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In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid electric vehicle, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICE) and the electric motor. The study described herein also presents the influence of factors such as the ambient temperature, vehicle speed and traffic distance on the PHEV's energy consumption. It has been shown that the vehicle's range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in everyday driving conditions. When analyzing the energy consumption based on the vehicle's unitary energy consumption model, attention was paid to values that are strongly correlated with traffic and weather conditions. In addition, the authors emphasized that the estimated total energy consumption of a battery electric vehicle (BEV) or hybrid vehicle (PHEV), relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. As shown in this paper, the energy consumption intensity of a vehicle is significantly influenced not only by its speed but also by weather conditions, including ambient temperature. In extreme cases, energy consumption intensity can increase by up to 68% relative to a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) cycle. [ABSTRACT FROM AUTHOR]

Prefabricated temporary buildings are a promising solution for post-disaster scenarios for their modularity, sustainability and transportation advantages. However, their low thermal mass building envelope shows a fast response to heat flux excitations. This leads to the risk of not meeting the occupant comfort and HVAC energy-saving requirements. The literature shows different measures implementable in opaque surfaces, like vacuum insulation panels (VIPs), phase change materials (PCMs) and switchable coatings, and in transparent surfaces (switchable glazing) to mitigate thermal issues, like overheating, while preserving the limited available internal space. This paper investigates the energy and overheating performance of the mentioned interventions by using building performance simulation tools to assess their effectiveness. The optimization also looks at the transportation flexibility of each intervention to better support the decision maker for manufacturing innovative temporary units. The most energy-efficient measures turn to be VIPs as a better energy solution for winter and PCMs as a better thermal comfort solution for summer. [ABSTRACT FROM AUTHOR]

Uncertainties related to the energy produced and consumed in smart grids, especially in microgrids, are among the major issues for both their design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. The intensive development and massive installation of smart meters will help to better characterize local energy consumption and production in the following years. However, models representing these variables over large timescales are essential for microgrid design. In this paper, we explore a simple method based on Markov chains capable of generating a large number of probabilistic production or consumption profiles from available historical measurements. We show that the developed approach can capture the main characteristics and statistical variability of real data on both short-term and long-term scales. Moreover, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements. [ABSTRACT FROM AUTHOR]

This paper investigates relative aggregate energy efficiency for a panel of 39 developing countries by econometrically estimating an energy-demand function (EDF) using the stochastic frontier analysis (SFA) approach to provide relative energy efficiency scores over the period 1989 to 2008. Energy efficiency is arguably difficult to define or even conceptualise with several interpretations in the literature but here it is based on an economists' perspective of efficiency. Hence, the estimates of 'true' energy efficiency found in the paper using this approach approximate the economically efficient use of energy capturing both technical and allocative efficiency and the results confirm that energy intensity should not be considered as a de facto standard indicator of energy efficiency. While, by controlling for a range of socio-economic factors, the measurements of energy efficiency obtained by the analysis are deemed more appropriate and hence it is argued that this analysis should be undertaken to avoid potentially misleading advice to policy makers. This study contributes to the literature since it is, as far as is known, the first attempt to apply the benchmarking parametric stochastic frontier technique to econometrically estimate energy efficiency for a large panel of only developing counties around the world. Moreover, the results from such analysis are arguably particularly relevant in a world dominated by environmental concerns, especially in the aftermath of energy price increase as a result of the unrest in Ukraine. [ABSTRACT FROM AUTHOR]

The growth of renewable energy actively takes part in decarbonizing the fossil-fuel-based energy system. It reduces carbon emissions, carbon footprint, and greenhouse gas emissions and increases clean energy. The usage of renewable resources reduces and solves several problems, such as increasing temperature, carbon footprint, greenhouse gas emissions, and energy waste. Every sector contributes to increasing the above-mentioned factors in the environment. One of the main reasons for this biodegradation and climate change is energy resources. Using renewable energy instead of fossil fuel can solve the problem. This paper aims to find open research problems about the application of renewable energy and to initiate new innovative ideas regarding renewable energy. A detailed state of the art includes trends for renewable energy resources, their theoretical evolution, and practical implementations. Methodologies used for decision analysis in renewable energy are discussed in detail. The time frame for this analysis of renewable energy is 2010 to >2022. An extensive literature review finds a huge research scope in applying renewable energy in other research, such as logistics, smart production management, and advanced inventory management. Then, major changes in the profit/cost of that system due to renewable energy can be analyzed. This research proposes some innovative new ideas related cost formulas for renewable energy for the corresponding open problems. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical programming approach for the strategic planning of hydrogen production from renewable energies and its use in electric power generation in conventional technologies. The proposed approach aims to determine the optimal selection of the different types of technologies, electrolyzers and storage units (energy and hydrogen). The approach considers the implementation of an optimization methodology to select a representative data set that characterizes the total annual demand. The economic objective aims to determine the minimum cost, which is composed of the capital costs in the acquisition of units, operating costs of such units, costs of production and transmission of energy, as well as the cost associated with the emissions generated, which is related to an environmental tax. A specific case study is presented in the Mexican peninsula and the results show that it is possible to produce hydrogen at a minimum sale price of 4200 $/tonH2, with a total cost of $5.1687 × 106 and 2.5243 × 105 tonCO2eq. In addition, the financial break-even point corresponds to a sale price of 6600 $/tonH2. The proposed model determines the trade-offs between the cost and the emissions generated. [ABSTRACT FROM AUTHOR]

