Search

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

Open water transposition channels in hot and arid regions, like those in the São Francisco River Integration Project (PISF) in Brazil, suffer significant water losses through evaporation. This paper proposes covering these channels with photovoltaic (PV) panels to reduce evaporation while simultaneously generating clean energy. The research aims to quantify water savings and energy generation potential across all channel lengths and assess whether the generated solar power can substitute grid electricity for powering the transposition pumps during peak hours, thereby enhancing energy efficiency. This study analyzed the state-of-the-art of PV generation and calculated their solar potential. Identified the specific characteristics of PISF channels and watercourses considering the regional geography, meteorology, irradiation, and social peculiarities. And, finally, assessed the feasibility of covering the watercourses with solar panels. The results reveal that covering all current PISF channels with PV panels could save up to 25,000 cubic meters of water per day, significantly contributing to water security and improving the quality of life for the local population. Additionally, the project could generate 1200 gigawatt-hours of electricity annually, meeting the energy demands of the transposition pumps during peak hours and promoting energy efficiency within the project. This research paves the way for utilizing PV technology to address water scarcity challenges and enhance the sustainability of water infrastructure projects in arid regions worldwide. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

Since the 1970s, Brazil has carried out the most successful world program of commercial biomass for use and production of energy by stimulating its sugarcane industry and promoting the large-scale production of ethanol nationwide in response to the first oil shock. Today, the technologies behind ethanol production are well established. Brazil is the world's largest sugarcane supplier, producing its ethanol at a competitive price. If other sugarcane producing countries decided to join Brazil's move toward the production of this biofuel, what impacts might there be for each country's economy and employment? This is what we investigate in this paper. Prime candidates for ethanol production include Australia, China, Colombia, India, Indonesia, Mexico, Pakistan, the Philippines, Thailand, and the United States of America. We evaluated the potential socioeconomic impacts of developing this promising industry by using an input–output approach. More specifically, we adapted the Brazilian method of producing ethanol to these countries' distinct economies. We augmented the input–output table of each country, inserting a new ethanol industry based on the Brazilian ethanol production model. We also augmented their new ethanol industry's sales following a hypothetical hydrous and anhydrous ethanol consumption scenario. Thereafter, we reconcile the national accounts, concluding our analysis by quantifying and comparing the different net effects of this new industry for each of the assessed countries for the year of 2009. We demonstrate which industries would be expected to be positively or negatively impacted by this substitution in each country; and find that not all of the countries we assessed would experience positive socioeconomic results from emulating Brazil's production of ethanol. [ABSTRACT FROM AUTHOR]

On average, 70% of the world's freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for this irrigation. The proposed approach involves grouping the detected flooded areas using k-means clustering with the electricity customers' geographical coordinates, provided by the Power Distribution Company. The methodology was evaluated in a dataset of satellite images from southern Brazil, and the results demonstrate the potential of using U-Net models to identify rice fields. Furthermore, comparing the estimated electrical energy consumption required for irrigation in each cluster with the billed energy values provides valuable insights into the sustainable management of rice production systems and the electricity grid, helping to identify non-technical losses and improve irrigation efficiency. [ABSTRACT FROM AUTHOR]

