Forecasting Energy Demands based on Ensemble of Classifiers
Soundaryadevi M, Jayashree L.S
Progress in Machines and Systems, Vol. 5, No. 1, 2016.
DOI: 
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Abstract:
Analysis of time series data and accurate prediction of future values are among the most challenging tasks
that the data analysts face in many fields. Forecasting of energy demands is very essential because both insufficient and
excess energy production may lead to a significant reduction of benefits and high storage costs respectively. In order to
discover the regularities in dynamic and non stationary data, improved time series forecasting requires a model that
combines multiple prediction models. The Ensemble approach performs better than single learning model and discovers
the dynamic patterns in Energy time series data. In this paper, we compare the performance of two different Ensemble
learning techniques; Bagging (Bootstrap Aggregating) and stacking in forecasting energy time series data. Stacking
technique used in this paper, combines different classifiers like Radial Basis Function (RBF), Multilayer perceptron (MLP)
and Support Vector Machine (SVM)