Search results for: demand forecast updating

Authors: Arjun Paul, Adrijit Goswami

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In our present work, an economic order quantity inventory model with shortages is developed where holding cost is expressed as linearly increasing function of time and demand rate is a ramp type function of time. The items considered in the model are deteriorating in nature so that a small fraction of the items is depleted with the passage of time. In order to consider a more realistic situation, the deterioration rate is assumed to follow a continuous uniform distribution with the parameters involved being triangular fuzzy numbers. The inventory manager offers his customer a discount in case he is willing to backorder his demand when there is a stock-out. The optimum ordering policy and the optimum discount offered for each backorder are determined by minimizing the total cost in a replenishment interval. For better illustration of our proposed model in both the crisp and fuzzy sense and for providing richer insights, a numerical example is cited to exemplify the policy and to analyze the sensitivity of the model parameters.

Keywords: fuzzy deterioration rate, price discount on backorder, ramp type demand, shortage, time varying holding cost

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Authors: Elena Collino, Dario A. Ronzio, Goffredo Decimi, Maurizio Riva

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The rapid increase of renewable energy in Italy is led by wind and solar installations. The 2017 Italian energy strategy foresees a further development of these sustainable technologies, especially solar. This fact has resulted in new opportunities, challenges, and different problems to deal with. The growth of renewables allows to meet the European requirements regarding energy and environmental policy, but these types of sources are difficult to manage because they are intermittent and non-programmable. Operationally, these characteristics can lead to instability on the voltage profile and increasing uncertainty on energy reserve scheduling. The increasing renewable production must be considered with more and more attention especially by the Transmission System Operator (TSO). The TSO, in fact, every day provides orders on energy dispatch, once the market outcome has been determined, on extended areas, defined mainly on the basis of power transmission limitations. In Italy, six market zone are defined: Northern-Italy, Central-Northern Italy, Central-Southern Italy, Southern Italy, Sardinia, and Sicily. An accurate hourly renewable power forecasting for the day-ahead on these extended areas brings an improvement both in terms of dispatching and reserve management. In this study, an operational forecasting tool of the hourly solar output for the six Italian market zones is presented, and the performance is analysed. The implementation is carried out by means of a numerical weather prediction model, coupled with a statistical post-processing in order to derive the power forecast on the basis of the meteorological projection. The weather forecast is obtained from the limited area model RAMS on the Italian territory, initialized with IFS-ECMWF boundary conditions. The post-processing calculates the solar power production with the Analog Ensemble technique (AN). This statistical approach forecasts the production using a probability distribution of the measured production registered in the past when the weather scenario looked very similar to the forecasted one. The similarity is evaluated for the components of the solar radiation: global (GHI), diffuse (DIF) and direct normal (DNI) irradiation, together with the corresponding azimuth and zenith solar angles. These are, in fact, the main factors that affect the solar production. Considering that the AN performance is strictly related to the length and quality of the historical data a training period of more than one year has been used. The training set is made by historical Numerical Weather Prediction (NWP) forecasts at 12 UTC for the GHI, DIF and DNI variables over the Italian territory together with corresponding hourly measured production for each of the six zones. The AN technique makes it possible to estimate the aggregate solar production in the area, without information about the technologic characteristics of the all solar parks present in each area. Besides, this information is often only partially available. Every day, the hourly solar power forecast for the six Italian market zones is made publicly available through a website.