The energy balance constitutes a powerful management instrument for government agencies, as it offers an overview of the energy situation of the country (or region) and serves as a guide for energy policies and monitoring of these policies. Although Brazil has published the national energy balance for more than half a century, the national publication does not adequately address energy statistics at the level of the states. This occurs either due to the lack of specific data or the absence of total disaggregation. Accordingly, the elaboration and implementation of public policies for the energy sector in the Brazilian states lack consistent energy statistics. Therefore, this paper aims to present a model for the Brazilian states to elaborate the energy balance. The proposed model consists of applying internationally referenced methodologies to develop a user-friendly software, which includes automatic energy unit conversions, different chart styles, high-level data organization, and Sankey diagrams. As a result, the software can be adopted by local governments as a tool to maintain the state energy balance publication periodically, and hence obtain the detailed information necessary to manage and formulate energy policies. The advantage of the software is that it can be operated by non-experts and the energy flow as well as the entire report can be generated automatically. The proposed software was successfully used to generate the energy balance of the Mato Grosso do Sul state. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the information about protection of technologies related to distributed generation (DG) of electric energy, available in the Brazilian patent bank, in the period between 2011 and 2020. Considering that the energy sector has strategic role for companies and countries, analyzing technological trends in the area can help management and decision making. As a methodological procedure, (1) data were collected from the national database of the Instituto Nacional de Propriedade Industrial - INPI (National Institute of Industrial Property) -- in July 2020, (2) associated with keywords and the Classificação Internacional de Patentes - IPC (International Patent Classification) for research and (3) treated with the aid of Microsoft Excel and IBM SPSS Statistics version 25 software. The variables found and analyzed were: nature of the patent; order status; international classification; year of filing; publication of request; countries of origin of depositors; main depositors; state of origin of depositors; electrical system structure; and types of energy sources. The results demonstrated that the number of patent filings fluctuated over the years; that there was a significant growth in the number of deposits in the years that normative resolution n° 482/2012, responsible for regularizing DG in Brazil, was published or revised and that most of the technologies deposited are intended for power generation, with emphasis on that use wind energy. [ABSTRACT FROM AUTHOR]

Two economic activities have been integrated in southern Brazil: olive cultivation and sheep farming. The favorable edaphoclimatic conditions in the southern region for cultivating olive trees and cultural preferences for the consumption of sheep meat make this integration a valuable strategy. Considering that every activity causes environmental impact and the exploitation of natural resources, new systems that promote sustainable development and the efficient use of these resources become necessary. This paper presents the first study of the carbon footprint of the life cycle of integrating of sheep and olive farming, with the objective to build an inventory based on primary data collected on olive and sheep farms in Brazil, determine the carbon footprint of the conventional and integrated system and estimate the potential for mitigating GHG emissions. The evaluation was performed using the life cycle assessment (LCA) methodology. The study focuses on the detailed analysis of the processes and activities that are responsible for GHG emissions, including enteric methane emissions, methane emissions from manure management, emissions from nitrogen fertilizer use and limestone application and energy consumption. For this, two major scenarios were considered, the first centered around on the traditional olive cultivation and sheep farming systems individually, and the second involved their synergistic integration. The findings indicate that the conventional system is associated with a global warming impact of 6.8 kg CO 2 eq per liter olive oil and kg live weight. In contrast, the integrated system demonstrates a significant mitigation potential of 27.7%, resulting in a reduced global warming impact of 4.92 kg CO 2 eq. This advantage is a consequence of the synergy between the systems, as they now occupy the same area, which allows the sharing of agricultural activities and agrochemical inputs, in addition to optimizing nutrient recycling and complementing each other. Additionally, the results of a sensitivity analysis showed that enteric methane from sheep and the application of limestone to the soil are the most influential parameters on the carbon footprint, indicating that they are key parameters for the development of GHG mitigation strategies, some of which were discussed in the article. • First life cycle assessment of integration between olive and sheep farming in Brazil. • The inventory was constructed with primary data collected in situ. • The study assesses the carbon footprint of the olive and sheep farming system. • The results show environmental advantages of the integration of olive and sheep farming systems. [ABSTRACT FROM AUTHOR]

After harvest, usually a significant amount of crop residues are available and part of it can be a feedstock for biomass energy. This paper's objective is to quantify sustainable residual biomass from corn harvesting, husk leaf, and cob, and its solid-fuel characterization for energy co-generation assessment. The methodology considers geographical distributions of corn production on a municipal basis, at Mato Grosso do Sul (MS) State in Brazil. Residual biomass from corn harvest has its mass fractions and energy characteristics determined via proximate and ultimate analyzes, bulk density, and heating values. Also, assessment of energy and power obtained by using co-generation power plants technology. The conclusions identify that residual biomass from corn harvest (husk leaf and cob) have large amounts available for energy use (~190.106 kg), and a main one geographic micro-region at MS. Its characteristics as solid fuel, i.e., HHV~18 MJ.kg-1 and TMC,db <10%, as well as high availability for oxygen for combustion (TO ~43%) and low ash content (TAsh < 4%) corroborate with its energy use, as in co-firing with sugarcane bagasse or coal thermal power plants. Bulk density values suggest densification as an interesting option for the commercialization of pellets or briquettes. Assessment of energy and power has a significant contribution to the Dourados micro-region (~2 GWElectric) and could attend farm and other rural energy demands through co-generation power plants. Altogether, husk leaf and cob are adding-values co-products that can play a significant role in the corn productive chain. [ABSTRACT FROM AUTHOR]