Several rural regions located in Africa are experiencing recurrent and even permanent problems in terms of energy production, supply, and distribution to citizens. This study was conducted to investigate the relevance of the use of a new solar technology that is gradually responding in Europe and in industrialized countries. It is about the use of hybrid photovoltaic thermal (PV/T) solar panels that co-produce electricity and hot water for local use. Furthermore, in Africa, local use of solar energy can provide a share in the energy mix. This work is motivated by the lack of studies on these hybrid solar panels in tropical climates. Hence, the paper examines the potential for integration of these systems in small households. A complete PV/T system consisting of solar panels, pump, storage tank, batteries, and controllers was tested and calibrated by using the TRNSYS simulation tool. A comparative study could thus be carried out for the performance of PV/T in a tropical climate (case of the city of Koua in the Comoros) to its performance in Mediterranean and continental climates (Marseille in the south and Longwy in the northeast of France). The results quantify the performance of the PV/T in the three climates and show that the performance in the town of Koua is 44% to 54% higher than in European climates. It can be concluded from this study that the Comorian market and more generally the sub-Saharan market for PVT systems has a good potential for development. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze energy pathways for a fossil fuel rich developing country towards an energy transition considering national and international oil availability using Ecuador as a reference. An integrated assessment model has been developed to simulate energy transition scenarios considering constraints on oil availability at the national and global level. Results show that if current trends in energy demand and supply persist, energy scarcity would start around 2040 due to depletion of national oil reserves and restricted access to foreign oil. This would trigger a degrowth in economic activity in sectors with high dependency on petroleum products. Scenarios with conservative efforts might partially revert the increasing use of fossil fuels supported by policies for energy efficiency and substitution of liquid fuels with electricity mainly from renewables. However, energy shortages would still be foreseeable as well as a decay of the economy. Under a maximum efforts scenario with an optimistic availability of national oil, a moderate-sustained economic growth could be feasible. This shows that oil would still play a key role during the transition. Furthermore, ambitious policies must be implemented in the short term to smooth the effects of displacing oil as energy and income source. [ABSTRACT FROM AUTHOR]

A Solution Validation involves comparing the data obtained from the system that are implemented following the model recommendations, as well as the model results. This paper presents a Solution Validation that has been performed with the aim of certifying that a set of computer-optimized designs, for a double façade, are consistent with reality. To validate the results obtained through simulation models, based on dynamic thermal calculation and using Computational Fluid Dynamic techniques, a comparison with the data obtained by monitoring a real implemented prototype has been carried out. The new validated model can be used to describe the system thermal behavior in different climatic zones without having to build a new prototype. The good performance of the proposed double façade solution is confirmed since the validation assures there is a considerable energy saving, preserving and even improving interior comfort. This work shows all the processes in the Solution Validation depicting some of the problems we faced and represents an example of this kind of validation that often is not considered in a simulation project. [ABSTRACT FROM AUTHOR]

In recent years, anaerobic membrane bioreactors (AnMBRs) technology, a combination of a biological reactor and a selective membrane process, has received increasing attention from both industrialists and researchers. Undoubtedly, this is due to the fact that AnMBRs demonstrate several unique advantages. Firstly, this paper addresses fundamentals of the AnMBRs technology and subsequently provides an overview of the current state-of-the art in the municipal and domestic wastewaters treatment by AnMBRs. Since the operating conditions play a key role in further AnMBRs development, the impact of temperature and hydraulic retention time (HRT) on the AnMBRs performance in terms of organic matters removal is presented in detail. Although membrane technologies for wastewaters treatment are known as costly in operation, it was clearly demonstrated that the energy demand of AnMBRs may be lower than that of typical wastewater treatment plants (WWTPs). Moreover, it was indicated that AnMBRs have the potential to be a net energy producer. Consequently, this work builds on a growing body of evidence linking wastewaters treatment with the energy-efficient AnMBRs technology. Finally, the challenges and perspectives related to the full-scale implementation of AnMBRs are highlighted. [ABSTRACT FROM AUTHOR]

Several socioeconomic factors such as industrialization, population growth, evolution of modern technologies, urbanization and other social activities do heavily influence the increase in energy demand. A thorough understanding of the effects of energy demand to power grid is highly essential for effective planning and operation of a power system network in terms of the available generation and transmission line capacities. This paper presents an optimal power flow (OPF) with the aim to determine the exact nodes through which the network capacities can be increased. The problem is formulated as a Direct Current (DC) OPF model, which is a linearized version of an Alternating Current (AC) OPF model. The DC-OPF model was solved as a single period OPF problem. The model was tested in several case studies using the topology of the IEEE test systems, and the computation speeds of the different cases were compared. The results suggested dual variables of the problem's constraints as an extra tool for the network designer to see where to increase the network capacities. [ABSTRACT FROM AUTHOR]

Last mile is known as the last leg of the delivery process, which is the most expensive and time-consuming part per kilometer. The most common last mile deliveries are postal packages, groceries and take away meals. Recently, there has been a growing interest and implementation of sidewalk autonomous delivery robots. These robots travel roughly at the speed of pedestrians (6 km/h). A high-speed (15 km/h) delivery robot design is proposed for reducing the time, energy and carbon footprint of deliveries. The preliminary design of the delivery robot powertrain is based on worst-case scenario analysis and Monte Carlo simulations with synthetic driving cycles. Synthetic driving cycles were used because there are no open data available of delivery robots. Thus, the procedure presented in this paper is a general approach for cases where there is no precedent application and/or no data are available. The synthetic driving cycles are based on start and end location of food-delivery services in Helsinki, Finland. Based on the simulations, the crucial factors contributing to energy demand and its variation were analyzed. Carbon footprint of the delivered package over distance of the design is compared to existing wheeled delivery robots and quadrupeds. The motivation of the work is that showcasing the energy savings of higher speed aids government officials in their decision-making regarding delivery robot regulations. As a result of the simulations, higher operation speed lowered the energy consumption by over 40%. [ABSTRACT FROM AUTHOR]