Keywords: analog ensemble, electricity market, PV forecast, solar energy

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Authors: Francesca Marsili

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The reliability assessment of existing structures with probabilistic methods is becoming an increasingly important and frequent engineering task. However probabilistic reliability methods are based on an exhaustive knowledge of the stochastic modeling of the variables involved in the assessment; at the moment standards for the modeling of variables are absent, representing an obstacle to the dissemination of probabilistic methods. The framework according to probability distribution functions (PDFs) are established is represented by the Bayesian statistics, which uses Bayes Theorem: a prior PDF for the considered parameter is established based on information derived from the design stage and qualitative judgments based on the engineer past experience; then, the prior model is updated with the results of investigation carried out on the considered structure, such as material testing, determination of action and structural properties. The application of Bayesian statistics arises two different kind of problems: 1. The results of the updating depend on the engineer previous experience; 2. The updating of the prior PDF can be performed only if the structure has been tested, and quantitative data that can be statistically manipulated have been collected; performing tests is always an expensive and time consuming operation; furthermore, if the considered structure is an ancient building, destructive tests could compromise its cultural value and therefore should be avoided. In order to solve those problems, an interesting research path is represented by investigating Artificial Intelligence (AI) techniques that can be useful for the automation of the modeling of variables and for the updating of material parameters without performing destructive tests. Among the others, one that raises particular attention in relation to the object of this study is constituted by Case-Based Reasoning (CBR). In this application, cases will be represented by existing buildings where material tests have already been carried out and an updated PDFs for the material mechanical parameters has been computed through a Bayesian analysis. Then each case will be composed by a qualitative description of the material under assessment and the posterior PDFs that describe its material properties. The problem that will be solved is the definition of PDFs for material parameters involved in the reliability assessment of the considered structure. A CBR system represent a good candi¬date in automating the modelling of variables because: 1. Engineers already draw an estimation of the material properties based on the experience collected during the assessment of similar structures, or based on similar cases collected in literature or in data-bases; 2. Material tests carried out on structure can be easily collected from laboratory database or from literature; 3. The system will provide the user of a reliable probabilistic description of the variables involved in the assessment that will also serve as a tool in support of the engineer’s qualitative judgments. Automated modeling of variables can help in spreading probabilistic reliability assessment of existing buildings in the common engineering practice, and target at the best intervention and further tests on the structure; CBR represents a technique which may help to achieve this.

Keywords: reliability assessment of existing buildings, Bayesian analysis, case-based reasoning, historical structures

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Authors: Viktória Sugár, Attila Talamon, András Horkai, Michihiro Kita

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The dense urban fabric of the 7^th district of Budapest -known as the former Jewish Quarter-, contains mainly historical style, multi-story tenement houses with courtyards. The high population density and the unsatisfactory energetic state of the buildings result high energy consumption. As a preliminary survey of a complex rehabilitation plan, the authors aim to determine the energy demand of the area. The energy demand was calculated by analyzing the structure and the energy consumption of each building by using Geographic Information System (GIS) methods. The carbon dioxide emission was also calculated, to assess the potential of reducing the present state value by complex structural and energetic rehabilitation. As a main focus of the survey, an energy intensity map has been created about the area.

Keywords: CO₂, energy intensity map, geographic information system (GIS), Hungary, Jewish quarter, rehabilitation

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Authors: Abdelhammid Chibane

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This study summarizes state-of-the-art knowledge in the seismic response of vertically irregular building frames. Criteria defining vertical irregularity as per the current building codes have been discussed. A review of studies on the seismic behaviour of vertically irregular structures along with their findings has been presented. It is observed that building codes provide criteria to classify the vertically irregular structures and suggest dynamic analysis to arrive at design lateral forces. Most of the studies agree on the increase in drift demand in the tower portion of set-back structures and on the increase in seismic demand for buildings with discontinuous distributions in mass, stiffness, and strength. The largest seismic demand is found for the combined-stiffness-and-strength irregularity.

Keywords: mass irregularity, set-back structure, stiffness irregularity, strength irregularity, vertical irregularity

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Authors: P. Caimmi, E. Bele, A. Abolfathi

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Lattice materials are cellular structures composed of a periodic network of beams. They offer high weight-specific mechanical properties and lend themselves to numerous weight-sensitive applications. The periodic internal structure responds to external vibrations through characteristic frequency bandgaps, making these materials suitable for the reduction of noise and vibration. However, the deviation from architectural homogeneity, due to, e.g., manufacturing imperfections, has a strong influence on the mechanical properties and vibration response of these materials. In this work, we present results on the influence of geometric imperfections on the vibration response of hexagonal lattices. Three classes of geometrical variables are used: the characteristics of the architecture (relative density, ligament length/cell size ratio), imperfection type (degree of non-periodicity, cracks, hard inclusions) and defect morphology (size, distribution). Test specimens with controlled size and distribution of imperfections are manufactured through selective laser sintering. The Frequency Response Functions (FRFs) in the form of accelerance are measured, and the modal shapes are captured through a high-speed camera. The finite element method is used to provide insights on the extension of these results to semi-infinite lattices. An updating procedure is conducted to increase the reliability of numerical simulation results compared to experimental measurements. This is achieved by updating the boundary conditions and material stiffness. Variations in FRFs of periodic structures due to changes in the relative density of the constituent unit cell are analysed. The effects of geometric imperfections on the dynamic response of periodic structures are investigated. The findings can be used to open up the opportunity for tailoring these lattice materials to achieve optimal amplitude attenuations at specific frequency ranges.