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

Natural resources in the current scenario of energy production and consumption demands appropriate management. Due to increasing urbanization worldwide, energy consumption optimization has become fundamental, including in the building sector. Many authors mention bioclimatic architecture as an intelligent way to achieve energy efficiency, reducing energy consumption while providing thermal comfort for the users. This paper aims to evaluate the energetic performance of a bioclimatic pilot plant in Bioclimatic zone 2. The methodology to evaluate the energetic performance of the bioclimatic building was developed from hygrothermal measurements and simulations in eQUEST version 3.65. The building was compared to its similar nonbioclimatic one by changing its envelope characteristics in the simulation. The results show the efficiency of bioclimatic strategies, which ensures the building's thermal and hygrometric stability, besides thermal comfort conditions most of the time. Consequently, these strategies allow one to reduce about 25% of the electric energy consumption compared to the nonbioclimatic building. [ABSTRACT FROM AUTHOR]

Abstract This paper is a multi-case study of three oil and gas projects in Brazil that investigates the tacit knowledge on how to best manage energy use during the construction phase. Twenty-two in-depth interviews form the primary evidence of the case studies. The methodology incorporates network analysis of concepts that influence energy use to formalize the case studies and collect additional knowledge. The framework comprises thirteen construction activities and twenty-one systems and factors related to design, resources and site characteristics, specific to the construction domain. This set of concepts can influence the energy consumption during the construction phase. Although these concepts are present in nearly every project, their dynamics and their inter-relations with project-specific systems change, depending on the context of the project. The proposed method, which employs network analysis, help identify their interrelationships and relative importance of these concepts. In addition to the opportunities and lessons learned that can guide future energy assessments and improve energy use in construction projects, the results show how promising network analytics is to obtain knowledge usually difficult to perceive in traditional knowledge processes. Besides, this work highlights the importance of supporting collaboration between project team members to study and decide on the plans to manage/reduce energy consumption during the construction phase in oil and gas facilities. While the energy savings are smaller than the upstream and operation phases, it has received much less attention. It is also a phase with much more subjective and contextualized knowledge – making the use of case-based analyses more suitable. Highlights • Supports collaboration in studying and planning energy use during the construction phase of O&G projects. • Three case studies are conducted supported by in-depth interviews. • Network analysis is used to represent construction activities, factors and systems. • The approach represents a meaningful way to formalize the knowledge in the cases. • Can help formalize the knowledge contained in a text case. [ABSTRACT FROM AUTHOR]

This paper focuses on an analysis of the energy mix profile and energy efficiency of the Brazilian dairy industry. It investigates dairies’ energy mix and energy efficiency and identifies some actions for a cleaner energy mix. Primary data from 37 dairy cheese-making establishments distributed among the Brazilian regions were obtained from online surveys. The results indicate that woodfuel plays a critical role, being the most used fuel in thermal energy generation, while diesel is dominant in electric generation. It also emphasizes that only 51% of the dairy establishments utilize electric energy generators. Other alternative biomass sources are still incipient in the sector, restricted to just 9.5% of the cases for thermal energy generation and no cases for electricity. Regarding the energy efficiency analysis, the results suggest dairies are more scale efficient than pure technical efficient. However, the dairies present a low energy efficiency level. There is no evidence that inefficiencies are differently distributed according to their size. These findings are important for government agencies, industry associations, scientists, universities and research institutes. High inefficiencies, regarding the use of electricity and thermal energy, are a key issue in sustainable bioenergy production. [ABSTRACT FROM AUTHOR]

