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The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.

Biodiesel is a fuel generally consisting of a mixture of fatty acid methyl esters (FAMEs) which is used in alternative or in combination with petroleum diesel for its environmental benefits. Biodiesel is conveniently manufactured from vegetable oils by transesterification of triglycerides with methanol. However, the process brings about the concurrent formation of glycerol, which may become an oversupplied chemical if biodiesel production keeps growing. A novel biodiesel-like material (abbreviated as DMC-BioD) was developed by reacting soybean oil with dimethyl carbonate (DMC), which avoided the co-production of glycerol. The main difference between DMC-BioD and biodiesel produced from vegetable oil and methanol (MeOH-biodiesel) was the presence of fatty acid glycerol carbonate monoesters (FAGCs) in addition to FAMEs. In the following study, details regarding synthesis and composition of DMC-BioD are provided along with physical properties relevant for its use as a fuel. In addition, the production of potential pyrogenic contaminants was investigated by analytical pyrolysis and compared with those from MeOH-biodiesel, and the model compounds tristearin, triolein, trilinolein and oleic acid glycerol carbonate ester (OAGC). The presence of FAGCs influenced both fuel and flow properties, while the distribution of main pyrogenic compounds, including polycyclic aromatic hydrocarbons (PAHs), was little affected. Benefits and drawbacks of DMC as a candidate transmethylating reagent for producing biofuel from renewable resources and alternative co-products (glycerol carbonate and glycerol dicarbonate) are discussed.

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

AbstractSolar fuels offer a promising approach to provide sustainable fuels by harnessing sunlight1,2. Following a decade of advancement, Cu2O photocathodes are capable of delivering a performance comparable to that of photoelectrodes with established photovoltaic materials3–5. However, considerable bulk charge carrier recombination that is poorly understood still limits further advances in performance6. Here we demonstrate performance of Cu2O photocathodes beyond the state-of-the-art by exploiting a new conceptual understanding of carrier recombination and transport in single-crystal Cu2O thin films. Using ambient liquid-phase epitaxy, we present a new method to grow single-crystal Cu2O samples with three crystal orientations. Broadband femtosecond transient reflection spectroscopy measurements were used to quantify anisotropic optoelectronic properties, through which the carrier mobility along the [111] direction was found to be an order of magnitude higher than those along other orientations. Driven by these findings, we developed a polycrystalline Cu2O photocathode with an extraordinarily pure (111) orientation and (111) terminating facets using a simple and low-cost method, which delivers 7 mA cm−2 current density (more than 70% improvement compared to that of state-of-the-art electrodeposited devices) at 0.5 V versus a reversible hydrogen electrode under air mass 1.5 G illumination, and stable operation over at least 120 h.

Pyroelectric energy harvesting has the ability to transform wasted heat into useful energy and as such has a potential to create “green” energy from freely available sources such as ambient temperature changes, and contribute to the fight against climate change. Current pyroelectrics applications are limited to low-power electronics, portable systems or tasks needing only very low range of power (μW–mW). Developing further this highly promising technology should ultimately lead to the creation of more powerful, autonomous and self-powered electronic devices that could one day use to recycle currently “lost” thermal energy to power electronic devices in both domestic and industrial settings. To the best of our knowledge, hexagonal phase ZnS (wurtzite ZnS) has not been studied as a possible energy harvesting pyroelectric material despite w-ZnS being isostructural to the well-exploited and widely praised hexagonal ZnO [1]. In addition, the Tc temperature (1020 ˚C for bulk material) is high enough for ZnS that it has the ability to operate at higher temperature that are good match with the working temperature of power plants and automobiles, and hence w-ZnS ceramics should have a potential to be used in pyroelectric harvesters of waste heat coming from those activities [2]. Here we report on the pyroelectric output registered for a wurtzite phase ZnS ceramic fabricated as part of our project. To probe the pyroelectric output for a w-ZnS ceramic a simple device (a “pyro-cell”) was created by evaporating gold electrods on both sides of a ceramic sample, which was mounted on a Cu-metalized rectangular insulating base (vetronite) using silver paint. This device is stable from room temperature up to approximately 180°C. Two different heating and cooling testing set-ups were established: Set-up n°1 used an industrial scale laser, providing a source with fast temperature change, and Set-up n°2 had a standard lab hot plate heating element, providing a much slower temperature change. The characterization required an accurate measurement of the currents of the order of 10-9 A. In addition, using the Pyroelectric Test System (PK‐SPIV17T, State College, PA, USA) with a Keithley 6517 B Picoammeter, we were able to measure the pyroelectric coefficient and monitor its change at different frequencies as a function of temperature from 20 °C up to 150° C, with a heating rate of between 2 and 10 °C/min. Figure 1: Pyroelectric current measurements on an ZnS ceramic sample, using testing set-up n°1. The horizontal axis shows the time (seconds). References [1] Y. Yang, W. Guo, K.C. Pradel, G. Zhu, Y. Zhou, Y. Zhang, Y. Hu, L. Lin, Z. Lin Wang, “Pyroelectric Nanogenerators for Harvesting Thermoelectric Energy”, Nano Lett, 12 (6), 2012, 2833–2838 [2] L.A. Chavez, F.O. Zayas Jimenez, B.R. Wilburn, L.C. Delfin, H. Kim, N. Love, Y. Lin, “Characterization of Thermal Energy Harvesting Using Pyroelectric Ceramics at Elevated Temperatures”, Energy Harvesting and Systems, 5(1-2), 2018, 3–10 This project was also partially supported by the Piano triennale di realizzazione 2019-2021 della ricerca di sistema elettrico nazionale – Progetto 1.3 Materiali di frontiera per usi energetici (C.U.P. code: I34I19005780001).