As energy demand is increasing, power systems’ complexities are also increasing. With growing energy demand, new ways and techniques are formulated by researchers to increase the efficiency and reliability of power systems. A distribution system, which is one of the most important entities in a power system, contributes up to 90% of reliability problems. For a sustainable supply of power to customers, the distribution system reliability must be enhanced. Distributed generation (DG) is a new way to improve distribution system reliability by bringing generation nearer to the load centers. Artificial intelligence (AI) is an area in which much innovation and research is going on. Different scientific areas are utilizing AI techniques to enhance system performance and reliability. This work aims to apply DG as a distributed source in a distribution system to evaluate its impacts on reliability. The location of the DG is a design criteria problem that has a relevant effect on the reliability of the distribution system. As the distance of load centers from the feeder increases, outage durations also increase. The reliability was enhanced, as the SAIFI value was reduced by almost 40%, the SAIDI value by 25%, and the EENS value by 25% after injecting DG into the distribution network. The artificial neural network (ANN) technique was utilized to find the optimal location of the DG; the results were validated by installing DG at prescribed localities. The results showed that the injection of DG at proper locations enhances the reliability of a distribution system. The proposed approach was applied to thte Roy Billinton Test System (RBTS). The implementation of the ANN technique is a unique approach to the selection of a location for a DG unit, which confirms that applying this computational technique could decrease human errors that are associated with the hit and trial methods and could also decrease the computational complexities and computational time. This research can assist distribution companies in determining the reliability of an actual distribution system for planning and expansion purposes, as well as in injecting a DG at the most optimal location in order to enhance the distribution system reliability.

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

Organizations and individuals have been facing disasters globally and locally since hundreds of years. Increasing number of natural disasters has demonstrated the paramount importance of the natural hazards subject for the protection of environment and the citizens. Now more than ever, designing an efficient traffic system, moving vast amounts of helping goods quickly and safely across great distances is one of our most pressing needs. Satellite navigation systems are changing the way in which we travel from place to place whether by land, sea or air and whether in remote areas or through congested city streets. Global positioning system (GPS) and its role in advanced transportation projects is inseparable and become a synonym. The management and operations of vehicles and giving real-time information to users that will lead to cost-effective and satisfied service to passengers is possible nowadays using a GPS based vehicle navigation system and communication via remote sensing. This paper describes how the already developed fleet management and remote sensing technologies can be merged together to form an efficient disaster management system

In open multiagent systems individual components act in an autonomous and uncertain manner, thus making it difficult for the participating agents to interact with one another in a reliable environment. A reputation model can however predict the behavior of the agents and can optimize the issues related with dynamism. In this paper we present a critical analysis of an existing model for open MAS (FIRE) against our proposed swarm intelligence based reputation model. The model not only provides an optimum solution to various issues of reputation but also provides the mechanism of getting information from various sources.

It is indeed a great pleasure to introduce this collection of papers to be presented at 2015 International Conference on Emerging Technologies (ICET- 2015) which is eleventh of its series being held under the auspices of IEEE Islamabad section. The main objective of holding this Flagship International Conference of IEEE Islamabad Section is to provide a unique platform for a diverse community composed of scholars, researchers, developers, academicians and practitioners from all over the world. The scope of the conference is based on science-based innovations with the potential to create a new industry or transform an existing one which can substantially alter the business and social environment. The 11th International Conference on Emerging Technologies covers multidisciplinary topics related to science and emerging technologies which include Communications, Signal and Image Processing, Network Security, Power, and other trending topics.

Swarm Intelligence Based Reputation Model for Open Multi agent Systems: 10.4018/978-1-60566-898-7.ch014: In open multiagent systems, individual components act in an autonomous and uncertain manner, thus making it difficult for the participating agents to interact