Energy Consumption Index Research Articles

Exploring the link between green energy, CO2 emissions, exchange rate and economic growth: Perspective from emerging South Asian countries

This paper investigates the nexus between renewable energy use, CO2 emissions, exchange rate, and economic development within emerging South Asian nations, namely Bangladesh, India, Pakistan, and Sri Lanka, employing the Autoregressive Distributed Lag (ARDL) framework. It examines annual data spanning from 1990 to 2019, examining key indicators of renewable energy consumption, CO2 emissions, exchange rate, and economic development. The ARDL bounds test results demonstrate the existence of co-integration among the variables in the long run. The empirical result finds that the renewable energy consumption, CO2 emissions, and exchange rate have a significant impact on economic growth in Bangladesh, Pakistan, and Sri Lanka in the long run. In India no significant relationship found in the long run. In short run assessment, Bangladesh, India, and Sri Lanka also found same relationship with economic growth and renewable energy consumption, CO2 emissions, and exchange rate. Interestingly, In Pakistan no significant relationship has found in short run estimation analysis. Furthermore, study tried to determine the causality direction by using the Toda Yamamoto granger causality approach, which reveals bidirectional causation between exchange rate and CO2 emission in India. In Pakistan, study also found bi-directional causality among the variables renewable energy consumption, CO2 emissions, and economic growth. Finally, this paper emphasizes developing the policy as well as making a concrete decision regarding the renewable energy consumption, CO2 emissions, exchange rate, and economic development for ensuring sustainable economic growth in South Asian region. Future research could extend this work by including different dimensional data, additional countries, or using alternative or supplementary modeling techniques.

Energy efficiency strategies for Brazilian social housing considering a life cycle perspective: Optimisation between thermal autonomy, energy consumption and costs

This study aims to establish optimal cases for energy-efficient social housing projects in Brazil, considering a life cycle cost-benefit analysis. Energy efficiency measures for the building envelope were evaluated in representative single-family and multifamily typology projects for the lower-income sector, considering thermal autonomy, energy consumption and cost indicators. The measures estimated in terms of macrocomponents allowed for comparative evaluation and association with costs. Cases were simulated in EnergyPlus to obtain the building’s expected operational consumption. With the macrocomponents and simulation data, Python routines were used to compute results for each proposed combination. Optimal cases that present high thermal performance in the national standard (NBR 15575) with cost-effectiveness, offering guidelines for projects in the 8 Brazilian bioclimatic zones, were established. Results presented in more detail for bioclimatic zone 8 showed optimal cases, with the increase in the percentage of hours of thermal autonomy reaching 65% to 71%, whereas that of a typical building was 30%, while life cycle cost related to the envelope showed reductions of up to 22%, even with higher initial costs. This study served as a basis for the new Brazilian policy proposed by the National Housing Office (SNH) to develop new social housing projects.

Intelligent analysis of landscape architecture based on digital technology under 5G network

In the wake of the wide promotion of 5G network, the era of super-high-speed networks and the Internet of Everything is approaching. Combining digital technologies led by 5G with landscape architecture has become an important way for the sustainable development of garden ecology. In order to achieve refined management of gardens and improve the accuracy and consistency of garden environmental data monitoring, this study constructs a new IoT sensor multi data fusion algorithm model. Considering the high redundancy and large error data collected by multiple sensors, this paper proposes a multi data fusion algorithm based on adaptive trust estimation and improved D-S evidence theory. The experimental data demonstrates that matched with IGA-BP, algorithm in this paper obtained the largest fitness value and the fastest convergence speed in three sensor application scenarios with different numbers of nodes. The lowest values were obtained in terms of unit energy consumption and network latency indicators. In the monitoring experiment for environmental data of landscape architecture, the algorithm obtained lower relative error and mean square error than IGA-BP in four environmental parameters of temperature, humidity, light intensity and carbon dioxide concentration. Therefore, the algorithm is effective in real-time monitoring of landscape garden environmental data, and can provide decision-making data for garden management as a reference.

