Short-term load forecasting using fuzzy logic and ANFIS

This paper presents short-term load forecasting models, which are developed by using fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS). Firstly, historical data are analyzed and weekdays are grouped according to their load characteristics. Then, historical load, temperature difference and season are selected as inputs. In general literature, fuzzy logic hourly load forecasts are tested in the range a few days or a few weeks. Unlike previous studies, the hourly load forecast is carried out for 1 year. This paper shows that fuzzy logic can give good results in very large test data sets for 1 year. Besides, for countries with large areas, the temperature data taken from only one point would lead to increase the forecasting errors. Therefore, the average of temperature for six cities having the maximum power consumption is weighted average. The mean absolute percentage errors of the fuzzy logic and ANFIS models in terms of prediction accuracy are obtained as 2.1 and 1.85, respectively. The results show that the proposed fuzzy logic and ANFIS models are capable of load forecasting efficiently and produce very close values to the actual data and are the alternative way for short-term load forecasting in Turkey.

[1]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[2]  Aysen Demiroren,et al.  Middle Anatolian Region Short-Term Load Forecasting Using Artificial Neural Networks , 2006 .

[3]  M. Bilgic,et al.  Forecasting Turkey's short term hourly load with artificial neural networks , 2010 .

[4]  M. Medeiros,et al.  Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data , 2008 .

[5]  Ismet Erkmen,et al.  A hybrid learning for neural networks applied to short term load forecasting , 2003, Neurocomputing.

[6]  Ioannis B. Theocharis,et al.  A load curve based fuzzy modeling technique for short-term load forecasting , 2003, Fuzzy Sets Syst..

[7]  Adem Alpaslan Altun,et al.  Long Term Electricity Demand Forecasting in Turkey Using Artificial Neural Networks , 2010 .

[8]  M. Bilgic,et al.  Forecasting Turkey's short term hourly load with artificial neural networks , 2010, IEEE PES T&D 2010.

[9]  Mansour Talebizadeh,et al.  Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models , 2011, Expert Syst. Appl..

[10]  B. Araabi,et al.  Short-Term Load Forecasting With a New Nonsymmetric Penalty Function , 2011, IEEE Transactions on Power Systems.

[11]  Jukka Saarinen,et al.  Short term electric load forecasting using a neural network with fuzzy hidden neurons , 2005, Neural Computing & Applications.

[12]  I. Erkmen,et al.  Short term load forecasting using genetically optimized neural network cascaded with a modified Kohonen clustering process , 1997, Proceedings of 12th IEEE International Symposium on Intelligent Control.

[13]  Ismet Erkmen,et al.  Four methods for short-term load forecasting using the benefits of artificial intelligence , 2003 .

[14]  M. E. El-Hawary,et al.  Fuzzy short-term electric load forecasting , 2004 .

[15]  Ashwani Kumar,et al.  Short-Term Load Forecasting in Deregulated Electricity Markets using Fuzzy Approach , 2010 .

[16]  H. Bevrani,et al.  A fuzzy inference model for short-term load forecasting , 2012, 2012 Second Iranian Conference on Renewable Energy and Distributed Generation.

[17]  Ismet Erkmen,et al.  Intelligent short-term load forecasting in Turkey , 2006 .

[18]  Cheng-Chin Chiang,et al.  A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting , 2006, Neural Computing & Applications.

[19]  S. Pandian,et al.  Fuzzy approach for short term load forecasting , 2006 .

[20]  S. Koopman,et al.  An Hourly Periodic State Space Model for Modelling French National Electricity Load , 2007 .

[21]  Luis Neves,et al.  Short‐term load forecasting based on support vector regression and load profiling , 2014 .

[22]  Rob J Hyndman,et al.  Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.

[23]  Muammer Gökbulut,et al.  Tachogenerator DC Motor Speed Control with PID and Fuzzy Logic , 2011 .

[24]  M. Mordjaoui,et al.  Short term electric load forecasting using Neuro-fuzzy modeling for nonlinear system identification , 2010 .

[25]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[26]  A.M. Escobar,et al.  Application of support vector machines and ANFIS to the short-term load forecasting , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America.

[27]  Mehmet Kurban,et al.  Short-term load forecasting without meteorological data using AI-based structures , 2015 .

[28]  M. Çunkaş,et al.  Turkey's Electricity Consumption Forecasting Using Genetic Programming , 2011 .

[29]  Kyung-Bin Song,et al.  Hybrid load forecasting method with analysis of temperature sensitivities , 2006, IEEE Transactions on Power Systems.