Keywords: lattice architectures, geometric imperfections, vibration attenuation, experimental modal analysis

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Authors: Jiri Dufek

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The national model of the Czech Republic has been modified in a detailed way to get detailed travel demand in the municipality level (cities, villages over 300 inhabitants). As a technique for this detailed modelling, three-dimensional procedure for calibrating gravity models, was used. Besides of zone production and attraction, which is usual in gravity models, the next additional parameter for trip distribution was introduced. Usually it is called by “third dimension”. In the model, this parameter is a demand between regions. The distribution procedure involved calculation of appropriate skim matrices and its multiplication by three coefficients obtained by iterative balancing of production, attraction and third dimension. This type of trip distribution was processed in R-project and the results were used in the Czech Republic transport model, created in PTV Vision. This process generated more precise results in local level od the model (towns, villages)

Keywords: trip distribution, three dimension, transport model, municipalities

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Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: Nicola Rubino

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This paper presents an analysis of a group of commodity exporting countries' nominal exchange rate movements in relationship to the US dollar. Using a series of Unrestricted Self-exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national CPI price differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute error measures on the basis of two-hundred and fifty-three months rolling window forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive non linear autoregressive model (AAR) and a simple linear Neural Network model (NNET). Our preliminary results confirm presence of some form of TAR non linearity in the majority of the countries analyzed, with a relatively higher goodness of fit, with respect to the linear AR(1) benchmark, in five countries out of sixteen considered. Although no model appears to statistically prevail over the other, our final out-of-sample forecast exercise shows that SETAR models tend to have quite poor relative forecasting performance, especially when compared to alternative non-linear specifications. Finally, by analyzing the implied half-lives of the > coefficients, our results confirms the presence, in the spirit of arbitrage band adjustment, of band convergence with an inner unit root behaviour in five of the sixteen countries analyzed.

Keywords: transition regression model, real exchange rate, nonlinearities, price differentials, PPP, commodity points

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Authors: Mohammed Bashir Uddin

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Human trafficking remains as a persistent problem in many parts of the world. It is considered by many countries as an issue of a threat to national security. Border enforcement to prevent trafficking has been the main incentive, which eventually causes human insecurity for vulnerable people, especially for women. This research argues that focus needs to be placed on the political economy of trafficking, hence on the supply and demand sides of trafficking from a broader socio-economic perspective. Trafficking is a global phenomenon with its contemporary origins in the international capitalist market system. This research investigates particularly the supply-demand nexus on the backdrop of globalization and its impact on human security. It argues that the nexus varies across the countries, particularly the demand side. While prostitution has been the sole focus of the demand side in all countries in Asia, the paper argues that organ trade, bonded labor, cheap and exploitable labor through false recruitment (male trafficking) and adoption are some of the rising demands that explore new trends of trafficking, which could be better explained through international political economy (IPE). Following a qualitative research method, the paper argues that although demands vary in destination countries, they are the byproducts of IPE which have different socio-economic impacts both on trafficked individuals and the states.

Keywords: globalization, human security, human trafficking, political economy

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Authors: Suleiman Hassan Otuoze, Dexter Vernon Lloyd Hunt, Ian Jefferson

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Urbanization has continued to widen the gap between demand and resources available to provide resilient and sustainable transport services in many fast-growing developing countries' cities. Transport demand management is a decision-based optimization concept for both benchmarking and ensuring efficient use of transport resources. This study assesses the service quality of infrastructure and mobility services in the Nigerian cities of Kano and Lagos through five dimensions of quality (i.e., Tangibility, Reliability, Responsibility, Safety Assurance and Empathy). The methodology adopts a hybrid AHP-SERVQUAL model applied on questionnaire surveys to gauge the quality of satisfaction and the views of experts in the field. The AHP results prioritize tangibility, which defines the state of transportation infrastructure and services in terms of satisfaction qualities and intervention decision weights in the two cities. The results recorded ‘unsatisfactory’ indices of quality of performance and satisfaction rating values of 48% and 49% for Kano and Lagos, respectively. The satisfaction indices are identified as indicators of low performances of transportation demand management (TDM) measures and the necessity to re-order priorities and take proactive steps towards infrastructure. The findings pilot a framework for comparative assessment of recognizable standards in transport services, best ethics of management and a necessity of quality infrastructure to guarantee both resilient and sustainable urban mobility.