Energy Efficiency and Environmental Benefits of Waste Heat Recovery Technologies in Fishmeal Production Plants: A Case Study in Vietnam

The fishmeal production industry is essential for providing protein for animal feed in the aquaculture sector. However, the industry faces challenges related to energy consumption and environmental sustainability. This study evaluates the energy efficiency and environmental benefits of waste heat recovery (WHR) technologies in a fishmeal production plant in Vietnam. Data were collected from the plant between 2016 and 2022, and a specific energy consumption (SEC) indicator and a comprehensive methodology were utilized. Implementing an economizer as a WHR technology resulted in a 55.5% decrease in SEC compared to the state before installation. The enhanced energy efficiency also translated to reduced energy consumption per output unit. Moreover, the economizer contributed to annual energy savings of 4537.57 GJ/year and cost savings of USD 26,474.49. Additionally, carbon dioxide (CO2) emissions associated with producing one ton of fishmeal decreased by 58.37%. These findings highlight the potential for WHR technologies to improve energy efficiency and reduce the environmental footprint of fishmeal production. The study’s results provide valuable insights for practitioners and policymakers in promoting energy efficiency practices and reducing environmental impact in this and similar industries.

Recycling waste dolomite powder in cement paste: Early hydration process, microscale characteristics, and life-cycle assessment

Waste dolomite powder (WDP) is a byproduct obtained from dolomite quarries during the preparation of dolomite products. To study the re-utilisation of WDP, an eco-friendly cement-based material was prepared using WDP as a micro-aggregate. The effects of WDP on the early hydration process, microscale characteristics, and life-cycle assessment of cement paste are discussed in this study. The isothermal calorimetry results showed that the incorporating WDP in cement paste accelerated the early hydration process of cement according to the degree of reaction. In this case, the setting time of the cement pastes with WDP was shortened, and the early compressive strength was significantly improved. The results of X-ray diffraction and scanning electron microscopy analysis at early curing ages (1 and 3 d) showed changes in the peak intensity of ettringite and portlandite and a denser microstructure. Mercury intrusion porosimetry tests showed that the middle and large capillary pores were refined by the nucleation and filling effects of WDP. Based on environmental and economic evaluations, the utilisation of WDP reduced energy consumption, CO2 emissions, and economic costs. Compared to the sample without WDP, the energy consumption, CO2 emissions, and economic cost indices were 42 %, 42.69 %, and 39.4 % lower, respectively. Our results may provide valuable references for the re-utilisation of WDP in low-carbonation cement-based materials.

Parameterization of the statistical model for electrical energy efficiency control

Purpose. Justification of a structural construction and parameters of a regression model for the normalization of specific energy consumption when controlling the production process energy efficiency. Methodology. Analysis of the peculiarities of energy efficiency control of the production process in conditions of frequent and significant changes in specific energy consumption, followed by the determination of the structure and parameters of the regression model. Findings. Based on the presence of frequent and significant changes in the energy efficiency control of the production process, the reasonableness of normalizing the specific energy consumption by using the regression model with a variable structure is substantiated. The actual daily specific energy consumption indicators, obtained during the month to control energy consumption efficiency and build the regression model of the variable structure, are used. The limited possibilities for the formation of voluminous statistical samples with homogeneous data, and the complexity and laboriousness of measuring a significant number of influence parameters make it necessary to reduce the number of explanatory variables of the regression model. The feasibility of using the value of the output volume, as a comprehensive characteristic of the level of energy consumption, is proven. The acceptability of the application of linear and non-linear univariate regression dependencies is determined. The nonlinear model, as a result of reducing the linear model of energy consumption to a nonlinear form characteristic of the values of its specific consumption, is obtained. Originality. For the first time, the use of the regression model of the variable structure for the normalization of specific energy consumption in conditions of frequent and significant changes in the energy efficiency of the production process, which helps to increase the accuracy of their determination, is proposed. The need to reduce the number of explanatory variables of the regression model is proven. The expediency of using linear or non-linear one-factor regression dependencies in the given conditions of energy efficiency control, which helps to simplify the procedure of registering the initial data for their construction, is confirmed. Practical value. The scientific results of the performed studies allow for taking into account the peculiarities of the production conditions when determining the structure and parameters of the regression model for normalizing the specific energy consumption. This contributes to increasing the accuracy and energy efficiency control of the production process.

