International Journal of Swarm Intelligence and Evolutionary Computation

School of Engineering, Information Technology and Physical Sciences, Federation University, Melbourne, Australia

Biography

Dr.Buvana Selvaraj is cuurently working in the department of Information Tecnhology and Physical Sciences, Federation University, Melbourne located in Australia. Over the past 9+ years he have worked in several projects, providing testing, support, development and post implementation testing in various companies in Australia such as MMG Limited Australia, CSC Australia Pty Limited, Australia Post, Komatsu Australia Pty Limited, Fujitsu Australia Pty Limited and at Iesupport Pty Limited as a permanent and contract employee. In addition, he have experience in designing automation framework for real-time complex projects in HP Mercury Quality Center 10, QTP 9.2, Worksoft Certify and SAP eCATT.

Publications

• Research   
  Short-Term Forecasting of Load and Renewable Energy Using Artificial Neural Network
  Author(s): Ram Srinivasan*, Venki Balasubramanian and Buvana Selvaraj
  
  Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for Short-Term Electrical Load Forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularization (BR) and Levenberg–Marqua.. View more»
  

  DOI: 10.35248/2090-4908.20.9.192

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