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The energy transition requires optimal knowledge of the thermal behaviour of different passive strategies. This paper explores the impact of 28 variables representing 4 shading devices, 5 external wall compositions (Uw), 3 window types (Uw), 4 window-to-wall ratios (WWR), 4 types of climates represented by 4 cities, and 8 orientations. Applying the Latin hypercube sampling method, a campaign of 300 dynamic thermal simulations is performed to assess the impact of the variables selected using the weighted generalised linear regression method for the energy demand for air conditioning, the energy demand for lighting, and the environmental impact expressed in kg of CO2. The model of energy demand for cooling (R² = 0.951) shows that the weather data is the variable that most explains energy demand, followed by the glazing ratio, the thermal characteristics of external walls, and shading devices. The model explaining the energy demand for lighting (R² = 0.945) shows that the WWR and shading devices, the weather data, and the orientation, influence the energy demand for lighting. Finally, the model explaining the embodied carbon footprint (kg of CO2) (R² = 0.989) shows that external walls and window type are the main influencing factors. Finally, the best combination for balancing the cooling-lighting-embodied carbon balance equation is discussed.

Transport is the second energy consumer sector after housing in Algeria. In this article, we explore the energy implication of commuting by considering a panel of socio-economic (SE) and built environment (BE) driving factors. The method is based on four steps: (i) The first step is to identify the main and potential drivers from the literature review and to propose a model that summarises the main assumptions that could explain the volume of commuting and the resulting energy consumption. (ii) In the second step, we designed and distributed 700 questionnaires in the municipality of Djelfa and retained 184 valid questionnaires in the final study sample. (iii) In the third step, we developed a method adapted to urban areas to quantify energy consumption as a function of the distance travelled, the type and density of occupation by means of transport and the type of fuel. (iv) The fourth step is to check the fit of the hypothetical model with a path analysis-based approach. The model developed identifies 15 factors, of which five have a direct impact and 10 have an indirect impact on the energy consumption of commuting. The model shows that building density and the age of the respondent can reduce the energy consumption of commuting by up to −15% and −12% respectively; whereas the number of cars by housing and the round-trip frequency could increase the energy consumption up to 38% and 27% respectively. Our results suggest a structuring role of the socio-economic characteristics of households in explaining the energy consumption of commuting.

This paper explores the impact of balconies on the energy demand required for cooling in the arid climate zone of the city of Adrar, in Algeria. For the purpose to assess several situations of the balconies, we have chosen a parametric method based on a campaign of thermal simulations. The open and eliminated balcony type were selected and characterized by four parameters: the balcony to room ratio, the orientation, the window type, and the balcony position. A set of 100 simulations was selected randomly based on the Monte-Carlo probability technique. The final sample was corrected based on Cook’s distance which gave 85 simulations as a final sample size. A generalized regression model was performed to identify the impact of each parameter. The accuracy of the model is above 97% and the sensitivity analysis shows that the most important factor is the balcony to room ratio which could reduce the energy demand up to 26% followed by the window type (24%), the orientation (8%) and the balcony position (5%). This conclusion stresses the idea of considering the balcony as a passive solution to reduce the cooling energy demand.

The key design strategies that reduce the energy demand of buildings are not present in most thermal codes in many countries. Therefore, modeling techniques offer an alternative to combine the architects' modus operandi with the energy efficiency in the early stages of architectural design and with higher speed and precision. However, a review of the scientific literature using modeling techniques shows that most researchers use a relatively large sample of thermal simulations. This paper proposes a simplified method based on the linear regression modeling technique and considers a relatively smaller sample of thermal simulations. A total of 6 key building design strategies were identified, related to the urban context, building envelope, and shape factor. A simulation protocol containing 60 possible combinations was designed by random selection. In the present study, the Pleiades software was used to estimate the annual energy demand for heating and cooling for a typical dwelling in a humid climate zone. A parametric study and sensitivity analysis to identify the most efficient parameters was performed in SPSS 21. The resulting model predicts the annual energy demand with an accuracy of 93.7%, a root mean square error (RMSE) of 5.88, and a scatter index (SI) of 8.59%. The models performed could efficiently and quickly assist architects while designing the buildings in the architectural practice.