Keywords: transportation demand management, multi-criteria decision support, transport infrastructure, service quality, sustainable transport

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Authors: Zaid Jamal Saeed Almahmoud

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Smart Grid is the vision of the future power system that combines advanced monitoring and communication technologies to provide energy in a smart, efficient, and user-friendly manner. This proposal considers a demand response model in the Smart Grid based on utility maximization. Given a set of consumers with conflicting preferences in terms of consumption and a utility company that aims to minimize the peak demand and match demand to supply, we study the problem of aggregating these preferences while modelling the problem as a game. We also investigate whether an equilibrium can be reached to maximize the social benefit. Based on such equilibrium, we propose a dynamic pricing heuristic that computes the equilibrium and sets the prices accordingly. The developed approach was analysed theoretically and evaluated experimentally using real appliances data. The results show that our proposed approach achieves a substantial reduction in the overall energy consumption.

Keywords: heuristics, smart grid, aggregation, mechanism design, equilibrium

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Authors: Juanling Lin

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The construction of urban parks is an important part of eco-city construction, and the intervention of big data provides a more scientific and rational platform for the assessment of urban parks by identifying and correcting the irrationality of urban park planning from the macroscopic level and then promoting the rational planning of urban parks. The study builds an urban park assessment system based on urban road network data and POI data, taking Futian District of Shenzhen as the research object, and utilizes the GIS geographic information system to assess the park system of Futian District in five aspects: park spatial distribution, accessibility, service capacity, demand, and supply-demand relationship. The urban park assessment system can effectively reflect the current situation of urban park construction and provide a useful exploration for realizing the rationality and fairness of urban park planning.

Keywords: urban parks, assessment system, POI, supply and demand

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Authors: K.Ishii, T. Furuichi, A. Fujiyama, S. Hariya

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Rice straw pellets are a promising fuel as a renewable energy source. Financial analysis is needed to make a utilization system using rise straw pellets financially feasible, considering all regional conditions including stakeholders related to the collection and storage, production, transportation and heat utilization. We conducted the financial analysis of feasibility for a heat utilization system using rice straw pellets which has been developed for the first time in Nanporo, Hokkaido, Japan. Especially, we attempted to clarify the effect of factors required for the system to be financial feasibility, such as the heating energy demand and collection and storage method of rice straw. The financial feasibility was found to improve when increasing the heating energy demand and collecting wheat straw in August separately from collection of rice straw in November because the costs of storing rice straw and producing pellets were reduced. However, the system remained financially unfeasible. This study proposed a contractor program funded by a subsidy from Nanporo local government where a contracted company, instead of farmers, collects and transports rice straw in order to ensure the financial feasibility of the system, contributing to job creation in the region.

Keywords: rice straw, pellets, heating energy demand, collection, storage

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Authors: Manish Khanra, Shashank Prabhu

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The decarbonization of hard-to-abate sectors is currently high on the agenda in the EU and its member states, as these sectors have substantial shares in overall GHG emissions while it is facing serious challenges to decarbonize. In particular, the aviation sector accounts for 2.8% of global anthropogenic CO₂ emissions. These emissions are anticipated to grow dramatically unless immediate mitigating efforts are implemented. Hydrogen and its derivatives based on renewable electricity can have a key role in the transition towards CO₂-neutral flights. The substantial shares of energy carriers in the form of drop-in fuel, direct combustion and Hydrogen-to-Electric are promising in most scenarios towards 2050. For creating appropriate policies to ramp up the production and utilisation of hydrogen commodities in the German aviation sector, a detailed analysis of the spatial distribution of supply-demand sites is essential. The objective of this research work is to assess the demand for hydrogen-based alternative fuels in the German aviation sector to achieve the perceived goal of the ‘Net Zero’ scenario by 2050. Here, the analysis of the technological pathways for the production and utilisation of these fuels in various aircraft options is conducted for reaching mitigation targets. Our method is based on data-driven bottom-up assessment, considering production and demand sites and their spatial distribution. The resulting energy demand and its spatial distribution with consideration of technology diffusion lead to a possible transition pathway of the aviation sector to meet short-term and long-term mitigation targets. Additionally, to achieve mitigation targets in this sector, costs and policy aspects are discussed, which would support decision-makers from airline industries, policymakers and the producers of energy commodities.

