Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data

Energy production and distributing have critical importance for all countries especially developing countries. Studies about energy consumption, distributing and planning have much importance at the present day. In order to manage any power plant or take precautions about energy subject, many kinds of observations are used for short, mid and long term forecasting. Especially long term forecasting is in need to plan and carry on future energy demand and investment such as size of energy plant and location. Long term forecasting often includes power consumption data for past years, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Long term forecasting data vary from one month to several years. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression models. In this study, artificial intelligence is used to forecast long term energy demand. Artificial intelligences are widely used for engineering problems to solve and obtain valid solutions. Adaptive neural fuzzy inference system is one of the most famous artificial intelligence methods and has been widely used in literature. In addition to numerical inputs, Adaptive neural fuzzy inference system has linguistics inputs such as good, bad and ugly. Adaptive neural fuzzy inference system is used to obtain long term forecasting results and the results are compared to mathematical methods to show validity and error levels. In order to show error levels, mean absolute error and mean absolute error percentage are used. Mean absolute error and mean absolute error percentages are very common and practical methods in literature. The obtained error results, from 2003 to 2025, mean absolute error and mean absolute percentage error are 1.504313 and 0.82439, respectively. Success of Adaptive neural fuzzy inference system for energy demand forecasting is 99.17%. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the organizing committee of 2nd International Conference on Advances in Energy Engineering (ICAEE).