• Energy Research
• 7. Clean energy close
• PT close
• University of Lisbon close

High energy consumption is a challenging issue that an ever increasing number of mobile applications face today.However, energy consumption is being tested in an ad hoc way, despite being an important non-functional requirement of an application.Such limitation becomes particularly disconcerting during software testing: on the one hand, developers do not really know how to measure energy; on the other hand, there is no knowledge as to what is the energy overhead imposed by the testing framework.In this paper, as we evaluate eight popular mobile UI automation frameworks, we have discovered that there are automation frameworks that increase energy consumption up to roughly 2200%.While limited in the interactions one can do, Espresso is the most energy efficient framework.However, depending on the needs of the tester, Appium, Monkeyrunner, or UIAutomator are good alternatives.In practice, results show that deciding which is the most suitable framework is vital. We provide a decision tree to help developers make an educated decision on which framework suits best their testing needs.

Abstract High power pseudocapacitors are extremely relevant to answer specific needs in the current energy transition arena and to implement an efficient renewable energy society. However, literature shows that are still open gaps concerning improvement of their energy density at high power, conversion efficiency, cost and cycle life. Electrodes based on active transition metal compounds, and in particular metal sulphides, evidence high potential to meet these objectives. This work discusses the dependence on the synthesis route of the charge storage mechanism of manganese sulphide-based materials and relates the pseudocapacitive response of these electrodes with their polycrystalline nature. Results reveal that a manganese oxy-sulphide mixture can achieve a high specific capacitance of 231 F.g−1 at 0.5 A/g in a 0.65 V active window. These values represent a 31.5 % increase compared to pure rambergite, γ-MnS, and 436 % compared to pure hausmannite Mn3O4 prepared under the same conditions. Moreover, the results show that manganese oxy-sulphide electrodes are characterized by good charge retention (73%), and superior long term capacity retention (above 86%) after 5000 cycles, evidencing potential for high power energy storage applications.

This research studied the impacts of the environmental Kuznets curve and the determinants of economic growth for Visegrad countries from 1990 to 2018. This paper reflects on the effects of renewable and non-renewable energy, urban population, foreign direct investment, economic growth, and carbon dioxide emissions. According to our results, the panel of unit root tests showed that the variables under study are integrated into the first differences. Considering the empirical results for the environmental Kuznets curve, we observe that economic growth is positively correlated with pollution emissions; nevertheless, the squared income per capita is negatively impacted by carbon dioxide emissions. Energy consumption increases carbon emissions, and foreign direct investment confirms the pollution halo hypothesis. Therefore, the econometric results showed that renewable energy consumption promotes regional growth. Consequently, urban population and foreign direct investment positively correlate with economic growth.

Quantitative reliability, availability, and maintainability (RAM) assessments are of fundamental importance at the early design stages, as well as planning and operation of marine renewable energy systems. This paper presents an RAM framework adaptable to different offshore renewable technologies, conceived to provide support in the choice of the device components and subsequent planning of the O&M strategies. A case study, characterizing a pilot farm of oscillating water column (OWC) wave energy converters (WECs), is illustrated together with the method used to obtain reliable estimate of its key performance indicators (KPIs). Based on a fixed feed-in-tariff for the project, economic figures are estimated, showing a direct relationship with the availability of the farm and the cost of maintenance interventions. Consequently, the probability distributions of the most relevant output variables are presented, and the mutual correlations between them investigated using principal components analysis (PCA) with the aim of discovering the relationships influencing the performance of the offshore farm. In this way, the contributions of the individual factors on the profitability of the project are quantified, and generic guidelines to support the decision-making process are derived. It is shown how this type of analysis provides important insights not only to ocean energy farm operators after the deployment of the devices, but also to device developers at the early design stage of wave energy concepts.

Non‐intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large‐scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross‐datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.This article is categorized under: Application Areas > Science and Technology Application Areas > Data Mining Software Tools Technologies > Computational Intelligence Technologies > Machine Learning

Hybrid capacitors have been developed to bridge the gap between batteries and ultracapacitors. These devices combine a capacitive electrode and a battery-like material to achieve high energy-density high power-density devices with good cycling stability. In the quest of improved electrochemical responses, several hybrid devices have been proposed. However, they are usually limited to bench-scale prototypes that would likely face severe challenges during a scaling up process. The present case study reports the production of a hybrid prototype consisting of commercial activated carbon and nickel-cobalt hydroxide, obtained by chemical co-precipitation, separated by means of polyolefin-based paper. Developed to power a 12 W LED light, these materials were assembled and characterized in a coin-cell configuration and stacked to increase device voltage. All the processes have been adapted and constrained to scalable conditions to ensure reliable production of a pre-commercial device. Important challenges and limitations of this process, from geometrical constraints to increased resistance, are reported alongside their impact and optimization on the final performance, stability, and metrics of the assembled prototype.

Thermal management is a particularly difficult challenge to the miniaturization of electronic components because it requires high performance cooling systems capable of removing large heat loads at fast rates in order to keep the operating temperature low and controlled. To meet this challenge, the Intermittent Spray Cooling (ISC) concept has been suggested as a promising technology which uses a proper match between the frequency and duration of consecutive injection cycles to control heat transfer. This concept also depends on: the atomization strategy; a homogeneous dispersion of droplets impinging on the hot surface; and the quantitative control of the liquid deposited, avoiding excessive secondary atomization or pre-impingement-evaporation. In this work, the use of liquid atomization by multiple jets impact, also referred as multijet atomization, is the subject of a thermal-fluid assessment using heat transfer correlations previously derived for intermittent sprays. Simultaneous measurements of droplet size and velocity are provided as input for the correlations and the analysis explores the influence of the number of impinging jets on the heat removal pattern and magnitude. Emphasis is put on the promising applicability of multijet atomization for promoting an intelligent use of energy in the thermal management of electronic devices.

The increasing integration of wind energy in power systems can be responsible for the occurrence of over-generation, especially during the off-peak periods. This paper presents a dedicated methodology to identify and quantify the occurrence of this over-generation and to evaluate some of the solutions that can be adopted to mitigate this problem. The methodology is applied to the Portuguese power system, in which the wind energy is expected to represent more than 25% of the installed capacity in a near future. The results show that the pumped-hydro units will not provide enough energy storage capacity and, therefore, wind curtailments are expected to occur in the Portuguese system. Additional energy storage devices can be implemented to offset the wind energy curtailments. However, the investment analysis performed show that they are not economically viable, due to the present high capital costs involved.