Search results for: energy consumption statistic

Authors: Kyungeun Sung

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Upcycling is a process in which used materials are converted into something of higher value and/or quality in their second life. It has been increasingly recognised as one promising means to reduce material and energy use and also to engender sustainable production and consumption. For this reason and other foreseeable benefits, the concept of upcycling has received more attention from numerous researchers and business practitioners in recent years. This has been seen in the growing number of publications on this topic since the 1990s. However, the overall volume of literature dealing with upcycling is still low and no major review has been presented. Therefore, in order to further establish this field, this paper analyses and summarises the current body of literature on upcycling, focusing on different definitions, trends in practices, benefits, drawbacks and barriers in a number of subject areas and gives suggestions for future research by illuminating knowledge gaps in the area of upcycling.

Keywords: circular economy, cradle to cradle, sustainable production and consumption, upcycling, waste management

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Authors: Farnaz Alborzi, Angelika Schmitz, Rainer Stamminger

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Reducing water temperatures in the wash phase of a washing programme and increasing the overall cycle durations are the latest trends in decreasing energy consumption of washing programmes. Since the implementation of the new energy efficiency classes in 2010, manufacturers seem to apply the aforementioned washing strategy with lower temperatures combined with longer programme durations extensively to realise energy-savings needed to meet the requirements of the highest energy efficiency class possible. A semi-representative on-line survey in eleven European countries (Czech Republic, Finland, France, Germany, Hungary, Italy, Poland, Romania, Spain, Sweden and the United Kingdom) was conducted by Bonn University in 2015 to shed light on consumer opinion and behaviour regarding the effects of the lower washing temperature and longer cycle duration in laundry washing on consumers’ acceptance of the programme. The risk of the long wash cycle is that consumers might not use the energy efficient Standard programmes and will think of this option as inconvenient and therefore switch to shorter, but more energy consuming programmes. Furthermore, washing in a lower temperature may lead to the problem of cross-contamination. Washing behaviour of over 5,000 households was studied in this survey to provide support and guidance for manufacturers and policy designers. Qualified households were chosen following a predefined quota: -Involvement in laundry washing: substantial, -Distribution of gender: more than 50 % female , -Selected age groups: -20–39 years, -40–59 years, -60–74 years, -Household size: 1, 2, 3, 4 and more than 4 people. Furthermore, Eurostat data for each country were used to calculate the population distribution in the respective age class and household size as quotas for the consumer survey distribution in each country. Before starting the analyses, the validity of each dataset was controlled with the aid of control questions. After excluding the outlier data, the number of the panel diminished from 5,100 to 4,843. The primary outcome of the study is European consumers are willing to save water and energy in a laundry washing but reluctant to use long programme cycles since they don’t believe that the long cycles could be energy-saving. However, the results of our survey don’t confirm that there is a relation between frequency of using Standard cotton (Eco) or Energy-saving programmes and the duration of the programmes. It might be explained by the fact that the majority of washing programmes used by consumers do not take so long, perhaps consumers just choose some additional time reduction option when selecting those programmes and this finding might be changed if the Energy-saving programmes take longer. Therefore, it may be assumed that introducing the programme duration as a new measure on a revised energy label would strongly influence the consumer at the point of sale. Furthermore, results of the survey confirm that consumers are more willing to use lower temperature programmes in order to save energy than accepting longer programme cycles and majority of them accept deviation from the nominal temperature of the programme as long as the results are good.

Keywords: duration, energy-saving, standard programmes, washing temperature

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Authors: Mustofa Mahmud Al Mamun

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Government spending, one of the major components of gross domestic product (GDP), is composed of government consumption, investment, and transfer payments. A change in government spending during recessionary periods can generate an increase in GDP greater than the increase in spending. This is called the "multiplier effect". Accurate estimation of government spending multiplier is important because fiscal policy has been used to stimulate a flagging economy. Many recent studies have focused on identifying parts of the economy that responds more to a stimulus under a variety of circumstances. This paper used the US dataset from 1966 to 2020 and local projection method assuming standard identification strategy to estimate the multipliers. The model includes important macroaggregates and controls for forecasted government spending, interest rate, consumer price index (CPI), export, import, and level of public debt. Investment multipliers are found to be positive and larger than the consumption multipliers. Consumption multipliers are either negative or not significantly different than zero. Results do not vary across the business cycle. However, the consumption multiplier estimated from pre-1980 data is positive.