(1) Background: The present paper aims at estimating the quality of the forecasts obtained by using one equation models. In particular, the focus is on the effect that the explanatory variables have on the forecasted quantity. The analysis is performed on the long term natural gas consumption in the Italian residential sector, but the same methodology can be applied to other contexts; (2) Methods: Different ex ante knowledge scenarios are built by associating different levels of confidence to the same set of explanatory variables. Forecasting results, coming from a standard regression algorithm and confirmed by a Kalman filter, are analyzed by means of covariance matrix propagation to assess the quality of the provided estimates; (3) Results: The outcomes show that one-equation models are very sensitive to the quality of the explanatory variables, therefore their erroneous estimation may have a relevant detrimental effect on the predictive accuracy of the model; (4) Conclusions: The overall ex ante forecasting accuracy of an example of one equation model is assessed. It has emerged that long-term forecasts need particular attention when the covered time horizon spans over decades. The information contained in the present paper is of interest for energy planners, supply network managers and policy makers in order to support their decisions. [ABSTRACT FROM AUTHOR]

The provision of energy infrastructure is essential for economic growth, social cohesion, and environmental sustainability. Understanding the multiple functions and services it provides us requires firstly a deeper understanding of the factors that influence energy infrastructure itself. This paper focusses on the factors that influence the evolution of energy infrastructure in Nigeria. By studying different eras of energy use according to the technologies that were being implemented, resources that were available, and the political practice of the time it is possible to better frame the drivers of energy infrastructure. The paper explores the transitions of how Nigerians managed to obtain the vast majority of energy from food calories and traditional biomass, to the broad portfolio of energy sources that is in use today. [ABSTRACT FROM AUTHOR]

Location is crucial when it comes to reducing the energy demand of buildings. Deterministic mathematical models of the energy demand of a single-family building were developed for the cities of Wrocław and Suwałki, representing the mild and severe climatic conditions of Poland, respectively, and compared with energy demand for Białystok, representing medium conditions. Models include the windows area, heat transfer coefficient, solar radiation transmittance of glazing, and orientation of windows. For medium conditions (Białystok), the energy demand is 18.3% higher than for mild conditions (Wrocław) and 7.3% lower than for severe climate conditions (Suwałki). Location does not influence the nature of the effect of the factors on energy demand, which increases with an increase in heat transfer coefficient and a decrease in window area, glazing solar radiation transmittance, and orientation change from north to south. The large impact of solar gains was proved. The optimisation procedure was performed and mathematical descriptions of recommended parameters were created to ensure the equivalent energy efficiency of windows for each orientation and location. For Bialystok, north-facing windows can have an area 1.32 times larger and south-facing windows 1.48 times smaller than east-facing windows to ensure a building's energy demand remains constant. [ABSTRACT FROM AUTHOR]

There is a growing interest in the application of microgrids around the world because of their potential for achieving a flexible, reliable, efficient and smart electrical grid system and supplying energy to off-grid communities, including their economic benefits. Several research studies have examined the application issues of microgrids. However, a lack of in-depth considerations for the enabling planning conditions has been identified as a major reason why microgrids fail in several off-grid communities. This development requires research efforts that consider better strategies and framework for sustainable microgrids in remote communities. This paper first presents a comprehensive review of microgrid technologies and their applications. It then proposes the STEEP model to examine critically the failure factors based on the social, technical, economic, environmental and policy (STEEP) perspectives. The model details the key dimensions and actions necessary for addressing the challenge of microgrid failure in remote communities. The study uses remote communities within Nigeria, West Africa, as case studies and demonstrates the need for the STEEP approach for better understanding of microgrid planning and development. Better insights into microgrid systems are expected to address the drawbacks and improve the situation that can lead to widespread and sustainable applications in off-grid communities around the world in the future. The paper introduces the sustainable planning framework (SPF) based on the STEEP model, which can form a general basis for planning microgrids in any remote location. [ABSTRACT FROM AUTHOR]

In recent years, due to the global energy crisis, the idea of a photovoltaic-assisted air-source heat pump (PV-ASHP) has become increasingly popular. This study provides a simulation in TRNSYS and the analysis of the use of a PV-ASHP system in a service building in different climate zones of Poland. For each of the six cities—Kolobrzeg, Poznan, Krakow, Warsaw, Mikolajki, and Suwalki, the effect of changing five system parameters (area, efficiency, type, and location of photovoltaic panels, and the use of a heat pump control strategy) on the amount of energy generated and consumed was determined. We also estimated the extent to which the photovoltaic panels could cover the energy requirements for the heat pump (HP) operation and the system could provide thermal comfort in the service room. Finally, a simplified analysis of the operating costs and capital expenditures was made. The results highlighted the issue of the incoherence of renewable energy sources and the need to store surplus energy under Polish climatic conditions. Abandoning the HP control strategy increased energy consumption by 36–62%, depending on the location and Variant, while the change in the place of the PV panels on the roof slope reduced energy generation by 16–22%. When applied to an ASHP in a service building, the use of PV panels to power it seems to be cost-effective. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability. [ABSTRACT FROM AUTHOR]

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye's energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations' Sustainable Development Goals (SDGs). [ABSTRACT FROM AUTHOR]