Keywords: the aviation sector, hard-to-abate sectors, hydrogen demand, alternative fuels, technological pathways, data-driven approach

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Authors: Nina N. Serdar, Jelena R. Pejović

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This paper presents seismic risk assessment of two bridge structure, based on the probabilistic performance-based seismic assessment methodology. Both investigated bridges are tree span continuous RC curved bridges with the difference in column shapes. First bridge (type A) has a wall-type pier and second (type B) has a two-column bent with circular columns. Bridges are designed according to European standards: EN 1991-2, EN1992-1-1 and EN 1998-2. Aim of the performed analysis is to compare seismic behavior of these two structures and to detect the influence of column shapes on the seismic response. Seismic risk assessment is carried out by obtaining demand fragility curves. Non-linear model was constructed and time-history analysis was performed using thirty five pairs of horizontal ground motions selected to match site specific hazard. In performance based analysis, peak column drift ratio (CDR) was selected as engineering demand parameter (EDP). For seismic intensity measure (IM) spectral displacement was selected. Demand fragility curves that give probability of exceedance of certain value for chosen EDP were constructed and based on them conclusions were made.

Keywords: RC curved bridge, demand fragility curve, wall type column, nonlinear time-history analysis, circular column

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Authors: Anne Gesare Timu, Megan Sheahan, Andrew Gache Mude, Rupsha Banerjee

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Women headed households are among the most vulnerable to negative climatic shocks and are often left poorer as a result. Credit provision has been recognized as one way of alleviating rural poverty and developing poor rural households’ resilience to shocks. Much has been documented about credit demand in small-holder agriculture settings in Kenya. However, little is known about demand for credit among pastoral women. This paper analyzes the determinants of demand for credit in the pastoral regions of Marsabit District of Northern Kenya. Using a five wave balanced panel data set of 820 households, a double hurdle model is employed to analyze if shocks, financial literacy and risk aversion affect credit demand among female and male headed households differently. The results show that borrowing goods on credit and monetary credit from informal market segments are the most common sources of credit in the study area. The impact of livestock loss and financial literacy on the decision to borrow and how much to borrow vary with gender. While the paper suggests that provision of credit is particularly valuable in the aftermath of a negative shock and more so for female-headed households, it also explores alternatives to the provision of credit where credit access is a constraint. It recommends further understanding of systems and institutions which could enhance access to credit, and particularly during times of stress, to enable households in the study area in particular and Northern Kenya in general to invest, engage in meaningful development and growth, and be resilient to persistent shocks.

Keywords: female headed households, pastoralism, rural financing, double hurdle model

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Authors: Maryam Ziaee

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This research proposes a new approach and provides an opportunity for manufacturing companies to produce large amounts of products that meet their prospective customers’ tastes, needs, and expectations and simultaneously enable manufacturers to increase their profit. Mass customization is the production of products or services to meet each individual customer’s desires to the greatest possible extent in high quantities and at reasonable prices. This process takes place at different levels such as the customization of goods’ design, assembly, sale, and delivery status, and classifies in several categories. The main focus of this study is on one class of mass customization, called optional customization, in which companies try to provide their customers with as many options as possible to customize their products. These options could range from the design phase to the manufacturing phase, or even methods of delivery. Mass customization values customers’ tastes, but it is only one side of clients’ satisfaction; on the other side is companies’ fast responsiveness delivery. It brings the concept of agility, which is the ability of a company to respond rapidly to changes in volatile markets in terms of volume and variety. Indeed, mass customization is not effectively feasible without integrating the concept of agility. To gain the customers’ satisfaction, the companies need to be quick in responding to their customers’ demands, thus highlighting the significance of agility. This research offers a different method that successfully integrates mass customization and fast production in manufacturing industries. This research is built upon the hypothesis that the success key to being agile in mass customization is to forecast demand, cooperate with suppliers, and control inventory. Therefore, the significance of the supply chain (SC) is more pertinent when it comes to this stage. Since SC behavior is dynamic and its behavior changes constantly, companies have to apply one of the predicting techniques to identify the changes associated with SC behavior to be able to respond properly to any unwelcome events. System dynamics utilized in this research is a simulation approach to provide a mathematical model among different variables to understand, control, and forecast SC behavior. The final stage is delayed differentiation, the production strategy considered in this research. In this approach, the main platform of products is produced and stocked and when the company receives an order from a customer, a specific customized feature is assigned to this platform and the customized products will be created. The main research question is to what extent applying system dynamics for the prediction of SC behavior improves the agility of mass customization. This research is built upon a qualitative approach to bring about richer, deeper, and more revealing results. The data is collected through interviews and is analyzed through NVivo software. This proposed model offers numerous benefits such as reduction in the number of product inventories and their storage costs, improvement in the resilience of companies’ responses to their clients’ needs and tastes, the increase of profits, and the optimization of productivity with the minimum level of lost sales.