Keywords: business cycle, consumption multipliers, forecasted government spending, investment multipliers, local projection method, zero lower bound

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Authors: Soufi Saylam

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Environmental concerns, rising consumption of energy, and the high cost of mechanical systems have all contributed to increased interest in building energy efficiency and passive thermal design in recent years. This study attempts to make an evaluation of building envelope components and associated retrofits in terms of their impact on energy efficiency and occupant comfort in a sustainable context. The design of the building envelope, as a critical component of the building, has a significant impact on the organization of interior space and user comfort. In this regard, in order to achieve maximum comfort and energy savings, the design of the building envelope should include a thermal comfort system that adapts to climatic variables. This system should be developed in harmony with the environmental features, building shape, and materials used. The aim of this study is to investigate the role of the building envelope in sustainable architecture by integrating traditional envelope design principles and strategies with technological techniques, as well as to examine its role in providing physical and psychological comfort to users in the interior space.

Keywords: envelope design, functional needs, physiological comfort, sustainable architecture, traditional techniques

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Okorowo Cyril Agochi

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More than ever human activity relating to uncontrolled greenhouse gas (GHG) and its effects on the earth is gaining greater attention in the global academic and policy discussions. Activities of man has greatly influenced climate change over the years as a result of consistent increase in the use of fossil fuel energy. Scientists and researchers globally are making significant and devoted efforts towards the development and implementation of renewable energy technologies that are harmless to the environment. One of such energy is solar energy with its source from the sun. There are currently two primary ways of harvesting this energy from the sun: through photovoltaic (PV) panels and through thermal collectors. This work discuses solar energy the abundant renewable energy in the tropical Nigeria, processes of harvesting and recommends same as an alternative means of electric power generation in a time the demand for power supersedes supply.

Keywords: electric, power, renewable energy, solar energy, sun, tropical

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Authors: Hamed Najafi, Ghasem Nikjou

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Nowadays, there is an increasing attention to the resources scarcity issues. Because of failure in present patterns in the field of the allocation of exhaustible resources between generations and the challenges related to economic justice supply, it is supposed, to present a pattern from the Islamic perspective in this essay. By using content analysis of religious texts, we conclude that governments should remove the gap which is exists between the per capita income of the poor and their minimum consumption (necessary consumption). In order to preserve the exhaustible resources for poor people) not for all), between all generations, government should invest exhaustible resources on endless resources according to Hartwick’s criteria and should spend these benefits for poor people. But, if benefits did not cover the gap between minimum consumption and per capita income of poor levels in one generation, in this case, the government is responsible for covering this gap through the direct consumption of exhaustible resources. For an exact answer to this question, ‘how much of exhaustible resources should expense to maintain justice between generations?’ The theoretical and mathematical modeling has been used and proper function has been provided. The consumption pattern is presented for economic policy makers in Muslim countries, and non-Muslim even, it can be useful.

Keywords: exhaustible resources, Islamic justice, intergenerational justice, distribution of resources, Hartwick criteria

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Authors: Haluk Altay, Bilal Yücel, Berkcan Ulcay, Yücel Aydın

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In this study, the design, integration, and testing of measurement systems required for performance tests of jet engines used in small-scale unmanned aerial vehicles are described. Performance tests are carried out as thrust and fuel consumption. For thrust tests, measurements are made using a load cell. Amplifier and filter designs have been made for the load cell to measure accurately to meet the desired sensitivity. It was calibrated by making multiple measurements at different thrust levels. As a result of these processes, the cycle thrust graph was obtained. For fuel consumption tests, tests are carried out using a flow meter. Performance graphics were obtained by finding the fuel consumption for different RPM levels of the engine.

Keywords: jet engine, UAV, experimental test, loadcell, thrust, fuel consumption

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Authors: Frodouard Minani

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Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.