The aim of this study was to use landscape grass from species-rich orchards for biogas production, thus preserving these very valuable areas for future generations. Since these grass clippings have high lignocellulose content, the substrate has to be pretreated before being fed into the biogas digester. In this study, three different mechanical treatment processes (cross-flow grinder, ball mill and a mounted mower) were investigated and compared with untreated grass clippings. Chemical composition, specific methane yield, degradation kinetics and microscopic images were analyzed. In order to derive recommendations, the harvesting and pretreatment processes were examined in terms of energy demand, additional methane yield, and suitability of the substrate for use in biogas plants, taking into account conservation aspects. Within the pretreatment process, ball milling leads to the highest significant increase in specific methane yield of up to 5.8% and the fastest gas formation kinetics (lag time λBM: 0.01 ± 0.0 d; duration to reach half of total gas production ½M(x)BM: 5.4 ± 0.2 d) compared to the untreated variant (λUT: 1.02 ± 0.2 d; ½M(x)UT: 6.5 ± 0.2 d). A comparison of the energy required for the mechanical disintegration of the substrates with the increased yield of methane during the digestion process shows that the mechanical processing of these substrates appears to be useful. A positive energy balance was achieved for the cross-flow grinder (12.3 kWh tVS−1) and the ball mill (21.4 kWh tVS−1), while the Amazone Grasshopper left a negative balance (−18.3 kWh tVS−1), requiring more energy for substrate pretreatment than was generated as methane surplus. In summary, the pretreatment of landscape management grass is a suitable approach for utilizing agricultural residues efficiently in a biogas plant and thus contributing to sustainable energy production. [ABSTRACT FROM AUTHOR]

Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002–2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file. [ABSTRACT FROM AUTHOR]

Under the background of Chinese Rural Revitalization Strategy, how to improve rural regional environment and living quality is very important and urgent. At present, residential buildings in gully regions of the Loess Plateau have poor insulation and high-energy consumption. Thus, better ecological design can largely save energy and improve living comfort. The findings of this paper provide an insight into the ecological design potentials for reducing energy demand across rural regions in China. In this paper, we select three main types of residential buildings in gully regions and build energy demand models based on the Life Cycle Assessment (LCA) method. The results show that the energy demand in the building use stage is extremely high in all three typical buildings, which account for around 90% of the whole life cycle. The energy demand of the traditional adobe residential building is lower than the brick-concrete structure buildings. The LCA method used in this paper can quantify the energy demand in each stage of life cycle, which helps to put forward the corresponding ecological design strategy. The research results can be used as a reference in the future development of this region and other rural regions in China. [ABSTRACT FROM AUTHOR]

In the present paper, an investigation into Thailand's energy demand is performed to determine if: (1) a linear or nonlinear Engel curve better explains the relationship between income and energy consumption, and (2) systems with pre-commitments better model energy consumptions. Four demand systems are estimated: an almost ideal demand system (AIDS), the quadratic almost ideal demand system (QAIDS), generalized almost ideal demand system (GAIDS), and the generalized quadratic almost ideal demand system (GQAIDS). Elasticities are calculated for policy implications. The empirical results suggest that models considering pre-commitments and nonlinear Engel curves may be slightly more appropriate for Thailand's energy system, from both statistic and economic standpoints. Statistical inferences appear to favor the GQAIDS model based on the encompassing results. Economic reasonability also appears to favor the GQAIDS model, in particular, petroleum products, as it provides results consistent with the notions of precommitments and fuel substitutability found in previous studies. Most of the previous studies in various forms have shown that the demand for petroleum products is relatively inelastic to price in Thailand. The current study, however, finds that own-price elasticities of uncompensated demand for petroleum products are almost unitary, which is relatively more elastic than most of the previous studies. As such, further studies are required and the price-based policy on petroleum products targeting the reduction in petroleum product dependence must be implemented with caution. [ABSTRACT FROM AUTHOR]

Energy consumption in residential, commercial and industrial buildings is one of the major contributors to global warming. Due to the increase in the latter, and growing global energy crisis, more attention is being paid to renewable energy resources (RES). The use of innovative concepts in existing buildings is gaining popularity to provide reduction in energy requirements for electricity, heating and cooling. In this paper, an electricity, heating and cooling cooperation mechanism among neighboring buildings with RES is proposed. It relies on adjusting the RES tariff with a mutual agreement between the neighboring buildings, with an aim to minimize the operational costs. For this purpose, a mathematical model is developed for joint energy cooperation, where surplus energy in one of the buildings is shared with others, thereby reducing dependency on the grid. The optimization structure of the environment friendly energy cooperation is nonlinear, which is linearized using the McCormick envelopes. A scenario for the city of Islamabad, Pakistan, is considered by utilizing its environmental data obtained from public domain websites. The simulation results show more than twenty percent energy cost savings with the proposed cooperation model. [ABSTRACT FROM AUTHOR]

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes were collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data were collected, the variables available and the representativeness of the sample compared to national estimates. It is intended to be a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it. [ABSTRACT FROM AUTHOR]

Understanding of energy consumption patterns is extremely important for optimization of resources and application of green trends. Traditionally, analyses were performed for large environments like regions and nations. However, with the advent of Smart Grids, the study of the behavior of smaller environments has become a necessity to allow a deeper micromanagement of the energy grid. This paper presents a data processing system to analyze energy consumption patterns in industrial parks, based on the cascade application of a Self-Organizing Map (SOM) and the clustering k-means algorithm. The system is validated with real load data from an industrial park in Spain. The validation results show that the system adequately finds different behavior patterns which are meaningful, and is capable of doing so without supervision, and without any prior knowledge about the data. [ABSTRACT FROM AUTHOR]