Keywords: agility, manufacturing, resilience, supply chain

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Authors: Maria Anjelica P. Ancheta, Roberto S. Soriano, Erickson L. Llaguno

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The main purpose of this study is to examine whether there is a significant relationship between socio-economic class and household water supply demand, through determining or verifying the factors governing water use consumption patterns of households from a sampling from different socio-economic classes in Metro Manila, the national capital region of the Philippines. This study is also an opportunity to augment the lack of local academic literature due to the very few publications on urban household water demand after 1999. In over 600 Metro Manila households, a rapid survey was conducted on their average monthly water consumption and habits on household water usage. The questions in the rapid survey were based on an extensive review of literature on urban household water demand. Sample households were divided into socio-economic classes A-B and C-D. Cluster analysis, dummy coding and outlier tests were done to prepare the data for regression analysis. Subsequently, backward stepwise regression analysis was used in order to determine different statistical models to describe the determinants of water consumption. The key finding of this study is that the socio-economic class of a household in Metro Manila is a significant factor in water consumption. A-B households consume more water in contrast to C-D families based on the mean average water consumption for A-B and C-D households are 36.75 m3 and 18.92 m3, respectively. The most significant proxy factors of socio-economic class that were related to household water consumption were examined in order to suggest improvements in policy formulation and household water demand management.

Keywords: household water uses, socio-economic classes, urban planning, urban water demand management

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Authors: Mykhailo Moskalets, Pablo Burset, Benjamin Roussel, Christian Flindt

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The single-particle injection from the Andreev level and how such injection is simulated using a voltage pulse are discussed. Recently, high-speed quantum-coherent electron sources injecting one- to few-particle excitations into the Fermi sea have been experimentally realized. The main obstacle to using these excitations as flying qubits for quantum-information processing purposes is decoherence due to the long-range Coulomb interaction. An obvious way to get around this difficulty is to employ electrically neutral excitations. Here it is discussed how such excitations can be generated on-demand using the same injection principles as in existing electron sources. Namely, with the help of a voltage pulse of a certain shape applied to the Fermi sea or using a driven quantum dot with superconducting correlations. The advantage of the latter approach is the possibility of varying the electron-hole content in the excitation and the possibility of creating a charge-neutral but spin-dipole excitation.

Keywords: Andreev level, on-demand, single-electron, spin-dipole

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Authors: M. Wajahat, M. Shoaib, A. Waheed

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As a result of increase in world energy demand as well as the demand for heating, refrigeration and air conditioning, energy engineers are now more inclined towards the renewable energy especially solar based thermal driven refrigeration and air conditioning systems. This research is emphasized on solar assisted adsorption refrigeration system to provide comfort conditions for a building in Islamabad. The adsorption chiller can be driven by low grade heat at low temperature range (50 -80 °C) which is lower than that required for generator in absorption refrigeration system which may be furnished with the help of common flat plate solar collectors (FPC). The aim is to offset the total energy required for building’s heating and cooling demand by using FPC’s thus reducing dependency on primary energy source hence saving energy. TRNSYS is a dynamic modeling and simulation tool which can be utilized to simulate the working of a complete solar based adsorption chiller to meet the desired cooling and heating demand during summer and winter seasons, respectively. Modeling and detailed parametric analysis of the whole system is to be carried out to determine the optimal system configuration keeping in view various design constraints. Main focus of the study is on solar thermal loop of the adsorption chiller to reduce the contribution from the auxiliary devices.

