Search results for: solar–climatic data
25054 Steps towards the Development of National Health Data Standards in Developing Countries
Authors: Abdullah I. Alkraiji, Thomas W. Jackson, Ian Murray
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The proliferation of health data standards today is somewhat overlapping and conflicting, resulting in market confusion and leading to increasing proprietary interests. The government role and support in standardization for health data are thought to be crucial in order to establish credible standards for the next decade, to maximize interoperability across the health sector, and to decrease the risks associated with the implementation of non-standard systems. The normative literature missed out the exploration of the different steps required to be undertaken by the government towards the development of national health data standards. Based on the lessons learned from a qualitative study investigating the different issues to the adoption of health data standards in the major tertiary hospitals in Saudi Arabia and the opinions and feedback from different experts in the areas of data exchange and standards and medical informatics in Saudi Arabia and UK, a list of steps required towards the development of national health data standards was constructed. Main steps are the existence of: a national formal reference for health data standards, an agreed national strategic direction for medical data exchange, a national medical information management plan and a national accreditation body, and more important is the change management at the national and organizational level. The outcome of this study can be used by academics and practitioners to develop the planning of health data standards, and in particular those in developing countries.Keywords: interoperabilty, medical data exchange, health data standards, case study, Saudi Arabia
Procedia PDF Downloads 33825053 Vocational Education: A Synergy for Skills Acquisition and Global Learning in Colleges of Education in Ogun State, Nigeria
Authors: Raimi, Kehinde Olawuyi, Omoare Ayodeji Motunrayo
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In the last two decades, there has been rising youth unemployment, restiveness, and social vices in Nigeria. The relevance of Vocational Education for skills acquisition, global learning, and national development to address these problems cannot be underestimated. Thus, the need to economically empower Nigerian youths to be able to develop the nation and meet up in the ever-changing global learning and economy led to the assessment of Vocational Education as Synergy for the Skills Acquisition and Global Learning in Ogun State, Nigeria. One hundred and twenty out of 1,500 students were randomly selected for this study. Data were obtained through a questionnaire and were analyzed with descriptive statistics and Chi-square. The results of the study showed that 59.2% of the respondents were between 20 – 24 years of age, 60.8% were male, and 65.8% had a keen interest in Vocational Education. Also, 90% of the respondents acquired skills in extension/advisory, 78.3% acquired skills in poultry production, and 69.1% acquired skills in fisheries/aquaculture. The major constraints to Vocational Education are inadequate resource personnel (χ² = 10.25, p = 0.02), inadequate training facilities (x̅ = 2.46) and unstable power supply (x̅ = 2.38). Results of Chi-square showed significance association between constraints and Skills Acquisition (χ² = 12.54, p = 0.00) at p < 0.05 level of significance. It was established that Vocational Education significantly contributed to students’ skills acquisition and global learning. This study, therefore, recommends that inadequate personnel should be looked into by the school authority in order not to over-stretch the available staff of the institution while the provision of alternative stable power supply (solar power) is also essential for effective teaching and learning process.Keywords: vocational education, skills acquisition, national development, global learning
Procedia PDF Downloads 12825052 A Proposal for U-City (Smart City) Service Method Using Real-Time Digital Map
Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo
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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.Keywords: RDM, multi-source data, big data, U-City
Procedia PDF Downloads 43325051 Energy Related Carbon Dioxide Emissions in Pakistan: A Decomposition Analysis Using LMDI
Authors: Arsalan Khan, Faisal Jamil