Upcycling presents one of many opportunities for reducing consumption of materials and energy. Despite recent growth evidenced by increasing numbers of practitioners and businesses based on upcycling, it remains a niche activity and requires scaling up to realise its potential benefits. This paper investigates UK household upcycling in order to develop interventions for scaling up upcycling in the UK. Mixed methods were used in four stages: (a) Interviews to gain insights into UK upcycling; (b) a survey to discover key factors influencing UK upcycling; (c) intervention development based on the synthesis of interviews and survey; and (d) use of a semi-Delphi technique to evaluate and develop initial interventions. The results showed approaches to upcycling (e.g., wood, metal and fabric as frequently used materials, online platforms as frequently used source of materials), context for upcycling (e.g., predominant use of home for upcycling), factors influencing UK upcycling with key determinants (i.e., intention, attitude and subjective norm), important demographic characteristics considering a target audience for interventions (i.e., 30+ females) and prioritised interventions for scaling up (e.g., TV and inspirational media and community workshops as short-term high priority interventions). The paper further discusses implications of the study in terms of development of theory and practice of upcycling. [ABSTRACT FROM AUTHOR]

An overview of the literature considering committed quantities in demand estimation for various commodities with an emphasis on energy commodities is presented. This overview provides a definition and the history of committed quantities, along with different theoretical modeling methodologies. Committed quantities are quantities that are consumed in the short run with little regard for price. Previous studies suggest that committed quantities for various commodities range from 15 to 98% of consumption. The inclusion of committed quantities appears to improve estimates generally, but it is not clear-cut. Problems arise when estimated committed quantities are negative or larger than the consumption amount. This review concludes with a recommendation that further research is necessary to resolve such issues, provide an improved understanding of the committed quantities in estimation, and fill in knowledge gaps concerning committed quantities ranging from theoretical to practical issues. [ABSTRACT FROM AUTHOR]

South Africa's energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa's coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied. [ABSTRACT FROM AUTHOR]

Improving the energy performance of existing buildings is one of the main strategies defined by the European Union to reduce global energy costs. Amongst the actions to be carried out in buildings to achieve this objective is working with passive measures adapted to each type of climate. To assist designers in the process of finding appropriate solutions for each building and location, different tools have been developed and since the implementation of building information modeling (BIM), it has been possible to perform an analysis of a building's life cycle from an energy perspective and other types of analysis such as a comfort analysis. In the case of Spain, the first BIM environment tool has been implemented that deals with the global analysis of a building's behavior and serves as an alternative to previous methods characterized by their lack of both flexibility and information offered to designers. This paper evaluates and compares the official Spanish energy performance evaluation tool (Cypetherm) released in 2018 using a case study involving the installation of sunlight control devices as part of a building refurbishment. It is intended to determine how databases and simplifications affect the designer's decision-making. Additionally, the yielded energy results are complemented by a comfort analysis to explore the impact of these improvements from a users' wellbeing viewpoint. At the end of the process the yielded results still confirm that the simulation remains far from reality and that simulation tools can indeed influence the decision-making process. [ABSTRACT FROM AUTHOR]

Upgrades in building energy efficiency codes led to differences between buildings designed according to outdated codes and those with most recent requirements. In this context, our study investigates the influence of external wall thermal transmittance, thermal inertia, and orientation on energy demand (heating, cooling) and occupant thermal comfort. Simulation models of an office building were designed, varying (i) the thermal transmittance values (0.20 and 0.60 W/(m2K)), (ii) the room orientation (four cardinal directions), and (iii) the wall thermal inertia (approximately 60 kJ/(m2K) for low and 340 kJ/(m2K) for high thermal inertia. The energy demand for heating and cooling seasons was calculated for Ljubljana using EnergyPlus 9.0.0 software. The reduction of the external wall thermal transmittance value from 0.6 W/(m2K) to 0.2 W/(m2K) contributes to significant energy savings (63% for heating and 37% for cooling). Thermal inertia showed considerable potential for energy savings, especially in the cooling season (20% and 13%, depending on the external wall insulation level). In addition, the orientation proved to have a notable impact on heating and cooling demand, however not as pronounced as thermal inertia (up to 7% total energy demand). Comparison of the thermal comfort results showed that when internal air temperatures are identically controlled in all the rooms (i.e., internal air temperature is not an influencing factor), the external wall thermal transmittance, thermal inertia, and room orientation show negligible influence on the average occupant thermal comfort. The simultaneous achievement of thermally comfortable conditions in the working environment and low energy use can only be achieved by simultaneously considering the U-value and thermal inertia. [ABSTRACT FROM AUTHOR]

Despite global efforts to reduce greenhouse gas emissions, the energy sector remains a major contributor, with hydrocarbon-based resources fulfilling around 80% of energy needs. As such, there is a growing focus on identifying effective and economically feasible policy mechanisms to promote renewable energy adoption. This study focuses on the theoretical problems surrounding the adoption of renewable energy policies. The study aims to highlight the potential for sustainable growth using renewable energy in the UAE and identify the most viable policy mechanisms for enhancing grid-tied solar energy adoption using qualitative and quantitative data collection methods and the HOMER Grid software. Compared to previous research, this study contributes by identifying a unified renewable energy policy mechanism that could significantly enhance the adoption of grid-tied solar energy generation in the UAE. The study's main findings show that a unified renewable policy mechanism could enhance grid-tied solar energy adoption throughout the UAE's electricity authorities. Net metering emerges as the most efficient and economically viable policy for customers and electricity utilities. [ABSTRACT FROM AUTHOR]

