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2013, vol. 17, iss. 2, pp. 97-99
Comparative analysis of temperature and load time series influence on short-term load forecasting
University of Novi Sad, Faculty of Technical Science

emailslobodan.ilic@uns.ac.rs
Keywords: short-term load forecasting; load data series; power industry; temperature influence; multiple linear regression
Abstract
This paper presents several Multiple Linear Regression models for short-term load forecasting, which are intended for use as benchmark tests. A comparative analysis based on models’ performance is presented, which resulted from helping a US power utility to develop a commercial product. The main goal of the analysis carried out is to determine which variables influence the electrical load the most. Models which take into account only weather variables are developed on one side, and the ones containing only load shape information on the other. Both models are of linear regression type, while the input variables are selected based on correlation analysis. The influence of the different variables is than compared, and the model that uses both weather and load shape information is developed. The proposed benchmark models are used as a tool in developing more sophisticated methods for electrical load prediction, such as artificial neural networks, or support vector machines. The analysis of proposed models could provide useful results in dealing with a real-life forecasting algorithm and re-evaluation of methods already presented.
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article language: English
document type: Original Scientific Paper
published in SCIndeks: 02/09/2013

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