Natural gas supply cuts and searching alternatives in Germany: A disaggregated level energy consumption analysis for environmental quality by time series approaches.

By considering the search for alternatives against Russia's natural gas supply cuts, this study explores the impact and causality of disaggregated level energy consumption indicators on environmental quality. Hence, the study investigates Germany, which is the leading economy in Europe and highly dependent on Russia's natural gas supply, by using carbon dioxide (CO2) emissions as the environment indicator, including annual data from 1970 to 2021, and applying novel time series approaches. In the empirical examination, Granger causality-in-quantiles (GCiQ), quantile-on-quantile regression (QoQR), and multivariate adaptive regression splines (MARS) are applied as base models while quantile regression (QR) and dynamic ordinary least squares (DOLS) are used for robustness. The empirical findings show that (i) there are causal impacts of disaggregated level energy consumption indicators on CO2 emissions; (ii) renewable energy and hydroelectricity consumption have a decreasing impact, whereas natural gas, coal, and oil energy consumption have an increasing impact on CO2 emissions; (iii) although nuclear energy has been discussed as a potential alternative, nuclear energy does not have a significant impact in decreasing CO2 emissions; (iv) natural gas consumption has an interaction with renewable energy, hydroelectricity, and coal energy consumption; (v) the power of disaggregated level energy consumption indicators on CO2 emissions vary according to quantiles, thresholds, and interactions between energy consumption indicators; (iv) alternative models validate robustness of the results obtained. Thus, the results imply that the most appropriate alternative is coal energy consumption in the short-term and renewable energy consumption in the long-term to compensate for Russia's natural gas supply cuts, whereas nuclear energy consumption is not a real alternative for Germany.

From Fossil Fuels to Renewables: Analyzing the Pathways to Carbon Emission Reduction in Developing Nations

The present study endeavours to examine the determinants that contribute to the enhancement of environmental quality within specific developing economies while concurrently assessing the validity of transitioning these economies from fossil fuel dependency to renewable energy sources. To achieve this objective, panel data encompassing selected developing economies spanning the period from 2000 to 2022 has been collected from the World Development Indicators database. Fossil fuel consumption is quantified through measures of energy consumption, while renewables are gauged using indicators of renewable energy consumption and renewable electricity consumption. In terms of analytical methodology, both fixed-effect and random-effect models have been employed. The estimated coefficients from the empirical analysis reveal that energy derived from fossil fuels significantly correlates with higher carbon dioxide emissions in the selected developing countries, suggesting that a shift toward renewable energy sources and electricity consumption can serve as an effective strategy for mitigating carbon emissions. Furthermore, the findings indicate that carbon dioxide emissions may be curbed through the facilitation of international trade and the expansion of industrial activities. Conversely, greater levels of foreign direct investment (FDI) and government expenditure appear to be associated with increased carbon dioxide emissions in these developing economies. In light of these findings, it is recommended that the promotion and adoption of renewable energy sources such as solar, wind, hydropower, and geothermal energy be prioritized as a means of effectively reducing carbon emissions and fostering environmental sustainability within these developing nations.