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The unprecedented increase in anthropogenic gases in recent decades has led to climatic changes worldwide. CO2 emissions are the most important factors responsible for greenhouse gases concentrations. This study decomposes the changes in overall CO2 emissions in Pakistan for the period 1990-2012 using Log Mean Divisia Index (LMDI). LMDI enables to decompose the changes in CO2 emissions into five factors namely; activity effect, structural effect, intensity effect, fuel-mix effect, and emissions factor effect. This paper confirms an upward trend of overall emissions level of the country during the period. The study finds that activity effect, structural effect and intensity effect are the three major factors responsible for the changes in overall CO2 emissions in Pakistan with activity effect as the largest contributor to overall changes in the emissions level. The structural effect is also adding to CO2 emissions, which indicates that the economic activity is shifting towards more energy-intensive sectors. However, intensity effect has negative sign representing energy efficiency gains, which indicate a good relationship between the economy and environment. The findings suggest that policy makers should encourage the diversification of the output level towards more energy efficient sub-sectors of the economy.Keywords: energy consumption, CO2 emissions, decomposition analysis, LMDI, intensity effect
Procedia PDF Downloads 39825050 Agile Methodology for Modeling and Design of Data Warehouses -AM4DW-
Authors: Nieto Bernal Wilson, Carmona Suarez Edgar
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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects. Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse
Procedia PDF Downloads 40925049 Identifying Model to Predict Deterioration of Water Mains Using Robust Analysis
Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee
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In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences
Procedia PDF Downloads 74325048 Automated Testing to Detect Instance Data Loss in Android Applications
Authors: Anusha Konduru, Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai
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Mobile applications are increasing in a significant amount, each to address the requirements of many users. However, the quick developments and enhancements are resulting in many underlying defects. Android apps create and handle a large variety of 'instance' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of Android, an app can be paused, sent into the background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. However, it is difficult for the programmer to manually test this issue for all the activities. This results in the issue of data loss that the data entered by the user are not saved when there is any interruption. This issue can degrade user experience because the user needs to reenter the information each time there is an interruption. Automated testing to detect such data loss is important to improve the user experience. This research proposes a tool, DroidDL, a data loss detector for Android, which detects the instance data loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.Keywords: Android, automated testing, activity, data loss
Procedia PDF Downloads 23725047 Big Data: Appearance and Disappearance
Authors: James Moir
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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.Keywords: big data, appearance, disappearance, surface, epistemology
Procedia PDF Downloads 42125046 From Data Processing to Experimental Design and Back Again: A Parameter Identification Problem Based on FRAP Images
Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann
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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design
Procedia PDF Downloads 27825045 Exploring the Feasibility of Utilizing Blockchain in Cloud Computing and AI-Enabled BIM for Enhancing Data Exchange in Construction Supply Chain Management
Authors: Tran Duong Nguyen, Marwan Shagar, Qinghao Zeng, Aras Maqsoodi, Pardis Pishdad, Eunhwa Yang
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Construction supply chain management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted building information modeling (BIM) -a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, artificial intelligence (AI) can be integrated to handle this data’s magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.Keywords: construction supply chain management, BIM, data exchange, artificial intelligence
Procedia PDF Downloads 2625044 Representation Data without Lost Compression Properties in Time Series: A Review
Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan
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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction
Procedia PDF Downloads 42825043 Data Mining As A Tool For Knowledge Management: A Review
Authors: Maram Saleh
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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.