Recognizing the need to increase energy efficiency across various sectors of the economy, the Brazilian government through the national energy efficiency plan (adopted in 2011) seeks to avoid 30 million tons of CO2 emissions through a reduction in electricity consumption of about 10% by 2030. Notwithstanding, we believe that a complete realization of such initiative will be dependent on the level of substitutability between different production factors and fuel types. Using the translog production function method, this paper, first provides insight on the economic impact of both factor and fuel inputs in Brazil. Most importantly, we examine the possibility of substitution among capital, labor, petroleum, and electricity. The ridge regression technique was used to estimate the parameters after our data depicted the existence of multicollinearity. Our results show that capital and labor are positively linked to output; however, petroleum and electricity contribute negatively to economic output in Brazil of which corruption is a plausible reason. Again, all variable inputs are substitutes with their relative technological progress depicting features of convergence. This implies that Brazil can achieve a cleaner energy system without much challenges due to the possibility of switching from greenhouse gas (GHG) emitting petroleum to a cleaner energy electricity. Our findings also highlight the importance of policies that advance merger of enterprises, and renewable energy use. [ABSTRACT FROM AUTHOR]

In this paper, a methodology was developed to analyze the results of energy efficiency programs used in the electricity distribution sector in Brazil. Analyzing the gains obtained through the investments made, and classifying which actions resulted in the best performance, contributed to decision making on the best allocation of investments to obtain the greatest energy savings. The Brazilian Energy Efficiency program was analyzed with a developed non-parametric model, using the data envelopment analysis method, and the categories of projects with better performance were determined. A database of the results from 1704 projects, from 2008 to 2016 in the Energy Efficiency program in Brazil's electricity distribution sector, was used. The results obtained show that the best performance was achieved by projects in the industrial and cogeneration categories; however, in Brazil these constitute only 4.24% of the projects presented and 5.28% of the total investments in the last eight years, indicating a need to review the regulatory strategies for energy efficiency in this country. [ABSTRACT FROM AUTHOR]

This paper explores the environmental impacts of a refrigerator recycling system in Brazil and quantifies its ecological advantages over primary resource production through a Life Cycle Assessment based on ISO 14040. The Life Cycle Assessment (LCA) accounts for savings of 720 kg CO 2 -eq per recycled refrigerator on average, thus demonstrating that an environmentally sound recycling process holds substantial emission reduction potential. In addition, the application of the recycling process in conjunction with collection and reverse logistics of refrigerators in a recycling system can reduce energy demand in Brazil by an average of 1.5 GJ per recycled unit. As a result, refrigerator recycling systems qualify as a valid and scalable energy efficiency measure in accordance with the Brazilian national framework for the evaluation of energy efficiency projects (PROPEE). The energy economics for projects based on recycling systems are shown for the 30 largest energy distributors in Brazil. Available budgets, calculated with diagnostics undertaken in conformity with the International Performance Measurement and Verification Protocol (IPMVP) for a threshold value (cost-benefit ratio = 0.8), clearly indicate the viability and competitiveness of these recycling systems as project opportunities for energy conservation under the national energy regulation agency ANEEL's energy efficiency program (PEE). [ABSTRACT FROM AUTHOR]

An emergent strategy to target sustainability involves an understanding of the water-energy interconnections. It takes water to produce energy, and it takes energy to source, treat, and distribute water. Sanitation and energy distribution are the world's pre-eminent development challenges. They are a magnifier of poverty, ill-health and mortality, and gender inequality. The word 'nexus' means the understanding of these interdependencies, tensions, and trade-offs. This paper investigates water-energy interconnections through critical analysis. We applied a bottom-up approach to describe Sao Paulo state's river basins, in Southeast Brazil, from the household to the regional scale. Initially, water availability, energy consumption and wastewater treatment data are inferred using per capita data. The subsequent data analysis then considered the positive and negative linkages among the basins. Local and state levels of dependency are also assessed, considering environmental and socioeconomic impacts. The significance of this paper lies in understanding these interconnections and their interdependence within the local context and individuals' behaviour. The results allow a comprehensive analysis of the water-energy Nexus across Sao Paulo state. The critical analysis also shows how the primary drivers contribute to urbanisation levels, population growth, and industrial activities. • Regional WE Nexus analysis is undertaken for the most industrialised state of Brazil. • The increase in wastewater treatment highlights the sustainability path. • Wastewater treatment growth rates are higher than population and GDP rates. • It develops a unique database for future studies using the Nexus approach. • Population growth, GDP, and industrial intensity are the main drivers in the WE Nexus. [ABSTRACT FROM AUTHOR]