Comparison of the Vortex Chamber Supercharger Characteristics for Grain Pumping with Other Types Superchargers

Problem. Among the superchargers used in pneumatic transport, jet pumps have the best reliability and durability indicators. However, their use in these systems is limited due to the large specific energy consumption indicators for pumping. Vortex chamber superchargers allow to increase the pressure of the medium at the outlet of the pump, in comparison with classic direct-flow jet pumps, but their characteristics during operation in undrained mode have not yet been investigated. Goal. The goal of this work is to analyze the characteristics of vortex chamber superchargers for pumping grain and compare the main integral parameters with the parameters of existing pneumatic devices of other types. Methodology. The study was carried out by comparing the experimental and calculated super-chargers characteristics with the technical characteristics of industrial pneumatic transport units based on direct-flow jet pumps and grain loaders. Results. As a result of the comparison, it was found that the mass flow rate for the solid medium (grain) for the vortex chamber supercharger exceeds the achievable (theoretical) rate of the direct-flow jet pump by 1.1 times and is 15% inferior to the grain loader. Originality. Improving the efficiency indicators of the vortex chamber supercharger allows for expanding the range of pneumatic device usage for grain over-loading. The distance of transportation can be increased to 220 m due to exceeding the indicators of the relative drop in comparison with direct-flow jet devices with an increase in the process efficiency. Compared to suction grain loaders, the vortex chamber supercharger can create a larger pressure drop, but with reduced efficiency. Practical value. The creation of a vacuum in a grain transfer unit is limited to 1 atm, unlike a jet supercharger, which can operate at almost any created pressure drop, which increases the transportation distance.

Deriving operating rules for inter-basin water transfer projects incorporating a scenario reduction strategy

Streamflow uncertainty is one of the main challenges in managing reservoir systems. To accurately characterize the streamflow uncertainty in a reservoir operation model, numerous streamflow scenarios should be considered, but this results in a huge computational burden for multi-objective optimization. Here, we propose a methodology for deriving the operating rules for an inter-basin water transfer project (IBWT). The methodology adopts a scenario-reduction strategy to balance accuracy against the computational burden. The proposed method first reduces a large number of streamflow scenarios to several typical scenarios and the corresponding occurrence probabilities using a simultaneous backward reduction method. A multi-objective optimal operation model is then formulated to optimize the expected values of performance indices of the IBWT system under the typical scenarios. Finally, a simulation-based optimization framework is adopted to derive the multi-objective operating rules. To test the method, the Han to Wei IBWT project in northwest China is considered as a case study. The results show that the proposed methodology produces effective reservoir operating rules with a relatively low computational burden. After the initial 1,000 streamflow scenarios have been reduced to 20, the model solution time decreases significantly, from 49.17 h to 1.05 h, while the changes in mean value (0.47%) and standard deviation (0.93%) of these scenarios are less than 1%. Compared with an operation scheme that uses only historical streamflow data, the operation performance is further improved in terms of energy production, energy consumption, and water shortage index when the streamflow uncertainty is considered.

COMPARATIVE ANALYSIS OF FOREIGN AND DOMESTIC ENERGY EFFICIENCY INDICATORS AND THEIR CALCULATION

Climate change, the level of environmental pollution, the assessment of the exhaustion of traditional raw materials for energy generation, the awareness of the fact that the negative impact on the environment is caused by anthropogenic activities, the world economic crises and the search for ensuring a happy and safe life for future generations became the driving force for the development of energy efficiency. But in order to understand the individual directions of tasks in increasing the level of energy efficiency, it is worth defining approaches to its assessment. It is interesting that this field includes not only the concept of "energy", but also has a connection and to some extent limitations with the fields of economics, ecology and law.
 This paper analyzes the concepts and approaches used in assessing the level of energy efficiency internationally and in the middle of the country. The results of the analysis showed that in global practice, energy efficiency assessment based on final consumption is used in four spheres of activity: production, transport, services and households. In each of the subsectors, the energy consumption indicator is evaluated in comparison with the previous year. And the index at the sector level is the average weighted sum of the indicators of its subsectors. To obtain better results, various types of adjustments are used. Climate corrections and purchasing power parity adjustments are one of them. To increase the level of energy efficiency within the country, it is possible to apply the listed practices at the regional level.

