption. The obtained results confirm that for a selected type of recursive model-based SoC estimation algorithm, it is possible to achieve an optimal algorithm estimation rate in the sense of satisfactory accuracy and acceptable energy consumption. The analysis and results presented in this paper establish a foundation for a future development of energy-efficient algorithms for SoC estimation in applications where the energy consumption of the estimation process is comparable to the energy consumption of the complete system.