• Energy Research
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Climate change is known as a serious threat to the human species, and its significance should be considered in building design. This study aims to investigate the relationship between energy consumption and CO2 emission in Iran during the years 2018–2019 using artificial neural networks (ANNs) and regression methods. The input data were gathered and optimized by the particle swarm optimization (PSO) algorithm. Lighting, equipment load rate, wall U-value, roof U-value and people density were deliberated as effective parameters. Afterwards, the ANN was created, trained and tested by the radial basis function (RBF) algorithm; also, the data were evaluated based on statistical analysis in SPSS software. The results demonstrated R2 = 0.99 and the 45-degree line for the predicted value. Energy consumption and CO2 were reduced to 35% and 73.21%, respectively. Furthermore, CO2 emissions and energy consumption had an inverse relationship with infiltration rates (−0.201) and (−0.098). Furthermore, CO2 emission and energy consumption had a linear relation in Iran with the equation of y = 1.63x + 0.52. Moreover, based on ANOVA test, R2 linear was 0.985 and R = 0.993, illustrating significant accuracy. Architects and designers could enjoy these findings as guidelines for renovation and designing purposes so as to alleviate the negative environmental impacts of construction.

Extensive cost in the building industry comes from cooling and heating to create thermal comfort. Hence, it is necessary to utilize passive solutions, in addition to suitable design, in order to reduce energy consumption. This research attempts to investigate the impact of archetype patterns in office buildings on annual energy consumption for cooling, heating and daylight loads. For this purpose, the DesignBuilder software was used to compare the forms. In this study, four conventional construction forms were considered, including the single and dense form, central courtyard buildings, U form and linear form, and each was considered with two, four and six-stories. Forms were simulated in the three cities of Bushehr, Shiraz and Tabriz, with hot-humid, hot-dry and cold climates, respectively. The results revealed that the office building with a linear form in Bushehr had the lowest energy consumption in the two and four-story forms, and also in the six-story form, the central courtyard form had the lowest energy consumption. Additionally, the central courtyard forms in Tabriz and Shiraz had the lowest energy consumption in all cases. Finally, the linear form possessed the most natural daylight through all of the studied cases for the three cities in terms of natural light gain.

The sustainable development goals (SDGs) are required to be achieved by 2030, and measurement indicators are needed to properly visualize individual efforts toward SDGs. Here, we developed a Japanese version of the Sustainability Consciousness Questionnaire (SCQ), the most well-known individual measure of SDGs, and examined its reliability and validity. Three online surveys were conducted with 1,268 Japanese adults. The results of confirmatory factor analysis showed that the Japanese version of the SCQ consists of two single-level factors: sustainability knowingness/attitude and sustainability behavior. These two factors demonstrated sufficient internal consistency by Cronbach's alpha and McDonald's omega coefficient, which ensured measurement reliability. Additionally, cocorrelations with other scales indicated that the higher the level of sustainability knowledge and attitude, the less positive attitude toward climate change and the higher the level of sustainability behavior, indicating the construct validity of these factors. These results indicate that the Japanese version of the SCQ is reliable and valid.

This paper aims to design a supply chain network for producing double glazed glass from the recycling of waste glass. All three pillars of sustainability are taken into consideration. The economic objective tries to maximize total profits. The environmental objective considers the energy consumption, the generated waste, the greenhouse gas emission, the water consumption, and the fuel consumption of vehicles. The social objective addresses created job opportunities, the worker safety, the regional development, the worker benefit, and training hours. To solve the model, a two-stage framework based on the group best-worst method and an interactive fuzzy programming approach is developed. The proposed model is validated through a real case study based on waste glass management in the city of Shiraz. It is revealed that when sustainable development goals are approached, a great degree of improvement will be attained in environmental and social aspects without a significant decrease in the economic sustainability. The results also demonstrate that the locations of glass recycling centres are different under economic, environmental, and social pillars, and the proposed model yields an optimal system configuration with a proper satisfaction degree of all objectives. Moreover, applying the proposed solution procedure enables system designers to obtain the most desirable trade-off between different aspects of sustainability.

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

AbstractSteam Assisted Gravity Drainage (SAGD) as a successful enhanced oil recovery (EOR) process has been applied to extract heavy and extra heavy oils. Huge amount of global heavy oil resources exists in carbonate reservoirs which are mostly naturally fractured reservoirs. Unlike clastic reservoirs, few studies were carried out to determine the performance of SAGD in carbonate reservoirs. Even though SAGD is a highly promising technique, several uncertainties and unanswered questions still exist and they should be clarified for expansion of SAGD methods to world wide applications especially in naturally fractured reservoirs. In this communication, the effects of some operational and reservoir parameters on SAGD processes were investigated in a naturally fractured reservoir with oil wet rock using CMG-STARS thermal simulator. The purpose of this study was to investigate the role of fracture properties including fracture orientation, fracture spacing and fracture permeability on the SAGD performance in naturally fractured reservoirs. Moreover, one operational parameter was also studied; one new well configuration, staggered well pair was evaluated. Results indicated that fracture orientation influences steam expansion and oil production from the horizontal well pairs. It was also found that horizontal fractures have unfavorable effects on oil production, while vertical fractures increase the production rate for the horizontal well. Moreover, an increase in fracture spacing results in more oil production, because in higher fracture spacing model, steam will have more time to diffuse into matrices and heat up the entire reservoir. Furthermore, an increase in fracture permeability results in process enhancement and ultimate recovery improvement. Besides, diagonal change in the location of injection wells (staggered model) increases the recovery efficiency in long-term production plan.

Nowadays, the use of renewable energies has increased due to the energy crisis and subsequent environmental issues. The window design significantly affects energy consumption and natural light absorption regarding preventing visual discomfort and improving indoor quality with effective external features. Hence, it should be carefully selected from the early stages of design. Thus, the present study investigated the optimal design of windows considering four components of the window-to-wall ratio (WWR), window shape, and positioning on each façade by separately considering the sill height of the window for a general office. The objective was to provide visual comfort and save energy. Applying constraints to the data set can yield an optimization method concerning the variables and their relationship as well as optimal solutions based on the stated goals. Therefore, the desired groups can be accepted as optimal solutions for improving the efficiency of the building. According to the results, the WWR of 30% with the square and horizontal shapes in the upper and central positions were optimal solutions for each window orientation, which had better performance in the north-facing WWR of 40%. Furthermore, several best design solutions were presented in each orientation in terms of energy consumption, daylighting, and visual comfort in the indoor environment. This method also allows the designer to visualize all the data while finding the clients’ desired option by improving the energy efficiency between the variables and choosing the appropriate solution.