Use of life cycle assessment and modeling techniques for prediction of energy-environmental indicators in different wheat production systems

This study was conducted to analyze and model the energy consumption and environmental indicators for different wheat production systems in Qazvin province. Information needed for this study, related to 2021–2022, data was collected by monitoring machinery, and inputs during wheat production were interviewed by farmers and filled out questionnaires. The purpose of this study includes: (1) estimating the fuel consumption and energy of inputs in different wheat production systems, (2) estimating the environmental impact associated with input consumption using life cycle assessment (LCA), and (3) providing suggestions for optimization. The results showed wheat production in irrigated conservation tillage system has energy ratio (2.12) and energy productivity (0.18 kg MJ−1) suitable compared to conventional irrigated and rainfed tillage, and conservation rainfed tillage. Nitrogen fertilizer (55%–68%) and fuel consumption (15%–31%) of affect the energy consumption of four wheat cultivation systems. Three categories of damage to human health, ecosystem and resources in LCA were using the ReCiPe2016 method. Resource damage category for conventional tillage irrigation (76.05 USD2013) has significant pollution. The share of seed emissions, On-Farm emissions and nitrogen emissions affect the categories of damage to human health, ecosystems and resources, respectively. The results of the Cobb-Douglas model showed that T3 (fertilization and weed control) was significant at the level of 1% among other inputs. T1 (chisel packer roller), T2 (planting) and T4 (harvest and straw packaging) were significant at 5% level. The results of artificial neural network (ANN) of conventional rainfed cultivation showed that 6 input layers, 7 hidden layers with 10 hidden neurons and 1 output parameter were selected as the best structure. R2 and RMSE for fuel consumption were 0.95 and 43.65, respectively. As a result, the ANN results show higher accuracy than the Cobb-Douglas model.

About the effect of tailored energy feedback on space heating consumption and indoor air temperature in social housing buildings: An experimental investigation

Providing the occupants with tailored feedback on their energy consumptions can be an effective method to help the users to adopt energy-efficient behaviours. However, in specific contexts such as the social housing, this may be challenging, due to the peculiarities and the economic constraints of the end-users, which make them vulnerable and needing a proper design of the information. Nevertheless, there is a lack of detailed studies in the literature analysing the effects of personalized energy feedback on the space heating consumption and the indoor air temperatures in the social housing sector. This paper aims to evaluate the effects on space heating consumption and indoor air temperature of a personalized feedback campaign delivered in three social housing buildings consisting of 27 apartments in Mediterranean climate. To this end, a personalized feedback campaign was designed and developed, providing for the use of disaggregated energy consumption indicators to show the location of excessive thermal energy consumptions. The daily thermal energy consumptions, as well as the indoor air temperatures of the single dwellings, were monitored daily for over four heating seasons and their variations were analysed before and after the implementation of the personalized feedback. A saving in normalized natural gas consumption of the buildings was found on average equal to −14,8% after the feedback campaign, with no significant variations in the indoor air temperatures. However, some critical issues have been highlighted in the response of individual end-users to the feedback provided. A greater propensity of the occupants to regulate the indoor air temperature through the thermostatic radiator valves in the unoccupied rooms of the house was also observed after the occurrence of the feedback campaign.

Enhances of nearly zero-energy public buildings in regions with hot summers and cold winter

Nearly Zero-energy residential buildings have attracted widespread attention due to their superior energy-saving performance. In order to achieve a balance between energy production and demand in residential buildings, the application of renewable energy is essential. This paper optimizes typical zero-energy public buildings in hot summer and cold winter locales using the concepts of passive priority, active optimization, and 100% renewable energy supply guarantee. The importance of passive energy-saving techniques like heating, shading, and heat recovery; in accordance with local resource conditions; analysis of building renewable energy utilization systems like solar energy and air energy; and recommendation of the most technologically efficient building energy system. According to the simulation results, if the criteria for zero-energy buildings are met, the annual energy consumption index per unit area is 37.6 kWh/m2, and the 8.9 kW building photovoltaic system can generate 36.4 kWh/m2 of power annually.