• A top-down analysis of the non-residential building stock in Brazil is proposed. • The share of each building typology in the country energy consumption was assessed. • A statistical analysis showed variation of EUI according to type of building and region. • Reference values for the commercial building stock in Brazil is provided. This paper provides a pioneering, top-down analysis of the non-residential building stock in Brazil. A country-wide dataset was used, comprising 10,000 buildings divided into 12 typologies. Key indicators include floor area, energy use intensity, and operation patterns. In addition, predominant wall constructions, lighting systems, equipment, water heating, and HVAC systems for each typology were identified, including average density power for the systems. Data from the Brazilian Energy Balance report was correlated with the sample of buildings to determine the proportion of each building typology in the total energy consumption of the country, considering different energy sources. This study provides an innovative and up-to-date status of the characteristics of non-residential buildings in Brazil. A complementary statistical analysis addressed the correlation between energy use intensity and the building characteristics including an ANOVA test and a regression analysis in order to investigate the influence of the energy usage indicators (Lighting Power Density, Equipment Power Density, Cooling capacity) on the energy use intensity of each building typology in Brazil. This study is intended to serve as a reference for building characteristics and building energy performance in Brazil, and hence support diverse research regarding country-level building stock modeling, energy benchmarking, climate change impacts, and energy-efficiency related studies. [ABSTRACT FROM AUTHOR]

This paper estimates the Genuine Progress Indicator (GPI) for Brazil from 1970 to 2010 as an alternative indicator to the Gross Domestic Product (GDP). After a growing disparity between these two indicators in the 1980s, when Brazil's per capita GPI featured a 35% decline, there was a relative catch-up in per capita GPI, but not one sufficiently strong enough to reduce the historical GPI–GDP gap. The recent trend of rapidly increasing environmental and social costs, along with the decline in unpaid labour and infrastructure services, poses concerns about the sustainability of an increasing GPI for Brazil into the future. Policies aimed at reducing environmental costs are necessary if Brazil is to enjoy a sustainable pattern of non-declining economic welfare. Brazil must pursue a higher rate of productivity in material and energy consumption in order to keep environmental pressures to a minimum level. [ABSTRACT FROM AUTHOR]

• An overall panorama of the school buildings in Brazil was presented. • Energy billed data and survey evidence were used to build a statistical stock model. • The Weibull distribution was the most adequate to represent the continuous variables. • kWh/student·year was more suitable than kWh/m2·year to model Brazilian school buildings stock. • Thermal performance is an issue to be greatly improved in schools in Brazil. Understanding the actual conditions in school buildings regarding energy usage and environmental satisfaction gives way to the development of consistent building stock modelling. In the Brazilian context, a deep-rooted inefficient structure of energy management implies a lack of mapping the actual conditions of the public schools. Therefore, this paper aimed to assess the actual energy performance of school buildings in Brazil. This paper innovates by integrating questionnaire responses and billed energy data to allow the construction of an evidence-based stock model through a statistical approach, which was never performed for the Brazilian building stock. Moreover, the association of energy use intensity (EUI) with the core aspects of building characteristics, occupancy, environmental satisfaction and necessary improvements mapped the relevance of such aspects in energy usage patterns. Additionally, a discussion of the EUI indicators was addressed. Results showed an unprecedented broad panorama of the school building stock in Brazil and its main features. In terms of statistical analysis, the Weibull distribution was appropriate for representing the continuous variables of the stock. The EUI as a function of the number of students was more suitable than the floor-plan area to achieve a reliable energy performance analysis. Several tests were performed to assess if the energy management, environmental satisfaction and maintenance practices impacted the EUI or not. Finally, the main conclusion was that the stock model obtained was simple and fit the energy management structure in the Brazilian scenario. [ABSTRACT FROM AUTHOR]