Procedia PDF Downloads 20825042 Application of Random Forest Model in The Prediction of River Water Quality
Authors: Turuganti Venkateswarlu, Jagadeesh Anmala
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Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.Keywords: water quality, land use factors, random forest, fecal coliform
Procedia PDF Downloads 19725041 Anomaly Detection Based Fuzzy K-Mode Clustering for Categorical Data
Authors: Murat Yazici
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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data
Procedia PDF Downloads 5325040 Big Data Analytics and Data Security in the Cloud via Fully Homomorphic Encyption Scheme
Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara
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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme
Procedia PDF Downloads 48025039 Performance Analysis of Photovoltaic Solar Energy Systems
Authors: Zakariyya Hassan Abdullahi, Zainab Suleiman Abdullahi, Nuhu Alhaji Muhammad
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In this paper, a thorough review of photovoltaic and photovoltaic thermal systems is done on the basis of its performance based on electrical as well as thermal output. Photovoltaic systems are classified according to their use, i.e., electricity production, and thermal, Photovoltaic systems behave in an extraordinary and useful way, they react to light by transforming part of it into electricity useful way and unique, since photovoltaic and thermal applications along with the electricity production. The application of various photovoltaic systems is also discussed in detail. The performance analysis including all aspects, e.g., electrical, thermal, energy, and energy efficiency are also discussed. A case study for PV and PV/T system based on energetic analysis is presented.Keywords: photovoltaic, renewable, performance, efficiency, energy
Procedia PDF Downloads 51625038 An Approximation of Daily Rainfall by Using a Pixel Value Data Approach
Authors: Sarisa Pinkham, Kanyarat Bussaban
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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.Keywords: daily rainfall, image processing, approximation, pixel value data
Procedia PDF Downloads 38725037 Small and Medium-Sized Enterprises in West African Semi-Arid Lands Facing Climate Change
Authors: Mamadou Diop, Florence Crick, Momadou Sow, Kate Elizabeth Gannon
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Understanding SME leaders’ responses to climate is essential to cope with ongoing changes in temperature and rainfall. This study analyzes the response of SME leaders to the adverse effects of climate change in semi-arid lands (SAL) in Senegal. Based on surveys administrated to 161 SME leaders, this research shows that 91% of economic units are affected by climatic conditions, although 70% do not have a plan to deal with climate risks. Economic actors have striven to take measures to adapt. However, their efforts are limited by various obstacles accentuated by a lack of support from public authorities. In doing so, substantial political, institutional and financial efforts at national and local levels are needed to promote an enabling environment for economic actors to adapt. This will focus on information and training about the threats and opportunities related to global warming, the creation of an adaptation support fund to support local initiatives and the improvement of the institutional, regulatory and political framework.Keywords: small and medium-sized enterprises, climate change, adaptation, semi-arid lands
Procedia PDF Downloads 20825036 A Next-Generation Blockchain-Based Data Platform: Leveraging Decentralized Storage and Layer 2 Scaling for Secure Data Management
Authors: Kenneth Harper
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The rapid growth of data-driven decision-making across various industries necessitates advanced solutions to ensure data integrity, scalability, and security. This study introduces a decentralized data platform built on blockchain technology to improve data management processes in high-volume environments such as healthcare and financial services. The platform integrates blockchain networks using Cosmos SDK and Polkadot Substrate alongside decentralized storage solutions like IPFS and Filecoin, and coupled with decentralized computing infrastructure built on top of Avalanche. By leveraging advanced consensus mechanisms, we create a scalable, tamper-proof architecture that supports both structured and unstructured data. Key features include secure data ingestion, cryptographic hashing for robust data lineage, and Zero-Knowledge Proof mechanisms that enhance privacy while ensuring compliance with regulatory standards. Additionally, we implement performance optimizations through Layer 2 scaling solutions, including ZK-Rollups, which provide low-latency data access and trustless data verification across a distributed ledger. The findings from this exercise demonstrate significant improvements in data accessibility, reduced operational costs, and enhanced data integrity when tested in real-world scenarios. This platform reference architecture offers a decentralized alternative to traditional centralized data storage models, providing scalability, security, and operational efficiency.Keywords: blockchain, cosmos SDK, decentralized data platform, IPFS, ZK-Rollups