Keywords: base station, clustering algorithm, energy efficient, sensors, wireless sensor networks

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Authors: Fatemehsadat Mousaviabarbekouh

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Population growth and changing consumption patterns have led to waste management becoming a significant global challenge. With projections indicating that nearly 67% of the Earth's population will live in megacities by 2050, there is a pressing need for smart solutions to address citizens' demands. Waste collection, facilitated by the Internet of Things (IoT), offers an efficient and cost-effective approach. This study aims to review the utilization of IoT for waste collection and explore technologies that promote eco-friendly waste management. The research focuses on information and communication technologies (ICTs), including spatial, identification, acquisition, and data communication technologies. Additionally, the study examines various energy harvesting technologies to further reduce costs. The findings indicate that the application of these technologies can lead to significant cost savings, energy efficiency, and ultimately reshape the future of waste management.

Keywords: waste collection, IoT, smart cities, eco-friendly, information and communication technologies, energy harvesting

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Authors: Mathieu Bourdeau, Philippe Basset, Julien Waeytens, Elyes Nefzaoui

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As residential buildings account for a third of the overall energy consumption and greenhouse gas emissions in Europe, building energy modeling is an essential tool to reach energy efficiency goals. In the energy modeling process, calibration is a mandatory step to obtain accurate and reliable energy simulations. Nevertheless, the comparison between simulation results and the actual building energy behavior often highlights a significant performance gap. The literature discusses different origins of energy performance gaps, from building design to building operation. Then, building operation description in energy models, especially energy usages and users’ behavior, plays an important role in the reliability of simulations but is also the most accessible target for post-occupancy energy management and optimization. Therefore, the present study aims to discuss results on the calibration ofresidential building energy models using real operation data. Data are collected through a sensor network of more than 180 sensors and advanced energy meters deployed in three collective residential buildings undergoing major retrofit actions. The sensor network is implemented at building scale and in an eight-apartment sample. Data are collected for over one year and half and coverbuilding energy behavior – thermal and electricity, indoor environment, inhabitants’ comfort, occupancy, occupants behavior and energy uses, and local weather. Building energy simulations are performed using a physics-based building energy modeling software (Pleaides software), where the buildings’features are implemented according to the buildingsthermal regulation code compliance study and the retrofit project technical files. Sensitivity analyses are performed to highlight the most energy-driving building features regarding each end-use. These features are then compared with the collected post-occupancy data. Energy-driving features are progressively replaced with field data for a step-by-step calibration of the energy model. Results of this study provide an analysis of energy performance gap on an existing residential case study under deep retrofit actions. It highlights the impact of the different building features on the energy behavior and the performance gap in this context, such as temperature setpoints, indoor occupancy, the building envelopeproperties but also domestic hot water usage or heat gains from electric appliances. The benefits of inputting field data from an extensive instrumentation campaign instead of standardized scenarios are also described. Finally, the exhaustive instrumentation solution provides useful insights on the needs, advantages, and shortcomings of the implemented sensor network for its replicability on a larger scale and for different use cases.

Keywords: calibration, building energy modeling, performance gap, sensor network

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Authors: Domenico Panno, Antonino D’amico, Hamed Jafargholi

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This paper presents an experimental analysis of the performance of a system for freezing fish products equipped with a modulating vapour injection scroll compressor operating with R448A refrigerant. Freezing is a critical process for the preservation of seafood products, as it influences quality, food safety, and environmental sustainability. The use of a modulating scroll compressor with vapour injection, associated with the R448A refrigerant, is proposed as a solution to optimize the performance of the system, reducing energy consumption and mitigating the environmental impact. The stream injection modulating scroll compressor represents an advanced technology that allows you to adjust the compressor capacity based on the actual cooling needs of the system. Vapour injection allows the optimization of the refrigeration cycle, reducing the evaporation temperature and improving the overall efficiency of the system. The use of R448A refrigerant, with a low Global Warming Potential (GWP), is part of an environmental sustainability perspective, helping to reduce the climate impact of the system. The aim of this research was to evaluate the performance of the system through a series of experiments conducted on a pilot plant for the freezing of fish products. Several operational variables were monitored and recorded, including evaporation temperature, condensation temperature, energy consumption, and freezing time of seafood products. The results of the experimental analysis highlighted the benefits deriving from the use of the modulating vapour injection scroll compressor with the R448A refrigerant. In particular, a significant reduction in energy consumption was recorded compared to conventional systems. The modulating capacity of the compressor made it possible to adapt the cold production to variations in the thermal load, ensuring optimal operation of the system and reducing energy waste. Furthermore, the use of an electronic expansion valve highlighted greater precision in the control of the evaporation temperature, with minimal deviation from the desired set point. This helped ensure better quality of the final product, reducing the risk of damage due to temperature changes and ensuring uniform freezing of the fish products. The freezing time of seafood has been significantly reduced thanks to the configuration of the entire system, allowing for faster production and greater production capacity of the plant. In conclusion, the use of a modulating vapour injection scroll compressor operating with R448A has proven effective in improving the performance of a system for freezing fish products. This technology offers an optimal balance between energy efficiency, temperature control, and environmental sustainability, making it an advantageous choice for food industries.