Energy management has been considered in the global agenda as a way to improve energy performance and greenhouse gas reduction in organizations. Industries account for a significant part of energy use worldwide and present opportunities for energy efficiency improvements. Within the industry, energy management is a complex task, regarding scenarios with variables related to the following perspectives: economics, contingency, technological change and behavioural. This paper aims at analyzing the influencing factors on energy management in industries from these perspectives. A survey with 40 variables was carried out with middle managers from different industrial sectors in Brazil. The variables were divided into three groups: drivers for investments in energy efficiency; organizational processes and actions in energy management; involvement of middle managers. Initially, an exploratory factor analysis technique was employed aiming at specifying the main factors influencing energy management. In the sequence , a confirmatory factor analysis was used to associate the variables to the main factors as well as to know how the factors relate to each other. The study showed a positive correlation among all the factors identified. Statistical tests suggested that the factors could not be explained separately. Hypotheses tests were applied to verify the influence of the factors among the groups surveyed. The final model comprised eight factors into the three groups: organizational (strategic , operational), involvement (motivation, support), drivers (production, economics , competitiveness, environment). The results and the main implications of the study are discussed in the paper. • The main factors on energy management presented a positive correlation among them. • Middle managers' involvement influenced strategy and operation on energy management. • Middle managers' involvement depended on the support and motivation from superiors. • Operational factor on energy management influenced drivers for energy efficiency. • Production process was a driver for energy efficiency influencing energy management. [ABSTRACT FROM AUTHOR]

• An innovative framework for energy benchmarking of buildings is proposed. • Entropy and cluster analysis reduced uncertainty in setting parameters of archetypes. • The machine learning technique used as a benchmarking tool showed high performance. • The benchmarking applied in actual buildings showed a high inefficiency ratio. Energy benchmarking of buildings has an important role in improving energy performance by establishing a reference for the energy efficiency of the building stock. The simulation of archetypes followed by a generalisation model has been widely used to obtain benchmarks. However, even though archetypes summarise the main features of the building stock, the uncertainties must be accounted for in the modelling process. Moreover, testing the response of the benchmarking model using the actual building stock data supports the reliability of the method. This paper aims to propose an innovative framework to reduce the uncertainty of archetypes for benchmarking buildings. A standard framework for data compiling is proposed and an assessment of the uncertainty of variables using entropy and cluster analysis allowed to obtain representative archetypes. An Artificial Neural Network (ANN) was used as a predictive tool, and it was applied to benchmark a sample of actual buildings. Also, the simulation outcomes were used to determine energy end-uses according to the climatic zones. The framework proposition is presented alongside with a practical application. The result is an unprecedented benchmarking model: the archetype considers more variation in parameters of the building stock with higher uncertainties. Additionally, the modelling process showed to be robust for combining different datasets, and the ANN achieved high-performance metrics. Conclusion indicates the potential of using the framework for other typologies. Moreover, the framework demonstration is used for a school stock in Brazil, showing a trend to the inefficiency while a specific case study was explored, showing the potential of the method to find faults in the building energy use. [ABSTRACT FROM AUTHOR]

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. • An efficient Multi-objective Evolutionary Swarm Hybrid algorithm is proposed. • Development of a nonlinear model to operational control of Hydro-power plants. • Hydro-power plant data regression obtains the maximum efficiency of the power units. • Efficiency energy goals achieved an increasing the profit in the energy production. [ABSTRACT FROM AUTHOR]

The city of Aripuana is one of the largest wood producer in the state of Mato Grosso, Brazil. Wood residues are used in the electricity generation at three thermoelectric plants in this region. However, the plants have high costs in transporting the wood residues (due to poor road conditions). Hence, this paper compares the energy performance of wood residues in natura and compacted as briquettes by calculating the heating value and determining the influence of moisture content on the energy characteristics of wood residues. The goal is to demonstrate the viability of using briquettes in order to improve thermoelectric generation. The wood residues from this region are affected by the high humidity of the biome. An alternative to improve the use of energy contained in the wood residues is to produce briquettes with lower humidity. This allows one to maintain high levels of heat energy in a lower volume, facilitating handling and storage. The results show that the use of briquettes improved the performance of thermoelectric plants, generating 1 MW of electricity power with less than 1 ton of briquettes. This contributes to the preservation of the environment, reducing operating costs, transportation and storage of the raw materials. [ABSTRACT FROM AUTHOR]

