Fahad Javed

Gift University · Computer Science

Efficient energy management of computing infrastructure has been shown to reduce the operational expenses of enterprises as well as contribute in reducing the carbon footprint of the organization. Various studies have proposed methods to reduce the energy consumption of computing devices at server farms, commercial offices and in consumer household...

Efficient energy management of computing infrastructure has been shown to reduce the operational expenses of enterprises as well as contribute in reducing the carbon foot-print of the organization. Various studies have proposed methods to reduce the energy consumption of computing devices at server farms, commercial offices and in consumer househol...

Smart grids coulpled with efficient 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 red...

Growing power demand and carbon emissions is motivating utility providers to introduce smart power systems. One of the most promising technology to deliver cheaper and smarter electricity is demand side management. A DSM solution controls the devices at user premises in order to achieve overall goals of lower cost for consumer and utility. To achie...

Economic dispatch and demand side management are two of the most important tools for efficient energy management in the grid. It is a casual observation that both these processes are intertwined and thus complement each other. Strategies aiming to optimize economic dispatch have implications for demand side management techniques and vice versa. In...

Home energy management systems (HEMs) are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network (HAN) that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in recent years but they are still expensive enough to prohibit...

Smart grids provide newer ways of energy production, transmission, and distribution. In a smart grid finer control of electrical devices in household and buildings are implemented to better manage energy demand and supply. However, this finer control commonly known as demand side management (DSM) requires extensive simulation at various levels befo...

Choice of load signature or feature space is one of the most fundamental design choices for non-intrusive load monitoring or energy disaggregation problem. Electrical power quantities, harmonic load characteristics, canonical transient and steady-state waveforms are some of the typical choices of load signature or load signature basis for current r...

Smart grid is the next generation of electricity generation, transmission and distribution technology. A major component of smart grid is an overlay communication network for two-way communication between the power providers and the customers. With this feature smart grid provides exciting new ways of energy management and conservation. One of ways...

The impending energy crisis has driven up the cost of electricity at an exponential rate. Managing electric consumption thus has become a very crucial task especially for home consumers. In this paper we present EnerPlan, a non-intrusive method to aid consumers to reduce their energy cost by advising them a consumption plan for their devices. Our s...

The electric grid is changing. With the smart grid the demand response (DR) programs will hopefully make the grid more resilient and cost efficient. However, a scheme where consumers can directly participate in demand management requires new efforts for forecasting the electric loads of individual consumers. In this paper we try to find answers to...

Autonomic and autonomous systems exist within a world view of their own. This world view is created from the training data and assumptions that were available at their inception. In most of these systems this world view becomes obsolete over time due to changes in the environment. This brings a level of inaccuracy in the autonomic behavior of the s...

In this paper, we propose and evaluate a self-optimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our self-optimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender wi...

Dynamically adaptive systems (DAS) such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough to incorporate self-* properties. To this end, we have developed a dynamic mathematical modeling framework for run...

Dynamically adaptive systems (DAS), such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough for self-management. To this end, we have developed a dynamic mathematical modeling framework for runtime modelin...

Optimizing self-evolving and dynamically changing systems is a grand challenge. In order to apply optimizations almost all conventional optimization techniques require a runtime system model. However, system models and their solution techniques vary in their strengths and limitations. For a rigid system, a single system model is acceptable. But if...

Shortage of electricity is a major problem in many developing countries. Unfortunately, for some of these countries the only solution to this problem is to shut down complete electricity supply to a few neighborhoods to make up for the gap between demand and supply. To this end, we have developed a self-optimization approach to reduce the gap betwe...

Efficient energy consumption in large sets of electric devices is a complex problem since it requires a balance between many competing factors. Presently, self-optimization techniques work expeditiously on small and relatively less complex problems. However, these techniques are not shown to be scalable on large and complex problems. In this paper...