Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting

Authors

  • Nurettin Cetinkaya

DOI:

https://doi.org/10.18201/ijisae.2017533858

Keywords:

ANFIS, Dynamic programming, Electrical load forecasting, ENPEP, MAED

Abstract

Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.

Downloads

Download data is not yet available.

References

Toksari M.D. (2009). Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey, Energy Policy, Vol: 37, pp. 1181-1187.

Badran I., El-Zayyat H., Halasa G. (2008). Short-Term and Medium-Term Load Forecasting for Jordan's Power System, American Journal of Applied Sciences, Vol: 5(7), pp. 763-768.

Kermanshahi B., Iwamiya H. (2002). Up to year 2020 load forecasting using neural nets, Electrical Power and Energy Systems, Vol: 24, pp. 789-797.

Haydari Z., Kavehnia F., Askari M., Ganbariyan M. (2007). Time-series load modelling and load forecasting using neuro-fuzzy techniques, 9th International Conference on EPQU, pp.1-6.

Hamzaçebi C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases, Energy Policy, Vol: 35, pp. 2009-2016.

Unler A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025, Energy Policy, Vol: 36, pp. 1937-1944.

Ceylan H., Ozturk H.K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach, Energy Conversion and Manag. Vol: 45 (15-16), pp. 2525-2537.

Ediger V., Tatlıdil H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns, Energy Conversion and Manag. Vol: 43, pp. 473-487.

Ozturk H.K., Canyurt O.E., Ceylan H., Hepbasli A. (2005). Electricity estimation using genetic algorithm approach: A case study of Turkey, International Journal of Energy, Vol: 30(7), pp. 1003-1012.

Sozen A., Arcaklioglu E., Ozkaymak M. (2005). Turkey’s net energy consumption, Applied Energy, Vol: 81(2), pp. 209-221.

Utlu Z., Hepbasli A. (2006). Assessment of the Energy Utilization Efficiency in the Turkish Transportation Sector between 2000 and 2020 using Energy and Exergy Analysis Method, Energy Policy, Vol: 34(13), pp. 1611-1618.

Yumurtaci Z., Asmaz E. (2004). Electric Energy Demand of Turkey for the Year 2050, Energy Sources, Vol: 26, pp. 1157-1164.

Ortiz-Arroyo D., Skov M.K., Huynh Q. (2005). Accurate Electricity Load Forecasting with Artificial Neural Networks”, in Proc. CIMCA-IAWTIC, pp. 1-6.

Lu´cia M.L., Minussi C.R., Diva A.P. (2005). Electric load forecasting using a fuzzy ART&ARTMAP neural network, Applied Soft Computing, Vol: 5, pp. 235-244.

Sachdeva S., Singh M., Singh U.P., Arora A.S. (2011). Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission”, Advances in Fuzzy Systems, Vol: 2011, pp. 1-6.

Jang R., Sun C.T., Mizutani E. (1997). Neuro-Fuzzy and Soft Computation, Prentice Hall, New Jersey.

Ghiassi M., Zimbra D.K., Saidane H. (2006). Medium term system load forecasting with a dynamic artificial neural network model. Electric Power Systems Research, Vol: 76, pp. 302-316.

Aslan Y., Yavasca S., Yasar C. (2011). Long Term Electric Peak Load Forecasting Of Kutahya Using Different Approaches, International Journal on Technical and Physical Problems of Engineering, Vol: 3(2), pp. 87-91.

Tasaodian B., Anvarian N., Azadeh A., Saberi M. (2010). An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electricity Consumption Forecasting (2008-2015): A Case Study of the Group of Eight (G8) Industrialized Nations: U.S.A, Canada, Germany, United Kingdom, Japan, France and Italy”, The 11th Asia Pacific Industrial Engineering and Management Systems Conference, pp. 1-12.

Jang J-S.R. (1993). ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on System, Man and Cybernetics, Vol: 23(5), pp. 665-685.

Akdemir B., Oran B., Güneş S., Karaaslan S. (2010). Prediction of cardiac end-systolic and end-diastolic diameters in m-mode values using adaptive neural fuzzy inference system, Expert Systems with Applications, Vol: 37(8), pp. 5720-5727.

Akdemir B., Çetinkaya N. (2012). Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data, Energy Procedia, Vol: 14, pp. 794-799.

Narukawa Y., Murofushi T., Sugeno M. (2000). Regular fuzzy measure and representation of comonotonically additive functional, Fuzzy Sets and Systems, Vol: 112(2), pp. 177-186.

http://www.teias.gov.tr/YayinRapor/apk/projeksiyon/ KAPASITEPROJEKSIYONU2014.pdf

Armstrong J.S. (2001). Principles of forecasting: a handbook for researchers and practitioners”, Chapter 14, Kluwer Academic, Publishers: Norwell, MA. pp. 441-472.

Hyndman R.J. and Koehler A.B. (2006). Another look at measures of forecast accuracy, International Journal of Forecasting, Vol: 22, pp. 679-688.

Akdemir B., Çetinkaya N. (2011). Importance of Holidays for Short Term Load Forecasting Using Adaptive Neural Fuzzy Inference System”, International Conference on Power and Energy Engineering.

Tayman J., Swanson D.A. (1999). On the Validity of MAPE as a Measure of Population Forecast Accuracy, Population Research and Policy Review, Vol: 18(4), pp. 299-322.

Yayar R., Hekim M., Yılmaz V., Bakırcı F. (2011). A comparison of ANFIS and ARIMA Techniques in the Forecasting of Electric Energy Consumption of Tokat Province in Turkey, Journal of Economic and Social Studies, Vol: 1(2), pp. 87-112.

Kandil M.S., El-Debeiky S.M., Hasanien N.E. (2002). Long-Term Load Forecasting for Fast Developing Utility Using a Knowledge-Based Expert System, IEEE Transactions on Power Systems, Vol: 17(2), pp. 491-496.

Maralloo M.N., Koushki A.R., Lucas C., Kalhor A. (2009). Long Term Electrical Load Forecasting via a Neurofuzzy Model, in Proc. of the 14th International CSICC'09, pp. 35-40.

Filik U. B., Gerek O. N., Kurban M. (2011). A novel modelling approach for hourly forecasting of long-term electric energy demand, Energy Conversion and Manag., