Research Paper | Computer Science & Engineering | India | Volume 3 Issue 9, September 2014
Short Term Load Forecasting by Using Data Mining Techniques
Prof. Maya Shelke, Prashant Dattatraya Thakare
In this paper reports short term load forecasting is conducted by Holt-winter model and K-NN algorithm for classification. The different algorithms and techniques like Classification, Clustering, Regression, Decision trees, Nearest neighbors methods etc., are used for knowledge discovery from database. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. The paper focuses mainly those techniques that are most commonly used for short term load forecasting. For classification purpose the month wise electric load consumption dataset is gathered from Symbiosis Institute of technology, pune campus of the year 2012 and 2013.Results have been classifiedby measuring Holt-winters model parameter and results has been conducted that adding parameter like temperature and event summarized to highlight the accuracy.
Keywords: Holt-Winters Model, General load forecasting technique, k-NN, short term load forecasting
Edition: Volume 3 Issue 9, September 2014
Pages: 1363 - 1367
How to Cite this Article?
Prof. Maya Shelke, Prashant Dattatraya Thakare, "Short Term Load Forecasting by Using Data Mining Techniques", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SEP14450, Volume 3 Issue 9, September 2014, 1363 - 1367
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