Procedia PDF Downloads 2725035 The Effect of Measurement Distribution on System Identification and Detection of Behavior of Nonlinearities of Data
Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri
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In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification
Procedia PDF Downloads 51625034 Critical Appraisal of Different Drought Indices of Drought Predection and Their Application in KBK Districts of Odisha
Authors: Bibhuti Bhusan Sahoo, Ramakar Jha
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Mapping of the extreme events (droughts) is one of the adaptation strategies to consequences of increasing climatic inconsistency and climate alterations. There is no operational practice to forecast the drought. One of the suggestions is to update mapping of drought prone areas for developmental planning. Drought indices play a significant role in drought mitigation. Many scientists have worked on different statistical analysis in drought and other climatological hazards. Many researchers have studied droughts individually for different sub-divisions or for India. Very few workers have studied district wise probabilities over large scale. In the present study, district wise drought probabilities over KBK (Kalahandi-Balangir-Koraput) districts of Odisha, India, Which are seriously prone to droughts, has been established using Hydrological drought index and Meteorological drought index along with the remote sensing drought indices to develop a multidirectional approach in the field of drought mitigation. Mapping for moderate and severe drought probabilities for KBK districts has been done and regions belonging different class intervals of probabilities of drought have been demarcated. Such type of information would be a good tool for planning purposes, for input in modelling and better promising results can be achieved.Keywords: drought indices, KBK districts, proposed drought severity index, SPI
Procedia PDF Downloads 44925033 Building a Scalable Telemetry Based Multiclass Predictive Maintenance Model in R
Authors: Jaya Mathew
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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R
Procedia PDF Downloads 37925032 Trusting the Big Data Analytics Process from the Perspective of Different Stakeholders
Authors: Sven Gehrke, Johannes Ruhland
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Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.Keywords: trust, data mining, CRISP DM, stakeholder management
Procedia PDF Downloads 9425031 Wireless Transmission of Big Data Using Novel Secure Algorithm
Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha
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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance
Procedia PDF Downloads 49025030 System Analysis on Compact Heat Storage in the Built Environment
Authors: Wilko Planje, Remco Pollé, Frank van Buuren
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An increased share of renewable energy sources in the built environment implies the usage of energy buffers to match supply and demand and to prevent overloads of existing grids. Compact heat storage systems based on thermochemical materials (TCM) are promising to be incorporated in future installations as an alternative for regular thermal buffers. This is due to the high energy density (1 – 2 GJ/m3). In order to determine the feasibility of TCM-based systems on building level several installation configurations are simulated and analyzed for different mixes of renewable energy sources (solar thermal, PV, wind, underground, air) for apartments/multistore-buildings for the Dutch situation. Thereby capacity, volume and financial costs are calculated. The simulation consists of options to include the current and future wind power (sea and land) and local roof-attached PV or solar-thermal systems. Thereby, the compact thermal buffer and optionally an electric battery (typically 10 kWhe) form the local storage elements for energy matching and shaving purposes. Besides, electric-driven heat pumps (air / ground) can be included for efficient heat generation in case of power-to-heat. The total local installation provides both space heating, domestic hot water as well as electricity for a specific case with low-energy apartments (annually 9 GJth + 8 GJe) in the year 2025. The energy balance is completed with grid-supplied non-renewable electricity. Taking into account the grid capacities (permanent 1 kWe/household), spatial requirements for the thermal buffer (< 2.5 m3/household) and a desired minimum of 90% share of renewable energy per household on the total consumption the wind-powered scenario results in acceptable sizes of compact thermal buffers with an energy-capacity of 4 - 5 GJth per household. This buffer is combined with a 10 kWhe battery and air source heat pump system. Compact thermal buffers of less than 1 GJ (typically volumes 0.5 - 1 m3) are possible when the installed wind-power is increased with a factor 5. In case of 15-fold of installed wind power compact heat storage devices compete with 1000 L water buffers. The conclusion is that compact heat storage systems can be of interest in the coming decades in combination with well-retrofitted low energy residences based on the current trends of installed renewable energy power.Keywords: compact thermal storage, thermochemical material, built environment, renewable energy
