Department of Industrial Engineering, Bayburt University, Bayburt, Turkey
Energy is one of the most critical inputs in social and economic development, is an essential factor in increasing living standards and creating sustainable development. Since energy is an indispensable input in all sectors, energy dependence contributed to the necessity of countries' energy policy. It has critical importance to predict energy demand to determine energy policies. According to Turkey's annual energy consumption, the industry sector has consumed the most energy in the last five years. The prediction and analysis of energy demand with economic data in the industrial sector is an essential indicator of the economic development relationship between energy demand and industry. In this study, Multiple Linear Regression (MLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and optimized ANFIS with Particle Swarm Optimization (PSO) methods are employed to forecast energy demand for Turkish industrial sectors. The indicators which affect energy consumption were determined to estimate the energy demand. The 30-year dataset between 1990 and 2019 was split as training and test set. MLR, ANFIS and PSO- ANFIS were compared according to performance evaluation, and the most proper model was identified. The coefficient of determination (R2) for PSO-ANFIS, MLR, and ANFIS models are 0.9951, 0.9889, and 0.9932 in the training stage, and 0.9423, 0.9181, and 0.8776 in the testing stage, respectively. The study results indicated that the PSO-ANFIS model showed superior prediction capability with the least estimation error than MLR and ANFIS models. Consequently, parameters tuned PSO-ANFIS is able to predict the industrial energy demand in Turkey with high accuracy.
Industrial Energy Demand, Forecasting, ANFIS, PSO-ANFIS, MLR
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