Experimental study on the thermal management performance of a power battery module with a pulsating heat pipe under different thermal management strategies

Given the power battery module for electric vehicle kinetic energy system in a high-temperature environment and large rate discharge process, there are thermal safety risks such as battery easy overheating and temperature uneven, this paper takes 23Ah LFP power battery module as the research object to build the system. The system uses TiO2 nanofluid pulsating heat pipe (TiO2-CLPHP) and cooling fan as heat conduction and heat dissipation components and conducts thermal monitoring of power battery modules through a thermocouple built into the power battery. The thermal management strategy of optimal thermal efficiency and optimal thermal management strategy of energy consumption proposed in this paper is used to solve the thermal management problem of power battery modules. On this basis, the performance tests of the power battery module thermal management system with optimal thermal efficiency and optimal energy consumption were carried out under different thermal management performance testing conditions, and the energy consumption and economy of the power battery module thermal management system under the two thermal management strategies were quantitatively analyzed. The test results show that compared with the pure power battery module without any thermal management technical measures, the maximum temperature of the thermal management system of the TiO2-CLPHP power battery module under the optimal thermal management strategy is reduced by 10.3℃ and the maximum cooling efficiency is up to 75.00%. In the dynamic test condition, the TiO2-CLPHP power battery module thermal management system implemented the optimal thermal management strategy of thermal efficiency, and the maximum temperature rise of the power battery was 9.7℃, 7.3℃, 7.5℃, and the maximum temperature difference of the battery was not more than 3.5℃ and 2.5℃, respectively. And when optimal thermal management, whose goal is to have a small critical temperature rise strategy by operating energy consumption, is adopted. The maximum temperature rises are 10.5℃, 8.5℃ and 6.9℃, respectively, with the maximum battery temperature difference not exceeding 3.6℃ and 3.5℃. Compared with the thermal management strategy with optimal thermal efficiency, this not only ensures the thermal management efficiency but also reduces the energy consumption index of the system (that is, it has less thermal management energy consumption). In addition, under different ambient temperatures, compared with the optimal thermal management strategy for thermal efficiency, the TiO2-CLPHP power battery module thermal management system implements the optimal thermal management strategy for energy consumption with a smaller temperature rise critical value, and the energy consumption index of the system can be reduced by up to 60.58%. This indicates that for the TiO2-CLPHP power battery module thermal management system, the proposed energy consumption optimal thermal management strategy with a small critical value can effectively take into account both thermal management efficiency and thermal management energy consumption, which is the most ideal thermal management strategy for power battery modules.

Does the tourism and travel industry really matter to economic growth and environmental degradation in the US: A sustainable policy development approach

The tourism and travel industry (TTI) has become a vital developmental tool for boosting economic growth globally; however, this economic thriving is adversely connected to the environment. This study examines the impacts of eight TTI subsectors on economic growth and environmental pollution in the United States of America by contextualizing energy consumption and globalization. We applied the ARDL bounds test and Granger causality approach on time-series data (2005 1st quarter–2019 4th quarter). Granger causality uncovers TLGH, GLTH, and feedback hypotheses between TT subindustries. The subindustries supporting TLGH’s proposal that long-term investment in these sectors could enhance economic growth. In addition, industries supporting GLTH indicate that a strong economy would be beneficial for these industries. Maximum subindustries indicate a significant positive association with energy consumption. The long-run dynamics show that TT subindustries have different influences on greenhouse gases (CO2, CH4, N2O) and air pollutants (CO, NH3, NOx, SO2, VOC, and PM2.5). Long-run dynamics show that food and drinking places emit more GHG than other entertainment and hospitality subsectors. Amusement, gambling, and entertainment contribute more to air pollutants among the entertainment and hospitality subsectors. Road and railway transportation contribute more to GHG emissions than other travel sectors in the long run. Air transportation in the travel subsector is responsible for high air pollutants. The Granger causality results reveal that art, gambling and recreation in entertainment and hospitality industries, and ground transportation contribute the most to environmental pollution. Globalization has varying effects on economic growth, energy consumption, and environmental pollution indicators. We have provided sustainable policy implications for reducing GHG emissions and air pollutants.

