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The connection between Earth’s global temperature and carbon dioxide (CO2) emissions is one of the highest challenges in climate change science since there is some controversy about the real impact of CO2 emissions on the increase of global temperature. This work contributes to the existing literature by analyzing the relationship between CO2 emissions and the Earth’s global temperature for 61 years, providing a recent review of the emerging literature as well. Through a statistical approach based on maximum entropy, this study supports the results of other techniques that identify a positive impact of CO2 in the increase of the Earth’s global temperature. Given the well-known difficulties in the measurement of global temperature and CO2 emissions with high precision, this statistical approach is particularly appealing around climate change science, as it allows the replication of the original time series with the subsequent construction of confidence intervals for the model parameters. To prevent future risks, besides the present urgent decrease of greenhouse gas emissions, it is necessary to stop using the planet and nature as if resources were infinite.

Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents ∼40% of total energy consumption. Major components of this are lighting, electrical equipment, heating, and central cooling systems. Solid evidence demonstrates that individual occupants’ behaviors impact these energy consumption components. In this work, we propose the methodology of using virtual choreographies to identify and prioritize behavior-change interventions for office users based on the potential impact of specific behaviors on energy consumption. We studied the energy-related office behaviors of individuals by combining three sources of data: direct observations, electricity meters, and computer logs. Data show that there are behaviors with significant consumption impact but with little potential for behavioral change, while other behaviors have substantial potential for lowering energy consumption via behavioral change.

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

This paper presents three new and improved non-isolated topologies of quadratic boost converters (QBC). Reduced voltage stress across switching devices and high voltage gain with single switch operation are the main advantages of the proposed topologies. These topologies utilize voltage multiplier cells (VMC) made of switched capacitors and switched inductors to increase the converter’s voltage gain. The analysis in continuous conduction mode is discussed in detail. The proposed converter’s voltage gain is higher than the conventional quadratic boost converter, and other recently introduced boost converters. The proposed topologies utilize only a single switch and have continuous input current and low voltage stress across switch, capacitors, and diodes, which leads to the selection of low voltage rating components. The converter’s non-ideal voltage gain is also determined by considering the parasitic capacitance and ON state resistances of switch and diodes. The efficiency analysis incorporating switching and conduction losses of the switching and passive elements is done using PLECS software (Plexim, Zurich, Switzerland). The hardware prototype of the proposed converters is developed and tested for verification.

An accurate evaluation of the thermal transmittance ( U -value) of building envelope elements is fundamental for a reliable assessment of their thermal behaviour and energy efficiency. Simplified analytical methods to estimate the U -value of building elements could be very useful to designers. However, the analytical methods applied to lightweight steel framed (LSF) elements have some specific features, being more challenging to use and to obtain a reliable accurate U -value with. In this work, the main analytical methods available in the literature were identified, the calculation procedures were reviewed and their accuracy was evaluated and compared. With this goal, six analytical methods were used to estimate the U -values of 80 different LSF wall models. The obtained analytical U -values were compared with those provided by numerical simulations, which were used as reference U -values. The numerical simulations were performed using a 2D steady-state finite element method (FEM)-based software, THERM. The reliability of these numerical models was ensured by comparison with benchmark values and by an experimental validation. All the evaluated analytical methods showed a quite good accuracy performance, the worst accuracy being found in cold frame walls. The best and worst precisions were found in the Modified Zone Method and in the Gorgolewski Method 2, respectively. Very surprisingly, the ISO 6946 Combined Method showed a better average precision than other two methods, which were specifically developed for LSF elements.

Second-generation bioethanol production’s main bottleneck is the need for a costly and technically difficult pretreatment due to the recalcitrance of lignocellulosic biomass (LCB). Chemical pulping can be considered as a LCB pretreatment since it removes lignin and targets hemicelluloses to some extent. Chemical pulps could be used to produce ethanol. The present study aimed to investigate the batch ethanol production from unbleached Kraft pulp of Eucalyptus globulus by separate hydrolysis and fermentation (SHF). Enzymatic hydrolysis of the pulp resulted in a glucose yield of 96.1 ± 3.6% and a xylose yield of 94.0 ± 7.1%. In an Erlenmeyer flask, fermentation of the hydrolysate using Saccharomyces cerevisiae showed better results than Scheffersomyces stipitis. At both the Erlenmeyer flask and bioreactor scale, co-cultures of S. cerevisiae and S. stipitis did not show significant improvements in the fermentation performance. The best result was provided by S. cerevisiae alone in a bioreactor, which fermented the Kraft pulp hydrolysate with an ethanol yield of 0.433 g·g−1 and a volumetric ethanol productivity of 0.733 g·L−1·h−1, and a maximum ethanol concentration of 19.24 g·L−1 was attained. Bioethanol production using the SHF of unbleached Kraft pulp of E. globulus provides a high yield and productivity.

This work addresses the effect of the total solar reflectance (TSR) value of paints applied in residential buildings upon their thermal performance. A semi-detached residential building was modeled in the ESP-r software, and taken as the basis for parametric studies which assessed the effects of variations in (i) the TSR values; (ii) the thermal characteristics of the building envelope; (iii) the location/climate; and: (iv) the way how the indoor temperature is controlled. The parametric studies were used to find optimal TSR values for each combination of Location + Building envelope characteristics (mainly the existence of thermal insulation). It was concluded that paints having a carefully chosen TSR value lead to better indoor thermal temperatures if the buildings have no mechanical heating or cooling, or to energy savings of up to 32% if they do.