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Nowadays, the methodology aiming to achieve carbon neutrality and net zero energy on a large scale is known. Despite this, few specialists are mastering this technology globally. What new scenarios. applied at the neighbourhood scale. generate a significant reduction in the rate of CO2 emissions and energy demand? In addition, a lack of massive, regular, and consistent data on carbon emissions and energy consumption has made it significantly difficult to understand the origins of climate change at the building and neighbourhood scales. This work has, as its main goal, the assessment of different strategies that facilitate reduction in the concentration of CO2 and lower energy demands at the district level. The life cycle assessment of a new district has been carried out over 100 years during the four stages of the life cycle of the neighbourhood (construction, operation, demolition and end of life). The results showed that up to 93% of greenhouse gas (GHG) was produced during the operational stage. The energy demand due to transport and waste management represented 60% of the total energy demand of the district during the operational stage. There is still a possibility to maintain air temperature growth around 1.5 °C in the next decade by means of the following: Global warming + 100% of heavy renovation of all buildings + 100% of electric car − renewable energy. This strategy would facilitate a reduction of over 92% of the CO2 produced at the district level. [ABSTRACT FROM AUTHOR]

Hybrid PV/T (Photovoltaic/thermal) systems are a robust alternative to the limitations of PV panels and thermal collectors in energy production. Improving their performance is therefore necessary. This article presents a new configuration of hybrid photovoltaic and thermal (PV/T) air/water-CuO/MgO/TiO2 collector which is optimized by seeking a better combination of design parameters which maximize the exergy performance. An energy and exergy analysis of the system is carried out and a multi-objective optimization with the genetic algorithm is developed using Matlab. These to determine the values of these nine (9) design parameters such as collector tilt angle, collector area, center to center distance between tubes, inside and outside diameter of tubes and thicknesses of the cells, of the glass layer, of the insulation, of the absorber. The other parameters are taken constant and a set of optimal solutions are sought for 1000 generations. The comparison of the different numerical results from this article with the design parameters from previous work shows good agreement. It is observed that the total exergy efficiency is maximum between the values of 23.41-36.6% and the majority of the design parameters studied in general are minimum. The discussions deduced that the minimization of the components of the bi-fluid PV/T hybrid collector could reduce the losses inside the latter by favoring the cooling as well as the displacement of the heat at the back of the PV cell. This work shows that the mixture of water and ternary nanoparticles with a flow rate of 0.00575 kg/s cooled more than air at 0.008 kg/s, but the system performed better when the two fluids operated simultaneously at 0.0035 kg/s. Due to the requirement of optimal efficiency and minimum costs, the hybridization of nanoparticles presents better thermo-economic performances. [ABSTRACT FROM AUTHOR]

Acidification and eutrophication are two environmental impacts that have a significant effect on air pollution and human health. The quantitative analysis of these two impacts remains hitherto unknown at the scale of new neighborhoods. The main objective of this study is to evaluate, analyze and compare the acidification and eutrophication potentials of one neighborhood initially located in Belgium. For making this comparison, this neighborhood was built in 149 other countries by applying four parameters such as building materials, energy mix, occupants' mobility and local climate. The environmental costs of acidification and eutrophication coming from this neighborhood were assessed over 100 years. This research, extended to the scale of several nations, will enable new researchers, and especially policy-makers, to measure the effectiveness of sustainable neighborhoods. Eutrophication and acidification potentials were assessed under different phases (construction, use, renovation and demolition), with Pleiades software (version 4.19.1.0). The effects of the energy mix were the most significant among the other parameters. The results show that 72%, and 65% of acidification and eutrophication potentials are produced during the operational phase of the neighborhood. In the case of sustainable neighborhoods, the acidification potential is 22.1% higher in the 10 top low-income countries than in the 10 top high-income countries. At the neighborhood scale, the main eutrophication potential component is water (34.2%), while the main source of acidification potential is electricity production (45.1%). [ABSTRACT FROM AUTHOR]

The aim of this research is to review the status and current trends about energy resources production potential and new energy policies in Madagascar to suggest possible solutions to help the government in its sustainable policy development. The results of this investigation showed that, nowadays in Madagascar, more than 80% of natural potential is remaining not exploited. What is more, in order to satisfy the demand of energy, the country will need to use up to 12,000 MW of its hydroelectric potential and 10% of its solar potential that can be easily reached with a good energy policy. In this sense, an operating of only 20% of solar, hydraulic and wind power resources, in Madagascar, can cover the energy needs of all five Indian Ocean countries on several years and, in particular, solar potential resulted significant in coastal zone of Madagascar. • In Madagascar, only 12% of the residents have the electricity, this rate should increase upto 30% in 2030. • An increase to 3 °C of the air temperature could generated an energy demand up to 40%. • The biomass produced 93% of the consumed energy in Madagascar. [ABSTRACT FROM AUTHOR]