Keywords: flat plate collector, energy saving, solar assisted adsorption chiller, TRNSYS

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Authors: Maximilian Richter

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Implementing shared autonomous electric vehicles (SAEVs) has many advantages. The main advantages are achieved when SAEVs are offered as on-demand services by a fleet operator. However, autonomous mobility on demand (AMoD) will be distributed nationwide only if a fleet operation is economically profitable for the operator. This paper proposes a microscopic approach to modeling two implementation scenarios of an AMoD fleet. The city of Zurich is used as a case study, with the results and findings being generalizable to other similar European and North American cities. The data are based on the traffic model of the canton of Zurich (Gesamtverkehrsmodell des Kantons Zürich (GVM-ZH)). To determine financial profitability, demand is based on the simulation results and combined with analyzing the costs of a SAEV per kilometer. The results demonstrate that depending on the scenario; journeys can be offered profitably to customers for CHF 0.3 up to CHF 0.4 per kilometer. While larger fleets allowed for lower price levels and increased profits in the long term, smaller fleets exhibit elevated efficiency levels and profit opportunities per day. The paper concludes with recommendations for how fleet operators can prepare themselves to maximize profit in the autonomous future.

Keywords: autonomous vehicle, mobility on demand, traffic simulation, fleet provider

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Authors: Apurva Saxena, Sunita Gond

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Demand of computation, data storage in large amount are rapidly increases day by day. Cloud computing technology fulfill the demand of today’s computation but this will lead to high power consumption in cloud data centers. Initiative for Green IT try to reduce power consumption and its adverse environmental impacts. Paper also focus on various green computing techniques, proposed models and efficient way to make cloud greener.

Keywords: virtualization, cloud computing, green computing, data center

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Authors: Hossein Panahi, Firouz Fallahi, Sima Nasibparast

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Background & Aim: Unnecessary growth in health expenditures of developing countries in recent decades, and also the importance of physicians’ behavior in health market, have made the theory of physician-induced demand (PID) as one of the most important issues in health economics. Therefore, the main objective of this study is to investigate the hypothesis of induced demand among mental patients who receive services from either psychologists or psychiatrists in East Azerbaijan province. Methods: Using data from questionnaires in 2020 and employing the theoretical model of Jaegher and Jegers (2000) and hierarchical linear modeling (HLM), this study examines the PID hypothesis of selected psychologists and psychiatrists. The sample size of the study, after removing the questionnaires with missing data, is 45 psychologists and 203 people of their patients, as well as 30 psychiatrists and 160 people of their patients. Results: The results show that, although psychiatrists are ‘profit-oriented physicians’, there is no evidence of inducing unnecessary demand by them (PID), and the difference between the behavior of employers and employee doctors is due to differences in practice style. However, with regard to psychologists, the results indicate that they are ‘profit-oriented’, and there is a PID effect in this sector. Conclusion: According to the results, it is suggested that in order to reduce competition and eliminate the PID effect, the admission of students in the field of psychology should be reduced, patient information on mental illness should be increased, and government monitoring and control over the national health system must be increased.

Keywords: physician-induced demand, national health system, hierarchical linear modeling methods, multilevel modela

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Authors: Madia Safdar, G. Amjad Hussain, Mashhood Ahmad

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One of the major challenges of today’s era is peak demand which causes stress on the transmission lines and also raises the cost of energy generation and ultimately higher electricity bills to the end users, and it was used to be managed by the supply side management. However, nowadays this has been withdrawn because of existence of potential in the demand side management (DSM) having its economic and- environmental advantages. DSM in domestic load can play a vital role in reducing the peak load demand on the network provides a significant cost saving. In this paper the potential of demand response (DR) in reducing the peak load demands and electricity bills to the electric users is elaborated. For this purpose the domestic appliances are modeled in MATLAB Simulink and controlled by a module called energy management controller. The devices are categorized into controllable and uncontrollable loads and are operated according to real-time tariff pricing pattern instead of fixed time pricing or variable pricing. Energy management controller decides the switching instants of the controllable appliances based on the results from optimization algorithms. In GAMS software, the MILP (mixed integer linear programming) algorithm is used for optimization. In different cases, different constraints are used for optimization, considering the comforts, needs and priorities of the end users. Results are compared and the savings in electricity bills are discussed in this paper considering real time pricing and fixed tariff pricing, which exhibits the existence of potential to reduce electricity bills and peak loads in demand side management. It is seen that using real time pricing tariff instead of fixed tariff pricing helps to save in the electricity bills. Moreover the simulation results of the proposed energy management system show that the gained power savings lie in high range. It is anticipated that the result of this research will prove to be highly effective to the utility companies as well as in the improvement of domestic DR.