For load forecasting, numerous machine learning (ML) approaches have been published. Besides fully connected feedforward neural networks (FFNNs), also called multilayer perceptron, more advanced ML approaches like deep, recurrent or convolutional neural networks or ensemble methods have been applied. However, evaluating the added benefit by novel approaches is difficult. Statistical or rule-based methods constitute a too low benchmark. FFNNs need extensive tuning due to their manifold design choices. To address this issue, a structured, comprehensible five-step FFNN model creation methodology is presented, which constitutes of initial model creation, internal parameter selection, feature engineering, architecture tuning and final model creation. The methodology is then applied to forecast real world heat load data of a hospital in Germany. The forecast constitutes of 192 values (upcoming 48 h in 15 min resolution) and is composed of a multi-model univariate forecasting strategy, with three test models developed at first. As a result, the test models show great similarities which simplifies creation of the remaining models. A performance increase of up to 18% between initial and final models points out the importance of model tuning. As a conclusion, comprehensible model tuning is vital to use FFNN models as benchmark. The effort needed can be reduced by the experience gained through repeated application of the presented methodology. [ABSTRACT FROM AUTHOR]

Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster's accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites. [ABSTRACT FROM AUTHOR]

The present paper reports the impact of the Covid-19 pandemic on the electricity peak load and power generation in the State of Kuwait during the partial and full curfews imposed in March, April and May 2020 using historic data measured data and the predictions provided by a statistical genetic algorithm model. A quantitative assessment is made of the economic and environmental impacts caused by partial and full lockdowns. Comparison of measured peak demand for 2019 and 2020 with predicted peak demand for 2020 has: (i) enabled an accurate evaluation of residential energy consumption in the state of Kuwait at nearly 18 MWh yearly the highest energy consumption per capita in the world, (ii) shown that the imposition of the curfews to reduce the spread of COVID-19 caused a fall in the demand for electrical power of 17.6% compared with the expected demand and (iii) quantified the reduction in CO2, NOx and CO pollutant emissions produced by power plants due to less fuel being consumed. A mathematical model has been developed to predict the peak electric load in the national grid according to climatic data supplied by the Meteorological Department of Civil Aviation of Kuwait and National Control Center (NCC). [ABSTRACT FROM AUTHOR]

In the present paper, the energy performance of buildings forming a school centre in the northwest of Spain was analyzed using a transient simulation of the energy model of the school, which was developed with TRNSYS, a software of proven reliability in the field of thermal simulations. A deterministic calibration approach was applied to the initial building model to adjust the predictions to the actual performance of the school, data acquired during the temperature measurement campaign. The buildings under study were in deteriorated conditions due to poor maintenance over the years, presenting a big challenge for modelling and simulating it in a reliable way. The results showed that the proposed methodology is successful for obtaining calibrated thermal models of these types of damaged buildings, as the metrics employed to verify the final error showed a reduced normalized mean bias error (NMBE) of 2.73%. It was verified that a decrease of approximately 60% in NMBE and 17% in the coefficient of variation of the root mean square error (CV(RMSE)) was achieved due to the calibration process. Subsequent steps were performed with the aid of new software, which was developed under a European project that enabled the automated calibration of the simulations. [ABSTRACT FROM AUTHOR]

Extensive research demonstrates the importance of user practices in understanding variations in residential heating demand. Whereas previous studies have investigated variations in aggregated data, e.g., yearly heating consumption, the recent deployment of smart heat meters enables the analysis of households' energy use with a higher temporal resolution. Such analysis might provide knowledge crucial for managing peak demand in district heating systems with decentralized production units and increased shares of intermittent energy sources, such as wind and solar. This study exploits smart meter heating consumption data from a district heating network combined with socio-economic information for 803 Danish households. To perform this study, a multiple regression analysis was employed to understand the correlations between heat consumption and socio-economical characteristics. Furthermore, this study analyzed the various households' daily profiles to quantify the differences between the groups. During an average day, the higher-income households consume more energy, especially during the evening peak (17:00–20:00). Blue-collar and unemployed households use less during the morning peak (5:00–9:00). Despite minor differences, household groups have similar temporal patterns that follow institutional rhythms, like working hours. We therefore suggest that attempts to control the timing of heating demand do not rely on individual households' ability to time-shift energy practices, but instead address the embeddedness in stable socio-temporal structures. [ABSTRACT FROM AUTHOR]

The development of thermal energy storage systems is a possible solution in the search for reductions in the difference between the global energy supply and demand. In this context, the ability of some materials, the so-called phase change materials (PCMs), to absorb and release large amounts of energy under specific periods and operating conditions has been verified. The applications of these materials are limited due to their low thermal conductivity, and thus, it is necessary to associate them with high-conductivity materials, such as metals, to make the control of energy absorption and release times possible. Bearing this in mind, this paper presents a numerical analysis of the melting process of a PCM into a triplex tube heat exchanger (TTHX) with finned copper tubes, which allowed for the heat transfer between a heating fluid (water) and the phase change material to power a liquid-desiccant air conditioning system. Through the analysis of the temperature fields, liquid fractions, and velocities, as well as the phase transition, it was possible to describe the material charging process; then, the results were compared with experimental data, which are available in the specialized literature, and presented mean errors of less than 10%. The total required time to completely melt the PCM was about 105.5 min with the water being injected into the TTHX at a flow rate of 8.3 L/min and a temperature of 90 °C. It was observed that the latent energy that accumulated during the melting process was 1330 kJ, while the accumulated sensitive energy was 835 kJ. The average heat flux at the internal surface of the inner tube was about 3 times higher than the average heat flux at the outer surface of the TTHX intermediate tube due to the velocity gradients that developed in the internal part of the heat exchanger, and was about 10 times more intense than those observed in the external region of the equipment. [ABSTRACT FROM AUTHOR]

