Rasoul Rahmani | Swinburne University of Technology, Hawthorn (original) (raw)

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Papers by Rasoul Rahmani

The integration of solar power generation into microgrid systems has become very popular due to i... more The integration of solar power generation into microgrid systems has become very popular due to its positive environmental aspects and cost effectiveness. Nevertheless the existence of natural intermittency and fluctuations in PV generation incurs extra cost or service interruption in PV-based microgrids. The power generation of PV systems follows a natural schedule based on a sunny day. Similarly, the usage profiles in a microgrid are known from experience. When there is a mismatch in load or generation schedule, the system has to react to maintain a balance. In this work, both a centralized and a decentralized demand-responsive multi-agent control and management system are devised which include backup diesel generation and load curtailment. The latter affects user satisfaction. Wpose new realistic models to measure user satisfaction depending on the type of appliance curtailed. Our simulation shows that the inclusion of demand-side management lowers the cost of a mismatch even when user satisfaction is considered. Expectedly, the centralized implementation achieves a lower cost in more difficult conditions-when the peak consumption happens earlier than anticipated-but the decentralised approach provides acceptable cost levels when a centralized model cannot be implemented.

—In photovoltaic (PV) power generation, partial shading is an unavoidable complication that signi... more —In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions. Index Terms—Differential evolution (DE) algorithm, maximum power point tracking (MPPT), partial shading, particle swarm optimization (PSO), photovoltaic (PV) system.

Electricity load forecasting has become one of the most functioning tools in energy efficiency an... more Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short-term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two-stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems. V

IPEC, 2010 …, Jan 1, 2010

Conference Presentations by Rasoul Rahmani

Recommender systems play an important role in today's electronic markets due to the large benefit... more Recommender systems play an important role in today's electronic markets due to the large benefits they bring by helping businesses understand their customers' needs and preferences. The major preference components modelled by current recom-mender systems include user and item biases, feature value preferences, conditional dependencies, temporal preference drifts, and social influence on preferences. In this paper, we introduce a new hybrid latent factor model that achieves great accuracy by integrating all these preference components in a unified model efficiently. The proposed model employs gradient descent to optimise the model parameters, and an evolutionary algorithm to optimise the hyper-parameters and gradient descent learning rates. Using two popular datasets, we investigate the interaction effects of the preference components with each other. We conclude that depending on the dataset, different interactions exist between the preference components. Therefore , understanding these interaction effects is crucial in designing an accurate preference model in every preference dataset and domain. Our results show that on both datasets, different combinations of components result in different accuracies of recommendation , suggesting that some parts of the model interact strongly. Moreover, these effects are highly dataset-dependent, suggesting the need for exploring these effects before choosing the appropriate combination of components.

IPEC, 2010 …, Jan 1, 2010