Keywords: scroll compressor, vapor injection, refrigeration system, EER

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Authors: Tahsin Shameem, Chowdhury Sadman Jahan, Mohammad Alam

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Interest about Net Zero Energy Buildings have gained traction in recent years following the need to sustain energy consumption with generations on site and to reduce dependence on grid supplied energy from large plants using fossil fuel. With this end in view, building integrated photovoltaics are being studied attempting to utilize all exterior facades of a building to generate power. In this paper, we have looked at the physical parameters defining a dye sensitized solar cell (DSSC) and discussed their impact on energy harvest. Following our discussion and experimental data obtained from literature, we have attempted to optimize these physical parameters accordingly so as to allow maximum light absorption for a given active layer thickness. We then modified a planer DSSC design with our optimized properties to allow adequate light transmission which demonstrated a high fill factor and an External Quantum Efficiency (EQE) of greater than 9% by computer aided design and simulation. In conclusion, a DSSC based solar window with such high output values even after such high light transmission through it definitely flags a promising future for this technology and our work elicits the need for further study and practical experimentation.

Keywords: net zero energy building, integrated photovoltaics, dye sensitized solar cell, fill factor, External Quantum Efficiency

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Authors: Asim Shabir, Ruqia Shaikh

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Supersized pricing is an effective strategy often used by marketers to make consumers buy more. However, such a strategy also results in more purchases and consumption, especially of hedonic food products. This study brings interesting insights about supersized pricing as it provides value-based justification to consumers; as a result, the guilt associated with the purchase and consumption of hedonic products diminishes, which mediates the impact between supersized pricing and size choice. Interestingly, there is a three-way interaction between pricing, product type, and health goal prime. Health prime diminishes the impact of supersized pricing in the case of more hedonic products (unhealthy) compared to less hedonic (perceived as healthy) products.

Keywords: supersized pricing, anticipated consumption guilt, health claim, product type

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Authors: G. Gurguze, I. Turkoglu

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Today, the developing features of technological tools with limited energy resources have made it necessary to use energy efficiently. Energy management techniques have emerged for this purpose. As with every field, energy management is vital for robots that are being used in many areas from industry to daily life and that are thought to take up more spaces in the future. Particularly, effective power management in autonomous and multi robots, which are getting more complicated and increasing day by day, will improve the performance and success. In this study, robot management algorithms, usage of renewable and hybrid energy sources, robot motion patterns, robot designs, sharing strategies of workloads in multiple robots, road and mission planning algorithms are discussed for efficient use of energy resources by mobile robots. These techniques have been evaluated in terms of efficient use of existing energy resources and energy management in robots.

Keywords: energy management, mobile robot, robot administration, robot management, robot planning

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Authors: W. Schellong

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Industry accounts for one-third of global final energy demand. Crossover technologies (e.g. motors, pumps, process heat, and air conditioning) play an important role in improving energy efficiency. These technologies are used in many applications independent of the production branch. Especially electrical power is used by drives, pumps, compressors, and lightning. The paper demonstrates the algorithm of the energy analysis by some selected case studies for typical industrial processes. The energy analysis represents an essential part of energy management systems (EMS). Generally, process control system (PCS) can support EMS. They provide information about the production process, and they organize the maintenance actions. Combining these tools into an integrated process allows the development of an energy critical equipment strategy. Thus, asset and energy management can use the same common data to improve the energy efficiency.

