International Journal of Swarm Intelligence and Evolutionary Computation

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

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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