This paper presents the investigation of a local heating system consisting of three heating mats incorporated within a sedentary workplace. Thermal comfort and individual power settings for the heating mats were studied in experiments with 41 human subjects. Experiments were performed in a climate chamber at two operative temperatures: 16 and 18 °C. Results showed that local heating systems improved occupants' comfort and were able to create acceptable thermal conditions under both temperatures. The power settings identified were used to study the applicability of heating mats in an industrial hall. Multi-variant analysis of energy consumption for heating was carried out using an ESP-r (Environmental Systems Performance–Research) simulation program. The analysis results demonstrate that in some cases, the installation of local heating mats in industrial halls may reduce energy usage compared to having to heat the entire hall. However, the benefits depend on numerous parameters. Local heating is most advantageous when installed in halls with a small number of workstations, small internal heat gains, and low-efficiency central heating systems. In reality for the satisfactory implementation of local heating systems for a particular application, it is advised to perform analysis for the specific local conditions. In order to improve the effectiveness of local heating systems, an advanced heating control strategy should be considered. [ABSTRACT FROM AUTHOR]

Access to electricity for every South African citizen, including rural dwellers, is a human right issue guaranteed by the government's laws and policies. However, many remote rural areas still suffer from a lack of this very important amenity, due to the expensive prospect of connecting them to the central national grid. The feasible approach to connecting the rural communities to electricity supply is suggestively through the use of microgrid solutions. The microgrid technology is a very recent and viable option for the energy revolution. Microgrids result from the incorporation of energy storage systems, distributed generators, and localized loads. The application of this technology requires deliberate and extensive work on the operational architecture and the policy framework to be adopted. The energy storage devices form an integral part of the microgrid configuration or architecture to make sure more maintainable and constant operation is attained. This paper presents a review of the architectures of the existing microgrid systems, as well as the policy framework for implementable solutions. The various architectures display the peculiarity of the systems based on the increased grid performance, stability, quality of electricity, and other comparative advantages. The microgrid architectures are fundamentally recognized according to their AC, DC, or hybrid distribution buses and the complexity inherent to them. In the Policy And Development Section, the problems are treated as 'a search for the truth'—a truth being revealed by close and objective examination. The core of the problem to be solved is revealed clearly, thereby giving the basis for simplifying and solving it. The policies encourage the accomplishment of a zero-carbon dioxide (CO 2 ) emissions, energy security attainment, the meeting of the electricity demand, and, lastly, the promotion of access to electricity in rural areas. It is established that the returns through charges of the consumers are very insignificant. Although returns on investment always come in conflict with the human right demands of the local indigenes, the policy framework would be explicit on the mode of returns for the government, private partners, and the communities—a return that can be short, medium, or long term. Policymakers would be keen on the exhaustive analysis of issues, leading to optimal decision making. [ABSTRACT FROM AUTHOR]

Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend. [ABSTRACT FROM AUTHOR]

Climate change is around us today and will affect human life in many ways. More frequent extreme weather events raise mortality and car accident rates, global warming leads to longer growing seasons for crops, which may change farmers' crop choices, and the relationship between energy demand in residential buildings and weather is widely investigated. In this paper, we focus on the impact of weather on energy consumption, in particular, gasoline consumption through the more frequent use of both vehicles themselves and the air conditioner of the vehicle that decreases fuel economy, which has not been paid enough attention in the literature. We estimate the relationship between fuel consumption and weather using unique U.S. panel data. We find that hot days increase gasoline consumption, but in contrast to the results of residential energy consumption literature, there is no statistically significant effect on cold weather. With climate prediction data from General Circulation Models (GCMs), we simulate the impact of climate change on fuel energy consumption. The results show that the fuel consumption in the transportation sector may increase by up to 4% under the "business-as-usual" (RCP 8.5) scenario. Also, climate change has heterogeneous impacts across the continental United States. [ABSTRACT FROM AUTHOR]

In this paper, the building energy performance modelling tools TRNSYS (TRaNsient SYstem Simulation program) and TRNFlow (TRaNsient Flow) have been used to obtain the energy demand of a domestic building that includes the air infiltration rate and the effect of natural ventilation by using window operation data. An initial model has been fitted to monitoring data from the case study, building over a period when there were no heat gains in the building in order to obtain the building infiltration air change rate. After this calibration, a constant air-change rate model was established alongside two further models developed in the calibration process. Air change rate has been explored in order to determine air infiltrations caused by natural ventilation due to windows being opened. These results were compared to estimates gained through a previously published method and were found to be in good agreement. The main conclusion from the work was that the modelling ventilation rate in naturally ventilated residential buildings using TRNSYS and TRNSFlow can improve the simulation-based energy assessment. [ABSTRACT FROM AUTHOR]

Thermal bridges constitute a significant share in the overall heat losses through building partitions. This is an important issue not only in traditional but also ecological buildings, where the load-bearing structure is often a wooden frame. In partitions insulated with hemp-lime composite, the skeleton is usually hidden in the insulation. However, in some nodes or jambs, wooden elements may be exposed or have a large cross-section, intensifying the heat transfer. This work presents simulations of energy demand in a single-family building insulated with hemp-lime composite, using the BSim dynamic simulation program. The calculations take into account the linear thermal transmittance of structural nodes modeled in the THERM program. The energy demand for heating and the share of thermal bridges in the heat loss of the entire building were calculated for different locations of the structural framework in the walls, as well as the size and number of windows. The share of thermal bridges in heat losses was about 10%, and the differences in energy demand for heating using various frame locations in the wall were negligible. [ABSTRACT FROM AUTHOR]

