Thair Mahmoud

An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use

New approach is used to generate combinatorial test suitesFuzzy-based strategy developed to generate the test cases using adaptive techniqueIt is a new approach in developing Artificial Intelligent and Expert systemsThe strategy proves its efficiency, performance compared to its counterpartsThe strategy proves its effectiveness also through the case study Recent research activities have demonstrated the effective application of combinatorial optimization in different areas, especially in software testing. Covering array (CA) has been introduced as a representation of the combinations in one complete set. CAλ(N; t, k, v) is an N?×?k array in which each t-tuple for an N × t sub array occurs at least λ times, where t is the combination strength, k is the number of components (factors), and v is the number of symbols for each component (levels). Generating an optimized covering array for a specific number of k and v is difficult because the problem is a non-deterministic polynomial-time hard computational one. To address this issue, many relevant strategies have been developed, including stochastic population-based algorithms. This paper presents a new and effective approach for constructing efficient covering arrays through fuzzy-based, adaptive particle swarm optimization (PSO). With this approach, efficient covering arrays have been constructed and the performance of PSO has been improved for this type of application. To measure the effectiveness of the technique, an empirical study is conducted on a software system. The technique proves its effectiveness through the conducted case study.

Fuzzy logic for smart utilisation of Storage Devices in a typical microgrid

Efficient utilisation of Storage Devices (SD) among multiple sources of dispatch within a typical microgrid have a substantial impact on reducing the economic and environmental generation costs in that particular microgrid. Eventually, managing the multiple sources that supply energy simultaneously is a big engineering challenge. The complexity rises due the uncertainty of demand, generation cost, availability of renewable energy sources and (charging/discharging) time and price for the installed SD. This paper introduces a utilisation method that makes the SD more efficient in supplying the electricity within a typical medium size enterprise microgrid. The method is simply targeting the dynamic charging price for the SD to achieve a profitable charging, and also to maximise the opportunity of participation during the SD lifetime. A fuzzy logic based adaptive charging price is set for charging the SD based on the microgrids local generation price at the time of charging, and the amount of the daily SD participation in the microgrid dispatch. By considering the economic and environmental generation costs in 30-minute operation intervals, a multi-objective Particle Swarm Optimisation (PSO) method is applied to optimise the energy dispatch for the managed microgrid. In addition, a switching mechanism based on the SD status is integrated with the proposed PSO to deal with the variable operation scenarios in the managed microgrid. The proposed optimisation technique has been tested on the realistic operation scenarios of the power grid of the Joondalup Campus of Edith Cowan University in Western Australia. The simulation results showed a reasonable amount of efficiency improvement with a range of benefits in cutting the generation cost for the targeted case study.

Fuzzy Inference System in Energy Demand Prediction

Fuzzy Inference Systems (FIS) have been widely used in many applications including image processing, optimization, control and system identification. Among these applications, we would like to investigate energy demand modelling. Generally, developing an energy demand model is the challenge of interpreting the historical use of energy in an electric power network into equations which approximate the future use of energy. The developed model’s equations are coded and embedded into a processor based system, which predicts the output when a certain type of input occurs. However, the range and quality of prediction is still limited within the knowledge supplied to the model. The major concern about the energy demand modelling is to categorize the type of prediction in short or longterm prediction. In addition, it is crucial to categorize the type of the power network to be modelled. Since identifying the useful historical operation data for setting the model parameters is crucial in modelling, the operation history of the modelled systems must to be analysed. In simple terms, modelling energy demand is the art of identifying the right modelling technique and system’s operation parameters. The operation parameters differ based on the type and size of the modelled system. So, taking into consideration why the system is modelled will justify the selection of modelling techniques. Among the reasons for modelling energy demand is managing the use of energy through an Energy Management System (EMS).