Keywords: crossover technologies, data management, energy analysis, energy efficiency, process control

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Authors: Baikunthanath Sahoo, Santosh Kumar Sahu, Krishna Malakar

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With the essence of global environmental sustainability and green business management, the wind of business research moved towards Corporate Social Responsibility. In addition to international and national treaties, businesses have also started realising environmental protection and energy efficiency through CSR as part of business strategy in response to climate change. Considering the ambitious emission reduction target and rapid economic development of India, this study is an attempt to explore the effect of CSR on the energy efficiency management of manufacturing firms in India. By using firm-level data, the panel fixed effect model shows that the CSR dummy variable is negatively influencing the energy intensity or technically, they are energy efficient. The result demonstrates that in the presence of CSR, all the production economic variables are significant. The result also shows that doing environmental expenditure does not improve energy efficiency might be because very few firms are motivated to do such expenditure and also not common to all sectors. The interactive effect model result conforms that without considering CSR dummy as an intervening variable only Manufacturers of Chemical and Chemical products, Manufacturers of Pharmaceutical, medical chemical, and botanical products firms energy intensity low but after considering CSR in their business practices all six sub-sector firms become energy efficient. The empirical result also validate that firms are continuously engaged in CSR activities they are highly energy efficient. It is an important motivational factor for firms to become economically and environmentally sustainable in the corporate world. This analysis would help business practitioners to know how to manage today’s profitability and tomorrow’s sustainability to achieve a comparative advantage in the emerging market economy. The paper concludes that reducing energy consumption as part of their social responsibility to care for the environment, will need collaborative efforts of business society and policy bodies.

Keywords: CSR, Energy Efficiency, Indian manufacturing Sector, Business strategy

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Authors: Ehsan Mohseni, Waiching Tang, Shanyong Wang

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Low thermal conductivity of phase change materials (PCMs) affects the thermal performance and energy storage efficiency of latent heat thermal energy storage systems. In the current research, a structural lightweight concrete with function of indoor temperature control was developed using thermal energy storage aggregates (TESA) and nano-titanium (NT). The macro-encapsulated technique was served to incorporate the PCM into the lightweight aggregate through vacuum impregnation. The compressive strength was measured, and the thermal performance of concrete panel was evaluated by using a self-designed environmental chamber. The impact of NT on microstructure was also assessed via scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) tests. The test results indicated that NT was able to increase the compressive strength by filling the micro pores and making the microstructure denser and more homogeneous. In addition, the environmental chamber experiment showed that introduction of NT into TESA improved the heat transfer of composites noticeably. The changes were illustrated by the reduction in peak temperatures in the centre, outside and inside surfaces of concrete panels by the inclusion of NT. It can be concluded that NT particles had the capability to decrease the energy consumption and obtain higher energy storage efficiency by the reduction of indoor temperature.

Keywords: heat transfer, macro-encapsulation, microstructure properties, nanoparticles, phase change material

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Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu

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Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: building energy management, machine learning, operation planning, simulation-based optimization

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Authors: N. Tahouni, M. Gholami, M. H. Panjeshahi

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Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.

Keywords: flaring, fuel gas network, GHG emissions, stream

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Authors: Mohamed Shaban, Adel Yahya

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The goal of this paper study is to schedule the power generation units to minimize fuel consumption cost based on a model that solves unit commitment problems. This can be done by utilizing forward dynamic programming method to determine the most economic scheduling of generating units. The model was applied to a power station, which consists of four generating units. The obtained results show that the applications of forward dynamic programming method offer a substantial reduction in fuel consumption cost. The fuel consumption cost has been reduced from $116,326 to $102,181 within a 24-hour period. This means saving about 12.16 % of fuel consumption cost. The study emphasizes the importance of applying modeling schedule programs to the operation of power generation units. As a consequence less consumption of fuel, less loss of power and less pollution

Keywords: unit commitment, forward dynamic, fuel cost, programming, generation scheduling, operation cost, power system, generating units