The electrification of vehicles from the automotive and public transport industries can reduce harmful emissions if implemented correctly, but there is little evidence of whether the electrification of heavy freight transportation vehicles (HFTVs), such as multi-articulated vehicles, used in the freight industry could see the same benefits. This work studied heavy multi-articulated freight vehicles and developed a comparative analysis between electric and conventional diesel power trains to reduce their total emissions. Real-world drive cycle data were obtained from a heavy multi-articulated freight vehicle operating around Melbourne, Australia, with a gross combination mass (GCM) of up to 66,000 kg. Numerical models of the case study freight vehicle were then simulated with diesel, through-the-road parallel (TTRP) hybrid and electric power trains over the five different drive cycles with fuel and energy consumption results quantified. Battery weights were added on top of the real-world operating GCMs to assure the operational payload did not have to be reduced to accommodate the addition of electric power trains. The fuel and energy consumptions were then used to estimate the real-world emissions and compared. The results showed a positive reduction in tailpipe emissions, but total greenhouse emission was worse for operation in Melbourne if batteries were charged off the grid. However, if Melbourne can move towards more renewable energy and change its emission factor for generating electricity down to 0.49 kg CO2-e/kWh, a strong decarbonization could be possible for the Australian road freight industry and could help meet emission reduction targets set out in the 2015 Paris Agreement. [ABSTRACT FROM AUTHOR]

Nowadays, the building sector is one of the main sources emitting pollutant gases to the atmosphere due to its deficient energy behaviour. Among the elements of the envelope, the thermal bridges are where the heat losses and gains mainly occur, depending on the season of the year. To reduce the effect of the thermal bridges, there are different patented technologies which give provide solutions. In this paper, the thermal behaviour of five patented slab front (slab-façade) thermal bridges are analysed in a case study located in the south of Spain. Moreover, the influence of the thermal bridge on the energy demand from the building analysed was evaluated, both in the current scenario and future ones (2020, 2050 and 2080). The results reveal that the use of the patents in slab fronts can mean reductions by up to 95.74% in the linear thermal transmittance. Likewise, due to the improvement of the thermal bridge of slab fronts by using the patented designs which offered the best features, a savings in the global energy demand for heating higher than 18% as well as a savings in the global energy demand for cooling higher than 2.80% could be achieved in all the time scenarios considered. [ABSTRACT FROM AUTHOR]

Among the solutions that make it possible to reduce CO2 emissions in the transport sector, particularly in urban traffic conditions, are hybrid vehicles. The share of driving performed in electric mode for hybrid vehicles is highly dependent on motion resistance. There are different methods for determining the motion resistance function during chassis dynamometer testing, leading to different test results. Therefore, the main objective of this study was to determine the effect of the chassis dynamometer load function on the energy demand and CO2 emissions of a full-hybrid passenger car. Emissions tests according to the New European Driving Cycle (NEDC) were carried out on a chassis dynamometer for three different methods of determining the car's resistance to motion. The study showed that adopting the motion resistance function according to different methods, results in differences in CO2 emissions up to about 35% for the entire cycle. Therefore, the authors suggest that in the case of tests carried out with chassis dynamometers, it is necessary to also provide information on the chassis dynamometer loading function adopted for the tests. [ABSTRACT FROM AUTHOR]

Renewable energy sources and their part in the global energy mix are beneficial to energy diversification and environment protection. However, raw biomass is characterized by low heating value, hydrophilic properties, various mechanical durability, and the logistic challenges related to transportation and storage. One frequently used process of combined biomass valorization is torrefaction and pelletization, which increase the heating value, homogeneity, and hydrophobicity of the fuel. However, industrial clients need fuel characterized by favorable grindability, whereas, the individual clients (householders) need fuel with high mechanical durability. Due to the different expectations of final customers regarding biomass fuel properties, it is necessary to investigate the influence of the torrefaction on the mechanical durability of the pellets. In this paper, five various types of pellets and their torreficates (obtained at a temperature of 200 and 300 °C) were examined. Then the mechanical durability index DU and the grindability of the untreated and torrefied pellets were determined. The results indicated that the mechanical durability of untorrefied pellets is significantly greater than torrefied pellets. Interestingly, no significant differences in mechanical durability between torrefied pellets at 200 and 300 °C were observed, For sunflower husk pellets, the DU index amounted to 95.28 ± 0.72 (untorrefied), 47.22% ± 0.28% (torrefied at 200 °C), and 46.34% ± 0.72% (torrefied at 300 °C). Considering the grindability, as the treatment temperature increased the energy demand for grindability decreased. For example, the grindability of pine tree pellets was 15.96 ± 3.07 Wh·kg−1 (untreated), 1.86 ± 0.31 Wh·kg−1 (torrefied at 200 °C), and 0.99 ± 0.17 Wh·kg−1 (torrefied at 300 °C). The highest difference between raw and torrefied pellets was determined for beetroot pomace pellet: 36.31 ± 2.06 Wh·kg−1 (untreated), 3.85 ± 0.47 Wh·kg−1 (torrefied at 200 °C), and 1.03 ± 0.12 Wh·kg−1 (torrefied at 300 °C). [ABSTRACT FROM AUTHOR]

Biogas production through anaerobic digestion may play an important role in a circular economy because of the opportunity to produce a renewable fuel from organic waste. However, the production of biogas may require energy in the form of heat and electricity. Therefore, resource-effective biogas production must consider both biological and energy performance. For the individual biogas plant to improve its energy performance, a robust methodology to analyse and evaluate the energy demand on a detailed level is needed. Moreover, to compare the energy performance of different biogas plants, a methodology with a consistent terminology, system boundary and procedure is vital. The aim of this study was to develop a methodology for analysing the energy demand in biogas plants on a detailed level. In the methodology, the energy carriers are allocated to: (1) sub-processes (e.g., pretreatment, anaerobic digestion, gas cleaning), (2) unit processes (e.g., heating, mixing, pumping, lighting) and (3) a combination of these. For a thorough energy analysis, a combination of allocations is recommended. The methodology was validated by applying it to two different biogas plants. The results show that the methodology is applicable to biogas plants with different configurations of their production system. [ABSTRACT FROM AUTHOR]