ECONOMIC ANALYSIS OF ENERGY EFFICIENCY OF CHINA'S AND INDIA'S NATIONAL ECONOMIES

The development of society is highly dependent on energy. However, the extensive use of power causes global problems such as resource exhaustion and climate warming. China and India are typical fast-growing developing countries whose economic advancement relies on traditional energy consumption. To realize the vision of sustainable development for these states, it is necessary to study the nations' economic efficiency of power use. Based on the analysis of energy consumption and gross domestic product trends in China and India from 1990 to 2019, this paper discusses the changes in the energy consumption index per unit of gross domestic product. The authors concluded that the power consumption in China and India is mainly concentrated in the industry, residential, and transport sectors. Divided by resource types, the energy consumed by China comes primarily from coal, oil products, and electricity, while the power consumed by India is mainly from coal, oil products, biofuels and waste. Since both countries mostly use coal and oil products which belong to fossil fuels, they emit large amounts of carbon dioxide, endangering the environment quality. Given this, China and India should focus on green power development. China's total energy consumption is much larger than India's, and China's gross domestic product is much larger than India's. However, China's energy consumption per unit of gross domestic product is less than India's. The higher the energy consumption per unit of gross domestic product is, the higher the dependence of economic development on energy is. The authors' suggestions are to develop non-fossil energy sources and adjust the energy mix, recycle solid wastes, and encourage carbon trading, climate investment and financing in the states to reach sustainable development targets.

Performance simulation and analysis of a multi-energy complementary energy supply system for a novel BIPVT nearly zero energy building

Building integrated photovoltaic–thermal (BIPVT) technology is an efficient form of solar energy utilization, which realizes self-energy supply and holds good development prospects. In this paper, a novel BIPVT module based on micro heat pipe array and BIPVT near-zero energy building are proposed. A matching multi-energy complementary energy supply system is also proposed for this building. The building and energy supply system models were built using TRNSYS. The load characteristics of BIPVT buildings were studied by analyzing the heat transfer process of BIPVT components. The energy-saving rate of the building was also calculated using the method of near-zero energy building evaluation index. The accumulated cooling load and heating load of the buildings throughout the year show that those of the BIPVT buildings are 8.6% higher than those of the benchmark building. Comparison of the energy consumption results shows that the annual cumulative energy consumption of the BIPVT building is 32.3% lower compared to the benchmark building’s. In addition, 61.2% of the energy required for year-round refrigeration, heating, and electricity can be supplied by this system. The energy consumption index of the BIPVT building is 70.26 kWh / (m2 • a). Compared with the benchmark building, the BIPVT building energy saving rate is 76.6% and the building energy efficiency improvement rate is 32.3%.

Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA.

By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO2) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R2 and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO2 emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO2 emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO2 emissions.

Selection of HVAC technology for buildings in the tropical climate case study

In this paper, the integral performance indicator for selecting HVAC systems (ACPI) is applied to select the Air Conditioning (AC) system with better results in their life cycle for a typical building in Barranquilla, Colombia. The AC systems under study were: direct expansion system (Split and VRF), air-cooled system (VAC and VAV), and water-cooled system (Chiller). Of the six selection criteria of ACPI, the energy consumption index (EC) and thermal comfort (TC) were calculated through energy simulation of buildings, the regulations were used for IAQ and carbon emissions (CE), and were estimated the investment costs (IC) and operating and maintenance costs (OMC). The best-performance AC system for the building was the air-cooled AC system got the best ACPI values between 25 % and 22.4 %, followed by the water-cooled system with 22.3 %, and finally the direct expansion systems between 16.8 % and 13.5 %, influencing in this level of priority the IAQ and IC criteria. This study contributes to introducing in Colombia a multifactorial analysis to select the more proper AC system technology according to the building and location conditions.