Predictive Data Mining Techniques for Forecasting Tamil Nadu Electricity Board (TNEB) Load Demand: An Empirical Study

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)


In the smart grid environment, the electricity consumers will act in response to electricity demand. And the construction of large power generation plants such as thermal, nuclear, atomic, and wind power stations and plants takes many years. In any field, demand has to be forecasted for smooth running of all works in the society. So, the government must determine the electricity needs well in advance. Many statistical and mathematical methods have been developed to forecast the energy demand in the market environment since restructuring of the electricity power industry. In this paper, predictive data mining models named support vector machine, multilayer perceptron, linear regression, and Gaussian processes are analyzed using real-time electricity data with data mining tool Weka for forecasting the electricity load demand. Many accuracy parameters such as mean absolute error, root-mean-squared error, root relative squared error, and relative absolute error were analyzed to find the best of those four models for forecasting the electricity load demand.


Forecasting demand Support vector machine Multilayer perceptron Gaussian processes Linear regression 


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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Information and Communication EngineeringAnna UniversityChennaiIndia
  2. 2.Electronics and Communication EngineeringK.L.N College of Information TechnologySivagangaiIndia

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