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6 Papers

A Novel Demand Side Management (DSM) Technique for Electric Grids with High Renewable Energy Mix using Hierarchical Clustering of Loads

Topics: Load Balancing in Smart Grids; Greener Systems Planning and Design; Renewable Energy Resources; Energy Management Systems

Authors: Muhammad Arbab Arshad , Syed Hasnain and Naveed Arshad

Affiliation: Department of Computer science, Lahore University of Management Sciences, Sector U, DHA, Lahore and Pakistan

Digital Object Identifier: 10.5220/0007721501370142

Proceedings of SMARTGREENS 2019, ISBN: 978-989-758-373-5

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Shortfall can occur at irregular times in an electric grid that has high a concentration of intermittent renewable energy sources. Many methods are being studied, proposed and used to change the demand in order to match the supply with the most common being Load Curtailment. New DSM techniques have evolved as a result of advancements in AMI technologies. The goal is to minimize the difference between supply and demand at the time of shortfall. Our proposed algorithm selects consumers and limits their energy consumption by profiling the commercial sites based on their historical consumption behaviour. Then, to save the required amount of energy, the sites with peak consumption levels with respect to their own daily usage are targeted. Thus, it harnesses the maximum potential of electricity deduction from a site while minimizing its effects on the residents.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Energy and Economy

Greener Systems Planning and Design

Renewable Energy Resources

Energy-Aware Systems and Technologies

Load Balancing in Smart Grids

Smart Grids

Soft Load Shedding: An Efficient Approach to Manage Electricity Demand in a Renewable Rich Distribution System

Topics: Load Balancing in Smart Grids; Greener Systems Planning and Design; Renewable Energy Resources; Energy Management Systems (EMS)

Authors: Tayyab Aslam and Naveed Arshad

Affiliation: Lahore University of Management Sciences, Pakistan

Digital Object Identifier: 10.5220/0006774001010107

Proceedings of SMARTGREENS 2018, ISBN: 978-989-758-292-9

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Matching demand and supply of electricity generation is difficult in a renewable-rich system. This is partly due to the long-term variability and short-term uncertainty of wind and solar. Utilities use several approaches to deal with the variations of renewable generation. Some of these include having extra fossil fuel based peaker plants, managing flexible loads using demand-side management, real-time pricing etc. In this paper, we present another approach to manage supply variations by introducing semi-flexible loads at the demand side. These semi-flexible loads are residential loads that cannot be shut down or be moved completely to another hour but have the possibility to shed a small percentage of their load for a short time. This approach called soft load shedding is challenging as residential customers have the multitude of energy usage patterns. In this paper we compare and contrast three soft load shedding techniques and discuss their strengths and shortcomings in matching the demand with available supply.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Energy and Economy

Greener Systems Planning and Design

Renewable Energy Resources

Energy Management Systems (EMS)

Load Balancing in Smart Grids

Smart Grids
Energy-Aware Systems and Technologies

Towards Providing Full Spectrum Antenatal Health Care in Low and Middle Income Countries

Topics: Decision Support Systems; Wearable Health Informatics; Mobile Technologies for Healthcare Applications; Healthcare Management Systems; e-Health

Authors: Muhammad Abubakar 1 , Amina Bibi 1 , Rashad Hussain 1 , Zohra Bibi 1 , Asma Gul 2 , Zahid Bashir 2 , Salman Noshear Arshad 1 , Momin Ayub Uppal 1 and Safee Ullah Chaudhary 1

Affiliations: 1 Lahore University of Management Sciences, Pakistan , 2 Shalamar Institute of Health Sciences, Pakistan

Digital Object Identifier: 10.5220/0005823104780483

Proceedings of HEALTHINF 2016/BIOSTEC, ISBN: 978-989-758-170-0

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The provision of Antenatal Care (ANC) for pregnant women plays a vital role in ensuring infant and maternal health. Limited access to antenatal care in Low and Middle Income Countries (LMIC) results in high Infant and Maternal Mortality Rate (IMR and MMR, respectively). In this work, we propose a cloud-based clinical Decision Support System (DSS) integrated with a wearable health-sensor network for patient self-diagnosis and real time health monitoring. Patient assessment is performed by evaluating the human-input coupled with sensor-generated symptomatic information using a Bayesian network driven DSS. High risk pregnancies can be identified and monitored along with dispensing of consultant advice directly to the patient. Patient and disease incidence data is stored on the cloud for tuning probabilities of the Bayesian network towards improving accuracy of predicting anomalies within the epidemiological context. The system therefore, aims to control IMR and MMR by providing ubiquitous access to ANC in LMICs. A scaled-up implementation of the proposed system can help reduce patient influx at the limited tertiary care centers by referring low-risk cases to primary or secondary care establishments.
Collapse Button Related Ontology Subjects/Areas/Topics (27)
Artificial Intelligence

Decision Support Systems, Remote Data Analysis

Mobile Technologies

Biomedical Engineering

Devices

Mobile Technologies for Healthcare Applications

Biomedical Signal Processing

Distributed and Mobile Software Systems

Neural Rehabilitation

Business Analytics

e-Health

Neurotechnology, Electronics and Informatics

Cardiovascular Technologies

Health Engineering and Technology Applications

Physiological Computing Systems

Cloud Computing

Health Information Systems

Platforms and Applications

Computing and Telecommunications in Cardiology

Healthcare Management Systems

Software Engineering

Data Engineering

Human-Computer Interaction

Symbolic Systems

Decision Support Systems

Knowledge-Based Systems

Wearable Sensors and Systems

DeLi2P - A User Centric, Scalable Demand Side Management Strategy for Smart Grids