Keywords: controllable and uncontrollable domestic loads, demand response, demand side management, optimization, MILP (mixed integer linear programming)

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Authors: Maisa Sales Gama Tobias, Humberto de Paiva Junior, Luciano Silva Brito, Rui António Rodrigues Ramos

Abstract:

Waterway transport in the Amazon was the first means of access and occupation in the region. For the economic and social matter of high importance, still nowadays one of the main transport modes to several places in the region. To some places, still the only transport mode. With the advent of the pandemic, transport companies that already faced management challenges began to experience unprecedented structural changes and trends in trade and global supply chains. Thus, companies need operational reorganization to maintain the sustainability of the service under the penalty of loss of demand. Allied to this fact, it was observed that the demand presented behavior changes to adapt to this new moment. However, the lack of information about these changes makes it difficult to find solutions to maintain the quality of service. This work aimed to characterize the changes in the demand of waterway passengers through an empirical study with field research involving interviews with users and crew, on-board journeys, and visits to the waterway service company. The case study is the route Belém-Camara, on Marajó Island, in the state of Pará. This line is traditionally the only means of transport for this route, besides air transport on a much smaller scale. The collected data had a descriptive and analytical statistical treatment presented in this work. As the main result, the COVID-19 pandemic has caused significant changes, mainly in trip time and motives and, in the perception itself on service quality by part of the demand, with the increase of trip time and the feeling of insecurity. In conclusion, the service operator must review cost management and business survival strategies and tactics. The viability of the service and the social guarantee of transport proved to be threatened, putting at risk the service to the riverside populations.

Keywords: demand of waterway transport passengers, data analysis, COVID-19, amazonia

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Authors: Seulki Lee, Seoung Bum Kim

Abstract:

Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

Abstract:

Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: T. A. Faria, C. C. Toniolo, L. F. Ribeiro

Abstract:

In order to satisfy the costumer’s needs, many sectors of industry and services has been spending major effort to make its processes more efficient. Facing a situation, when its production cannot cover the demand, the traditional way to achieve the production required involves, mostly, adding shifts, workforce, or even more machines. This paper narrates how lean manufacturing supported a dramatic increase of productivity at a rail welding plant in Brazil in order to meet the demand for the next years.

Keywords: productivity, lean manufacturing, rail welding, value stream mapping

Procedia PDF Downloads 333

Authors: Mamadou Dione, Eric Matzner-lober, Philippe Alexandre

Abstract:

The Energy Transition Act defined by the French State has precise implications on Renewable Energies, in particular on its remuneration mechanism. Until then, a purchase obligation contract permitted the sale of wind-generated electricity at a fixed rate. Tomorrow, it will be necessary to sell this electricity on the Market (at variable rates) before obtaining additional compensation intended to reduce the risk. This sale on the market requires to announce in advance (about 48 hours before) the production that will be delivered on the network, so to be able to predict (in the short term) this production. The fundamental problem remains the variability of the Wind accentuated by the geographical situation. The objective of the project is to provide, every day, short-term forecasts (48-hour horizon) of wind production using weather data. The predictions of the GFS model and those of the ECMWF model are used as explanatory variables. The variable to be predicted is the production of a wind farm. We do two approaches: a direct approach that predicts wind generation directly from weather data, and an integrated approach that estimâtes wind from weather data and converts it into wind power by power curves. We used machine learning techniques to predict this production. The models tested are random forests, CART + Bagging, CART + Boosting, SVM (Support Vector Machine). The application is made on a wind farm of 22MW (11 wind turbines) of the Compagnie du Vent (that became Engie Green France). Our results are very conclusive compared to the literature.

Keywords: forecast aggregation, machine learning, spatio-temporal dynamics modeling, wind power forcast

Procedia PDF Downloads 193