International Journal of Swarm Intelligence and Evolutionary Computation

Department of Electrical and Electronics, School of Electrical and Technology Central, Queensland University, Melbourne, Australia

Biography

Dr.Ram Srinivasan received the master’s degree in electrical engineering from Central Queensland University, Melbourne. He is currently with the School of Electrical Engineering and Technology, Central Queensland University, Melbourne, Australia. He is also working on applying deep learning and machine learning techniques in healthcare and VANET with a Fellow Researcher Dr. Venki Balasubramanian. His research interests include power systems, numerical methods, artificial intelligence methods, deep learning, VANET, computer networks, and operating systems

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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