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Authors: M. Manjunath, R. Rakesh, Y. T. Krishne Gowda, G. Panduranga Murthy

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The availability of energy resources plays a vital role in the progress of a country. Over the last decades, there is an increase in the consumption of energy worldwide resulting in the depletion of fossil fuels. This necessitates dependency on other countries for energy resources. Therefore, a renewable eco-friendly alternate fuel is replaced in place of fossil fuel which can be vegetable oils as a substitute fuel for diesel. Since oils are more viscous it cannot be used directly in CI engines without any engine modification. Thus, a conversion of vegetable oils to biodiesel is done by a Transesterification process. The present paper is restricted to Biofuel substitute for diesel and which can be obtained from a number of edible and non-edible oil resources. The oil from these resources can be Transesterified by a suitable method depending on its FFA content for the production of biodiesel and that can be used to operate CI engine. In this work, an attempt is made to test the performance of CI engine using Transesterified peanut and sunflower oil methyl esters blends with diesel.

Keywords: SOME, POME, BMEP, BSFC, BTE

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Authors: Somesh Kumar Sharma, Sunanad Gupta

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The contribution of aviation to society and economy is undisputedly significant. The aviation industry drives economic and social progress by contributing prominently to tourism, commerce and improved quality of life. Identifying the amount of fuel consumed by an aircraft while moving in both airspace and ground networks is critical to air transport economics. Aviation fuel is a major operating cost parameter of the aviation industry and at the same time it is prone to various constraints. This article aims to develop a model for fuel consumption of aviation product. The paper tailors the information for the fuel consumption optimization in terms of information development, information evaluation and information refinement. The information is evaluated and refined using statistical package R and Factor Analysis which is further validated with neural networking. The study explores three primary dimensions which are finally summarized into 23 influencing variables in contrast to 96 variables available in literature. The 23 variables explored in this study should be considered as highly influencing variables for fuel consumption which will contribute significantly towards fuel optimization.

Keywords: fuel consumption, civil aviation industry, neural networking, optimization

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: Amien Rahardjo, Faiz Husnayain, Iwa Garniwa

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PLN used the kWh meter to determine the amount of energy consumed by the household customers. High precision of kWh meter is needed in order to give accuracy results as the accuracy can be decreased due to the presence of harmonic. In this study, an estimation of active power consumed was developed. Based on the first year study results, the largest deviation due to harmonics can reach up to 9.8% in 2200VA and 12.29% in 3500VA with kWh meter analog. In the second year of study, deviation of digital customer meter reaches 2.01% and analog meter up to 9.45% for 3500VA household customers. The aim of this research is to produce an estimation system to calculate the total energy consumed by household customer using analog meter so the losses due to irregularities PLN recording of energy consumption based on the measurement used Analog kWh-meter installed is avoided.

Keywords: harmonics estimation, harmonic distortion, kWh meters analog and digital, THD, household customers

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Authors: Han Yan, Jieren Luo, Qiuhui Yan, Xiaoqing Li

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The thermal conductivity of porous materials is closely related to the thermal and moisture environment and the overall energy consumption of the building. The study of thermal conductivity of porous materials has great significance for the realization of low energy consumption building and economic construction building. Based on the study of effective thermal conductivity of porous materials at home and abroad, the thermal conductivity under a variety of different density of polystyrene board (EPS), plastic extruded board (XPS) and polyurethane (PU) and phenolic resin (PF) in wet state through theoretical analysis and experimental research has been studied. Initially, the moisture absorption and desorption properties of specimens had been discussed under different density, which led a result indicates the moisture absorption of four porous materials all have three stages, fast, stable and gentle. For the moisture desorption, there are two types. One is the existence of the rapid phase of the stage, such as XPS board, PU board. The other one does not have the fast desorption, instead, it is more stabilized, such as XPS board, PF board. Furthermore, the relationship between water content and thermal conductivity of porous materials had been studied and fitted, which figured out that in the wake of the increasing water content, the thermal conductivity of porous material is continually improving. At the same time, this result also shows, in different density, when the same kind of materials decreases, the saturated moisture content increases. Finally, the moisture absorption and desorption properties of the four kinds of materials are compared comprehensively, and it turned out that the heat preservation performance of PU board is the best, followed by EPS board, XPS board, PF board.