Topics: Load Balancing in Smart Grids; Optimization Techniques for Efficient Energy Consumption; Energy Management Systems (EMS)

Authors: Syed Muhammad Ali 1 , Mohammad Naveed 2 , Fahad Javed 3 , Naveed Arshad 2 and Jahangir Ikram 2

Affiliations: 1 University of British Columbia, Canada , 2 Lahore University of Management Sciences, Pakistan , 3 GIFT University, Pakistan

Digital Object Identifier: 10.5220/0005437301480156

Proceedings of SMARTGREENS 2015, ISBN: 978-989-758-105-2

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Smart grids coulpled with effiicient demand side management (DSM) is an important step for greener cities of the future. DSM has the potential to significantly improve smart grid operations by reducing the peak to average ratio. Current DSM schemes are able to reduce peak load by as much as 30% which can translate to significant cost savings and reduction in green house emissions. But for realistic deployment of DSM systems in the grid there are two very important aspects which need to be considered: scalability and user acceptability. Since the current DSM algorithms are required to control potentially hundreds of thousands of devices, they have to be scalable and tractable for such myriad numbers. On the other hand DSM affects the life style of the consumer and this should be as less disruptive as possible. The various DSM techniques proposed in the literature attempt to first reduce the cost and then attempt to resolve one of the two aforementioned aspects. The result is that the techniques are either scalable or are only considerate of the deadlines of the consumers. An ideal system should cater to both of these aspects. Our system Deli2P is user centric and scalable thus catering to both of these aspects. Essentially we provide to the consumer a deadline centric interface. The deadlines solutions are generally not scalable. But instead of solving this problem as a scheduling for deadline problem we transform the problem to a priority-based problem thus making it scalable for large number of devices. Our results show that with this scheme we can reduce peak power by upto 30% but without violating consumers` deadlines.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Energy and Economy

Energy-Aware Systems and Technologies

Optimization Techniques for Efficient Energy Consumption

Energy Management Systems (EMS)

Load Balancing in Smart Grids

Smart Grids

Enhanced LZMA and BZIP2 for Improved Energy Data Compression

Topics: Energy Monitoring; Energy Profiling and Measurement; Energy Management Systems (EMS)

Authors: Zaid Bin Tariq 1 , Naveed Arshad 2 and Muhammad Nabeel 2

Affiliations: 1 Department of Computer Science and School of Science and Engineering, Pakistan , 2 Lahore Univeristy of Management Sciences, Pakistan

Digital Object Identifier: 10.5220/0005454202560263

Proceedings of SMARTGREENS 2015, ISBN: 978-989-758-105-2

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Smart grid is the next generation of electricity production, transmission and distribution system. This is possible through an overlayed communication layer with the power delivery layer. Due to this communication layer smart grids produce enormous amounts of data. This data may be analyzed for improving the quality of service of smart grids. However, handling such enormous amount of data is a challenge. LZMA and BZIP2 are two industrial strength compression techniques. In this paper we present an enhanced version of these two schemes specifically targeted to smart grid data through a pre-processing step. Our results show that while the original LZMA is able to compress the data size to around 80% our enhanced scheme using the preprocessing is able to reduce the size of the smart grid data to 98% on average.
Collapse Button Related Ontology Subjects/Areas/Topics (5)
Energy and Economy

Energy Monitoring

Energy-Aware Systems and Technologies
Energy Management Systems (EMS)

Energy Profiling and Measurement

Preserving Privacy in Collaborative Business Process Composition

Topics: Privacy; Security and Privacy in IT Outsourcing; Security and Privacy in Web Services

Authors: Hassaan Irshad 1 , Basit Shafiq 1 , Jaideep Vaidya 2 , Muhammad Ahmed Bashir 1 , Shafay Shamail 1 and Nabil Adam 2

Affiliations: 1 Lahore University of Management Sciences, Pakistan , 2 Rutgers University, United States

Digital Object Identifier: 10.5220/0005567801120123

Proceedings of SECRYPT 2015/ICETE, ISBN: 978-989-758-117-5

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Collaborative business process composition exploits the knowledge of existing business processes of related organizations to compose an executable business process for a given organization based on its requirements and design specifications. Typically, this requires organizations to share and upload their existing business process execution sequences to a central repository. However, even after masking of confidential data, the execution sequences may still include sensitive business information which organizations may not want to share with their competitors. To address this issue, we develop a privacy-preserving Business Process Recommendation and Composition System (BPRCS), that generates a differentially private dataset of execution sequences which can be published and shared with other organizations for composition and implementation of their business processes. We also employ process mining and classification techniques on this differentially private dataset to regenerate the executable business process workflow. We experimentally validate the effectiveness of our approach.
Collapse Button Related Ontology Subjects/Areas/Topics (5)
Data and Application Security and Privacy

Privacy

Security and Privacy in Web Services
Information and Systems Security

Security and Privacy in IT Outsourcing

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