Featured Application: An analysis of the optimal ventilation level in naturally ventilated classrooms under COVID-19 pandemic requirements. The present paper aims to show a mathematical understanding of the effect of ventilation rate over building energy consumption. Moreover, as a case study to show this methodology, a proposal was analyzed of modifying the teaching period to reach a maximum increase of air changes in school buildings, to allow adherence to the COVID-19 pandemic requirements in the Galicia region, with lower energy consumption. In this sense, to analyze the energetic implication of this proposal, the building construction was defined, modeled in accordance with the ISO Standard 13790 and implemented in accordance with the Monte Carlo method. Results showed the probability of energy consumption as a Weibull model. Furthermore, a map of different Weibull models in accordance with different ventilation rates was developed. The constants of the Weibull models allow to identify normal distributions of the probability density functions of energy consumption, especially the ones with lower energy consumption. As a consequence, these constants are a better parameter to identify the optimal ventilation rate for each season in search of a healthy indoor ambience, which is of interest for a future design guide. Finally, the main results showed a reduction of energy consumption at a higher ventilation rate in the summer season. As a consequence, the necessity of modifying teachings periods, as an adequate procedure to prevent more COVID infections, is concluded. [ABSTRACT FROM AUTHOR]

Researchers have used passive strategies, such as the implementation of thermal insulation and the use of phase change materials (PCM), in several studies, but some problems have not yet been solved. It is the case of showing the real effect of external shading combined with thermal insulation and phase change materials to improve the thermal performance and energy efficiency of office buildings in tropical coastal areas. Another pending problem to be solved is to define the impact produced by passive strategies on the performance of workers in office buildings in coastal zones. It is with a view to answering all these questions that this study was envisaged with the main objective of evaluating, analyzing, comparing, and discussing the effect of thermal insulation and phase change materials on thermal comfort and energy demand in coastal areas of hot and humid tropical climates located in the island of Madagascar. In this sense, hourly climate data for the past 30 years have served as the basis for assessing environmental conditions of future climate. It was found that the PCMs have a more significant effect on the coastal zone of hot climates than humid tropical climates. The results of the statistical analyses showed that the application of passive strategies stabilizes indoor air temperatures to between 23 °C and 28 °C in the offices, which is the recommended comfort range in these regions. In the coastal regions of Madagascar, up to 30% of cooling energy is expected to be reduced by combining the introduction of thermal insulation and PCM materials. [ABSTRACT FROM AUTHOR]

The prediction of global solar radiation in a region is of great importance as it provides investors and politicians with more detailed knowledge about the solar resource of that region, which can be very beneficial for large-scale solar energy development. In this sense, the main objective of this study is to predict the daily global solar radiation data of 27 cities (Brussels, Paris, Lisbon, Madrid...), located in 27 countries, which have mostly different solar radiation distributions in Europe. In this research, six different machine-learning algorithms (Linear model (LM), Decision Tree (DT), Support Vector Machine (SVM), Deep Learning (DL), Random Forest (RF) and Gradient Boosted Trees (GBT)) are used. In the training of these algorithms, daily air temperature(Ta), wind speed(Va), relative humidity(RH) and solar radiation of these cities are used. The data is supplied from the Meteonorm tool and cover the last years grouped in two periods (1960–1990; 2000–2019). To decide on the success of these algorithms, four different statistical metrics (Average Relative Error (ARE), Average absolute Error (AAE), Root Mean Squared Error (RMSE), and R2 (R-Squared)) are discussed in the study. In addition, the forecasting of air temperature and global solar radiation of these cities in 2050 and 2100 were made using three of the most recent Intergovernmental Panel on Climate Change (IPCC) scenarios (RCP2.6; RCP 4.5, and RCP 8.5). The results show that ARE, R,2 and RMSE values of all algorithms are ranging from 0.114 to 6.321, from 0.382 to 0.985, from 0.145 to 2.126 MJ/m2, respectively. By analysing all the algorithms, it is noticed that the Decision tree exhibited the worst result in terms of R,2 and RMSE metrics. Among the six prediction algorithms, the DL was recognized as the only algorithm that exceeded the t-critical value (The t-critical value is the cutoff between retaining or rejecting the null hypothesis). Globally, all the six machine learning algorithms used in this research can be applied to predict the daily global solar radiation data with good accuracy. Despite this, the SVM model is the best model among all the six models used. It is followed by the DL, LM, GB, RF and DT, respectively. • Decision tree exhibited the worst result in terms of R2, and RMSE metrics, • Deep learning was recognized as the only algorithm that exceeded the t-critic value, • Algorithms used in all the cities have shown very successful results [ABSTRACT FROM AUTHOR]

Thermal comfort is an important factor in a classroom. This paper presents the results of a field study campaign in three classrooms of the University of Yaounde I in Cameroon. An adaptive approach was used. The questionnaire was formulated according to the adaptive and statistical model prescribed by UNI EN ISO 10551. Various values of air temperature, wind speed, and relative humidity were measured at the same time students were filling the questionnaire. The study was conducted during three seasons (little rainy season, little dry season, and great rainy season). Several correlations were established according to the model of Fanger (PMV, PPD) and that of Wray. The temperatures of neutrality for the three seasons were found between 23.4 °C and 25.7 °C. The vote of thermal acceptability showed that during the three seasons, 66.02 % of the students found their environment acceptable, while the votes of thermal preference were: "want warmer" during the little rainy season, "want no change" during the little dry season and "want cooler" during the great rainy season. [ABSTRACT FROM AUTHOR]