In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

Large-scale photovoltaic (PV) electricity production plants rely on reliable operation and maintenance (O&M) systems, often operated by means of supervisory control and data acquisition (SCADA) platforms aimed at limiting, as much as possible, the intrinsic volatility of this energy resource. The current trend is to develop SCADAs that achieve the finest possible control of the system components to efficiently and effectively cope with possible energy delivery problems. In this study, we investigated an innovative design of an IoT-based SCADA specifically tailored for large PV systems in which data transmission overheads are reduced by adopting lightweight protocols, and reliable data storage is achieved by means of hybrid solutions that allow the storage of historical data, enabling accurate performance analysis and predictive maintenance protocols. The proposed solution relies on an architecture where independent functional microservices handle specific tasks, ensuring scalability and fault tolerance. The technical approaches for IoT-SCADA connectivity are herein described in detail, comparing different possible technical choices. The proposed IoT-based SCADA is based on edge computing for latency reduction and to enhance real-time decision making, enabling scalability, and centralized management while leveraging cloud services. The resulting hybrid solutions that combine edge and cloud resources offer a balance between responsiveness and scalability. Finally, in the study, a blockchain solution was taken into account to certify energy data, ensuring traceability, security, and reliability in commercial transactions.

Low-cost air quality monitoring systems (LCAQMSs) and machine learning (ML) techniques are enabling a new paradigm in air quality monitoring networks. Nevertheless, compliance with data quality objective (DQO) is still an open point. The assessment of various calibration models proposed in the literature has ever neglected the concept drift, i.e., the differences in data distributions associated with input and target variables of the streaming data coming from dynamic nonstationary environments. The influence of the concept drift is investigated on the maintenance of the (calibrated) low-cost instrumentation. The data from mid-term co-location campaigns are first used to train a multiple linear regression (MLR) as calibration model. Then, an original methodology based on the two-sample Kolmogorov–Smirnov test (TSKS test) is proposed for automatically detecting the presence of the concept drift. The time evolution of the relative expanded uncertainty (REU) is considered as well, to highlight the negative influence of the concept shift on the metrological performance of the LCAQMS, proving the need for the estimation of a new calibration model during the maintenance of the instrumentation in order to match the DQOs. A quantitative analysis is carried out on the distances among the training and test distributions about the input and outputs of the calibration model by correlating them with the time evolution of uncertainty. The scheme of an add-on block based on the proposed approach is designed for the continuous monitoring of the metrological performance exhibited by the calibration model. This management of the concept drift is expected to allow the long-awaited achievement of DQOs.

Scalable and effective calibration is a fundamental requirement for low-cost air quality (AQ) monitoring systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensor-specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in AQ monitoring networks. Concept and sensor drift often force the calibration process to be frequently repeated. These issues lead to unbearable calibration costs, which denies their massive deployment when accuracy is a concern. In this work, we propose a zero transfer samples, global calibration methodology as a technological enabler for Internet of Things (IoT) AQ multisensory devices, which relies on low-cost particulate matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning (ML) concepts and can be universally applied to all units of the same type. A multiseason test campaign has shown that, when applied to different sensors, this methodology’s performances match those of state-of-the-art methodology, which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded onboard of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long-range data transfer needs.