This paper explores the role of the built environment and socio-economic drivers in shaping the modal share of commuting. For this, we have identified through our literature review 67 potential variables categorised into two groups; the built environment and the households’ socio-economic characteristics. We have considered the city of Djelfa as a case study and used the questionnaire as a data collection tool. The questionnaire processing of the 700 questionnaires provided to the households allowed us to select 184 questionnaires for our analysis. The sensitivity analysis protocol is designed for two stages; (i) an exploratory stage, conducted by principal component analysis and bivariate correlation analysis; (ii) and a confirmatory stage conducted by a path analysis. The first step allowed us to hypothesise several causal pathways that could explain, directly or indirectly, the modal share of commuting. The results of the path analysis show that the modal shares of walking, private car and public transit are controlled by 13, 16 and 12 explanatory variables, respectively. Overall, the socio-economic characteristics of households discourage walking and transit use, and encourage private car commuting. On the other hand, the variables identified in this paper related to the built environment discourage walking, but encourage the use of public transit rather than private cars for commuting.

Households are the major energy consumer and contributor to the emission of greenhouse gases. The Algerian policy of mastering energy has improved building energy efficiency since 1994 by introducing thermal regulation (DTR). However, energy consumption is still increasing instead of decreasing, which is mainly due to occupants’ behaviour which is difficult to estimate and predict. This paper explores the impact of households and housing characteristics on residential gas and electricity consumption in the 36 municipalities of the department of Djelfa (Algeria) which is located in an arid and semi-arid climate zone. This paper is based on GIS and statistical techniques. It considers the yearly gas and electricity energy consumption (2013) of the municipalities of the department of Djelfa. The method is organised in four steps: (a) a multiple linear regression is used to construct two estimative models of gas and electricity. The models have more than 93% of accuracy for both gas and electricity models; (b) estimating gas and electricity consumption for 2008 according to the developed models; (c) organisation of the census data of 2008 in five dimensions: the population distribution, household characteristics, housing type and occupancy, and finally household appliance ownership; (d) a set of sensitivity analysis is performed based on Principal Component Analysis (PCA) and Pearson’s bivariate correlation and finally a path analysis is performed based on Structural Equation Model (SEM) to assess the importance of each variable. The overall impact of all these variables indicates that increasing the household size is the first factor reducing the electricity and gas consumption followed by the housing surface, density, room occupancy, and older households, while increasing the education level and appliance ownership boosts both per-capita gas and electricity consumption.

Climate change has been mankind's main concern over the last century, with its many impacts such as rising temperatures, water stress, increase in natural disasters and reduced thermal comfort in urban spaces due to the Urban Heat Island (UHI) effect, a phenomenon that causes several deaths around the world every year and for which measures must be taken to mitigate its impact. The aim of this paper is to assess the impact of climate change on the urban microclimate and the resulting thermal comfort. The Place de Ettoute in Blida city centre, Algeria, is considered as a case study. This case is considered as a reference on which 3 scenarios, B1, A1B and A2 are applied representing three possible ways of the future of climate change according to the prospective study for 2100 of the IPCC for future periods. Envimet V3.1 is used to model the Ettoute urban square, in a 120x120m square with a 3x3m grid. It is based on a three-dimensional digital model that takes into account fluid dynamics and thermodynamic models to simulate the impact of surfaces, vegetation and the atmosphere at the micro-scale of the square. The comparison between the four scenarios shows an increase in temperature (from 0.5 to 1.24°C), particularly at night (8.8 to 10.03°C), which would reduce thermal comfort in Ettoute square, but scenario B1 has less impact on the urban microclimate and scenario A2 represents the worst case. In addition, a reduction in relative humidity (up to −19%) is expected for the scenarios A1B and A2 while a relative increase would be observed for the B1 scenario up to 2%. This study highlights the urgent need for adequate mitigation measures to reduce the impact of UHI for future periods. [ABSTRACT FROM AUTHOR]