There is consensus that the use of renewable energy must be expanded. More and more restrictions on the use of fossil derived fuels may open the way for an increased consumption of biomass event those so far not perceived as feasible, such some biomass residue. Energy derived from biomass residues may provide even more benefits because it may replace other sources of higher-grade energy while providing proper destination for residues. Biomass from pruning residue has been overlooked as an energy resource due to many limitations. This paper addresses the issue and the means for energy valuation of pruning residues while provides final destination for them. It presents a framework to overcome most of today's challenges across the world. A case study was carried out to determine the main constraints and how they can influence the decision-making process. The variety of urban pruning residues and their rate of generation, handling, storage, logistics, economic and environmental issues, institutional regulations for a small city in Southern Brazil were taken into consideration in the analysis. Pruning residues were sampled in the field, then handled, shredded, dried, specified, stored and burnt in a concurrent woodburning furnace for testing. The diagnostics of the constraints and the lab tests were summarized and organized in a framework that aimed to assess the energy valuation of pruning residues. Results show that with proper assessment tools, projects, biomass analyses and burning technology energy valuation of pruning residues can be feasible. • Energy valuation of biomass pruning residues is feasible given proper conditions. • A novel assessment framework provides routes to overcome most of today's challenges. • A case study of energy valuation of urban pruning biomass residue in Brazil showed feasible to generate 1 to 1.5 MWe. • Information and data of bulk density, moisture content, heating value, of residues of five urban tree species. • Estimate of opportunity costs of disposal of biomass pruning residues in landfills. [ABSTRACT FROM AUTHOR]

The energy performance of 11 bank branches in the Brazilian city of Curitiba was analysed using bottom-up engineering energy benchmarking method. As part of a previous study to classify bank buildings, these bank branches were selected for a further study with their building parametric and energy-related data obtained from building drawings and specifications as well as on-site surveys. The energy assessment of these branches was conducted using the existing (RTQ-C) and proposed (INI-C) Brazilian energy efficient labelling regulations for non-domestic buildings. The ability of both systems to produce results which are compatible and could be used to reflect the actual building energy consumption was examined. The study shows improvement of the INI-C system with calculation of energy consumption values and conditions for different reference buildings. However, performance gap between the calculated and actual energy consumption was found due to various reasons including reference values for office buildings were inaccurate for bank buildings. Hence, a list of modified reference values was proposed for bank buildings to represent bank buildings more accurately. This paper highlights the strengths and limitations of both the existing and proposed regulations and presents a reality check against the calculations of energy ratings and actual energy consumption of the buildings. It was concluded that the existing and proposed regulations have to be improved significantly in terms of calculation methodology in order to meet the demand of future energy policy in Brazil. [ABSTRACT FROM AUTHOR]

Over the years, electricity consumption behavior in Brazil has been analyzed due to financial and social problems. In this context, it is important to simulate energy prices of the energy efficiency auctions in the Brazilian electricity market. The Markov Chain Monte Carlo (MCMC) method generated simulations; thus, several samples were generated with different sizes. It is possible to say that the larger the sample, the better the approximation to the original data. Then, the Kernel method and the Gaussian mixture model used to estimate the density distribution of energy price, and the MCMC method were crucial in providing approximations of the original data and clearly analyzing its impact. Next, the behavior of the data in each histogram was observed with 500, 1000, 5000 and 10,000 samples, considering only one scenario. The sample which best approximates the original data in accordance with the generated histograms is the 10,000th sample, which consistently follows the behavior of the data. Therefore, this paper presents an approach to generate samples of auction energy prices in the energy efficiency market, using the MCMC method through the Metropolis–Hastings algorithm. The results show that this approach can be used to generate energy price samples. [ABSTRACT FROM AUTHOR]

