Search

Studying the trends of countries in the past has a significant impact on developing their future roadmaps and efficient policies in energy sector. This paper examines the performance of BRICS and G7 countries in the field of energy and environment. In this regard, 3 indicators related to the field of energy, i.e., energy intensity, energy import and use of renewable resources, and 3 indicators related to the environment, i.e., greenhouse gas emissions, CO 2 emissions and CO 2 emissions intensity were selected. Using an optimization model based on Holt-Winters, the values of these indicators were obtained for the studied countries in the 2040 horizon. Finally, by using the KEMIRA method, the performance of the countries in the base and forecast year was evaluated. The findings reveal that in the projected year, BRICS countries excelled with a higher emphasis on renewable energy for ranking, whereas developed G7 economies showed better performance in the base year with a greater contribution of energy intensity in ordering. Recommendations for mature economies include prioritizing environmental taxes, investing in clean technologies, embracing circular economy, and securing critical mineral supply chains. For BRICS nations, a balanced energy transition is advised, leveraging natural gas as a transitional fuel, financing renewables, and advancing carbon capture, electric vehicles, and hydrogen technologies. Although various dynamic indicators influence the countries' policy-making process and performance, this research provides a general perspective of the energy and environmental performance of key players in the global economy. • A novel integrated model is proposed in order to compare the current and future energy and environment performance of BRICS and G7 countries. • An optimization model based on Holt-Winters method is adopted to forecast energy and environment criteria for the 2040 horizon. • The newly-introduced KEMIRA method is utilized to rank the countries in the base and forecast years and perform a multi-criteria analysis. [ABSTRACT FROM AUTHOR]

Modeling of energy systems in the building sector is one of the most fundamental topics in energy systems engineering. Multi-criteria decision-making methods have also had many applications in this field. This paper models an energy system regarding combined cooling, heating and power (CCHP). In his regard, for a commercial building, the energy system was modeled in six different scenarios. Then, using multi-criteria decision-making methods and considering economic, technical and environmental indicators, scenarios were analyzed and ranked. To obtain the criteria weights, the CRITIC and Shannon's Entropy methods were used, and by introducing a novel weight integration method, the weights were combined to obtain the final weights. Finally, the scenarios were ranked using the EDAS method. The results revealed by changing from scenario S1 to S6, economic indicators decrease and the values of net present cost, levelized cost of electricity and operating cost lower by 38.64%, 36.14% and 40%, respectively. It is also shown that the combination of solar system, grid, boiler and electric chiller as well as solar system, grid, boiler, gas generator and absorption chiller were the most favorable energy systems for the building. • A commercial CCHP system is modeled considering six different system architecture. • A combined decision making framework is utilized in order to rank the scenarios. • A novel weight integration method is proposed to aggregate the initial weights and acquire final weights of criteria. [ABSTRACT FROM AUTHOR]

With increasing environmental problems of fossil fuel-based devices and systems in societies, diffusion and adoption of sustainability solutions such as renewable energy technologies and hybrid/electric vehicles have increased in the residential and commercial sectors. However, energy demand in the buildings and energy supply from renewables are complex, dynamic, and non-linear. This complexity shows itself in cold-climate regions that the supply of required energy from renewables is along with uncertainties and even sometimes stochastic. This paper assesses the energy supply/demand performance of a group of residential buildings in a community in a cold-climate region, St. Albert, Canada. First, all the buildings of the community are modeled and the required energy to respond to the demand and electric vehicles are calculated. Then, the potential of each building for electricity supply via photovoltaic panels is calculated. Finally, the energy supply/demand management of the community is assessed using a machine learning tool. The results show that the community peak heating and cooling loads are 420 kW and 121 kW, respectively and the total annual energy production from photovoltaic system is 14,203 kWh for a single house. Regarding the electric vehicle load, the photovoltaic system can provide 29.23% of the community's total load, annually. Finally by comparing the modeled pattern and the predicted pattern, an accuracy of 88.6% is obtained for the prediction. • Assessing the energy supply/demand performance of a group of residential buildings. • Calculating the required energy for the demand and electric vehicles and supply via PV. • Predicting the energy supply/demand management using a machine learning tool. [ABSTRACT FROM AUTHOR]