Keywords: porous materials, thermal conductivity, moisture content, transient hot-wire method

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Authors: Balazs Kulcsar

Abstract:

Professional energy organizations perform analyses mainly on the global and national levels about the expected development of the share of renewables in electric power generation, heating, and cooling, as well as the transport sectors. There are just a few publications, research institutions, non-profit organizations, and national initiatives with a focus on studies in the individual towns, settlements. Issues concerning the self-supply of energy on the settlement level have not become too wide-spread. The goal of our energy geographic studies is to determine the share of local renewable energy sources in the settlement-based electricity supply across Hungary. The Hungarian energy supply system defines four categories based on the installed capacities of electric power generating units. From these categories, the theoretical annual electricity production of small-sized household power plants (SSHPP) featuring installed capacities under 50 kW and small power plants with under 0.5 MW capacities have been taken into consideration. In the above-mentioned power plant categories, the Hungarian Electricity Act has allowed the establishment of power plants primarily for the utilization of renewable energy sources since 2008. Though with certain restrictions, these small power plants utilizing renewable energies have the closest links to individual settlements and can be regarded as the achievements of the host settlements in the shift of energy use. Based on the 2017 data, we have ranked settlements to reflect the level of self-sufficiency in electricity production from renewable energy sources. The results show that the supply of all the energy demanded by settlements from local renewables is within reach now in small settlements, e.g., in the form of the small power plant categories discussed in the study, and is not at all impossible even in small towns and cities. In Hungary, 30 settlements produce more renewable electricity than their own annual electricity consumption. If these overproductive settlements export their excess electricity towards neighboring settlements, then full electricity supply can be realized on further 29 settlements from renewable sources by local small power plants. These results provide an opportunity for governmental planning of the realization of energy shift (legislative background, support system, environmental education), as well as framing developmental forecasts and scenarios until 2030.

Keywords: energy geography, Hungary, local small power plants, renewable energy sources, self-sufficiency settlements

Procedia PDF Downloads 147

Authors: Auwal Mustapha Imam

Abstract:

The aim of the energy management system is to monitor and control utilization, access, optimize and manage energy availability. This can be realized through real-time analyses and energy sources and loads data control in a predictive way. Smart-home monitoring and control provide convenience and cost savings by controlling appliances, lights, thermostats and other loads. There may be different categories of loads in the various homes, and the homeowner may wish to control access to solar-generated energy to protect the storage from draining completely. Controlling the power system operation by managing the converter output power and controlling how it feeds the appliances will satisfy the residential load demand. The Internet of Things (IoT) provides an attractive technological platform to connect the two and make home automation and domestic energy utilization easier and more attractive. This paper presents the use of IoT-based control topology to monitor and control power distribution and consumption by DC loads connected to single-input multiple outputs (SIMO) DC-DC converter, thereby reducing leakages, enhancing performance and reducing human efforts. A SIMO converter was first developed and integrated with the IoT/Raspberry Pi control topology, which enables the user to monitor and control power scheduling and load forecasting via an Android app.

Keywords: flyback, converter, DC-DC, photovoltaic, SIMO

Procedia PDF Downloads 49

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

Procedia PDF Downloads 78

Authors: T. Parhizkar, H. Jafarian, F. Aramoun, Y. Saboohi

Abstract:

Applying motorized shades have substantial effect on reducing energy consumption in building sector. Moreover, the combination of motorized shades with lighting systems and PV panels can lead to considerable reduction in the energy demand of buildings. In this paper, a model is developed to assess and find an optimum combination from shade designs, lighting control systems (dimming and on/off) and implementing PV panels in shades point of view. It is worth mentioning that annual saving for all designs is obtained during hourly simulation of lighting, solar heat flux and electricity generation with the use of PV panel. From 12 designs in general, three designs, two lighting control systems and PV panel option is implemented for a case study. The results illustrate that the optimum combination causes a saving potential of 792kW.hr per year.

Keywords: motorized shades, daylight, cooling load, shade control, hourly simulation

Procedia PDF Downloads 171