• General framework for the optimal net present value of smart appliance investment. • A Tree Algorithm with Interior Point speedup the framework in days. • Forty important economic cities in Brazil taken as case studies using real data. • Payback and return on investment measures are not enough for economic analysis. • Full set of results available online. Smart homes allow optimized energy usage, allowing households to reduce electricity bills or even make profits. By 2020, 20% of all households in Europe will be expected to become smart homes. Although smart homes seem to be the future for homes, many customers have the perception that a transition from current homes to smart ones is unprofitable. Adopting a smart home concept requires investments for which the households desire a positive return. A question in this context is the following: for a given household, when and/or what set of home appliances/technologies should be acquired so that the investment made by householder has a positive financial return? The available tool to answer that question can be time-consuming from a practical perspective. Based on our previous work, this paper proposes a framework to help the transition from current houses to smart homes considering customized electricity usage and economic measures. A tree algorithm is developed to decrease the time needed by an economic analysis of each possible acquisition combination of smart appliances or equipment for a given user. The proposed framework is tested on 40 cases covering all Brazilian capital cities, whose results are available online and may be used directly as an approximation for economic analyses. An example of one case is described in detail. Results show that the proposed tree algorithm is able to reduce days of CPU time to solve the problem and Net Present Value should be used as an economic measure to answer the aforementioned question. [ABSTRACT FROM AUTHOR]

Brazil, an emerging economy with more than 208 million inhabitants, is currently one of the biggest consumers of disposable diapers worldwide, generating huge waste streams to sanitary landfills or dumps. This take-make-dispose system causes high material and fuel consumption and serious environmental impacts. The concept of the Circular Economy, promoting that material flows should be circular, material and energy efficiency should be maximized and residues, as far as possible, designed out, has continuously gained attention over the last decade. For a Circular Economy, technological innovation, new business models and changes in consumer behavior are needed. The present study compares the use of modern cloth diapers and disposable diapers via a screening Life Cycle Assessment, checking conventional and innovative circular business models for the application of modern cloth diapers, such as the leasing or pay-per-service models, while focusing on the regional context of Brazil. Results indicate that cloth diapers show generally a better environmental performance than disposable diapers, whose poor performance is mainly due to their end-of-life treatment in sanitary or unsanitary landfill, provoking high greenhouse gas emissions. For cloth diapers, the most advantageous product system consists in a diaper-as-service model at large scale, using efficient continuous batch washers for sanitizing the diapers. Diaper-as-service models at smaller scale in so-called on-premise laundries show poorer environmental performance, due to higher water and energy needs. Domestic laundering of diapers is a good alternative regarding its environmental performance, because of its low energy consumption. However, consumers accustomed with disposable diapers might be reluctant to engage with this additional domestic labor. Image 1 • Disposable diapers hold bad environmental performance due to disposal in landfills. • Average conditions in Brazil lead to lower environmental impacts for cloth diapers. • Product-as-service-models should be used only with highly efficient batch washers. • The cotton used in absorbents of cloth diapers has significant influence on impacts. • Cloth diapers may be a bad choice or Brazil's regions with water scarcity. [ABSTRACT FROM AUTHOR]

Most European countries have committed to ambitious emissions reduction goals. Energy generation in particular is responsible for more than 30% of global emissions, where significant focus has been placed on renewable energy generation, including biomass. On the one hand, there are countries, like the UK, where the biomass stock is insufficient to meet the demand; on the other hand, there are countries, like Brazil, where the stock significantly exceeds the demand. To promote a natural symbiosis, it is necessary to take on the challenge of transporting biomass through long distances in an environmentally and economically efficient manner. This paper aims to assess the efficiency of alternative pathways of international biomass supply-chains. The alternatives involve different biomass origin regions, transportation modes, export ports and processing technologies, including torrefaction. Data Envelopment Analysis (DEA) has been used for the first time to assess the efficiency of the alternative biomass supply chain pathways in a Latin American context, considering multiple-criteria relating to economic and environmental performance simultaneously, such as the biomass delivered cost, the environmental impact and the fossil energy consumption. Additionally, a sensitivity analysis was performed to analyse the robustness of the results under uncertainty in parameter values. The DEA approach presented can assist the process of planning biomass sourcing and improve decision-making under multiple decision criteria. The results can support medium- and long-term strategic decisions for decision- and policy-makers. • Efficiency assessment of biomass supply chain pathways between Brazil and the UK. • 56 alternative pathways considered with different supply chain structure. • Data Envelopment Analysis used for multi-criteria efficiency assessment. • Results can support decision and policy making. • Trade-offs between different criteria identified. [ABSTRACT FROM AUTHOR]