Procedia PDF Downloads 24425029 Photovoltaic-Driven Thermochemical Storage for Cooling Applications to Be Integrated in Polynesian Microgrids: Concept and Efficiency Study
Authors: Franco Ferrucci, Driss Stitou, Pascal Ortega, Franck Lucas
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The energy situation in tropical insular regions, as found in the French Polynesian islands, presents a number of challenges, such as high dependence on imported fuel, high transport costs from the mainland and weak electricity grids. Alternatively, these regions have a variety of renewable energy resources, which favor the exploitation of smart microgrids and energy storage technologies. With regards to the electrical energy demand, the high temperatures in these regions during the entire year implies that a large proportion of consumption is used for cooling buildings, even during the evening hours. In this context, this paper presents an air conditioning system driven by photovoltaic (PV) electricity that combines a refrigeration system and a thermochemical storage process. Thermochemical processes are able to store energy in the form of chemical potential with virtually no losses, and this energy can be used to produce cooling during the evening hours without the need to run a compressor (thus no electricity is required). Such storage processes implement thermochemical reactors in which a reversible chemical reaction between a solid compound and a gas takes place. The solid/gas pair used in this study is BaCl2 reacting with ammonia (NH3), which is also the coolant fluid in the refrigeration circuit. In the proposed system, the PV-driven electric compressor is used during the daytime either to run the refrigeration circuit when a cooling demand occurs or to decompose the ammonia-charged salt and remove the gas from thermochemical reactor when no cooling is needed. During the evening, when there is no electricity from solar source, the system changes its configuration and the reactor reabsorbs the ammonia gas from the evaporator and produces the cooling effect. In comparison to classical PV-driven air conditioning units equipped with electrochemical batteries (e.g. Pb, Li-ion), the proposed system has the advantage of having a novel storage technology with a much longer charge/discharge life cycle, and no self-discharge. It also allows a continuous operation of the electric compressor during the daytime, thus avoiding the problems associated with the on-off cycling. This work focuses on the system concept and on the efficiency study of its main components. It also compares the thermochemical with electrochemical storage as well as with other forms of thermal storage, such as latent (ice) and sensible heat (chilled water). The preliminary results show that the system seems to be a promising alternative to simultaneously fulfill cooling and energy storage needs in tropical insular regions.Keywords: microgrid, solar air-conditioning, solid/gas sorption, thermochemical storage, tropical and insular regions
Procedia PDF Downloads 24125028 One Step Further: Pull-Process-Push Data Processing
Authors: Romeo Botes, Imelda Smit
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In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list
Procedia PDF Downloads 24425027 Extreme Temperature Forecast in Mbonge, Cameroon Through Return Level Analysis of the Generalized Extreme Value (GEV) Distribution
Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph
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In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research.Keywords: forecasting, generalized extreme value (GEV), meteorology, return level
Procedia PDF Downloads 47825026 Impact of Stack Caches: Locality Awareness and Cost Effectiveness
Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang
Abstract:
Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.Keywords: hit rate, locality of program, stack cache, stack data
Procedia PDF Downloads 30325025 Autonomic Threat Avoidance and Self-Healing in Database Management System
Authors: Wajahat Munir, Muhammad Haseeb, Adeel Anjum, Basit Raza, Ahmad Kamran Malik
Abstract:
Databases are the key components of the software systems. Due to the exponential growth of data, it is the concern that the data should be accurate and available. The data in databases is vulnerable to internal and external threats, especially when it contains sensitive data like medical or military applications. Whenever the data is changed by malicious intent, data analysis result may lead to disastrous decisions. Autonomic self-healing is molded toward computer system after inspiring from the autonomic system of human body. In order to guarantee the accuracy and availability of data, we propose a technique which on a priority basis, tries to avoid any malicious transaction from execution and in case a malicious transaction affects the system, it heals the system in an isolated mode in such a way that the availability of system would not be compromised. Using this autonomic system, the management cost and time of DBAs can be minimized. In the end, we test our model and present the findings.Keywords: autonomic computing, self-healing, threat avoidance, security
Procedia PDF Downloads 504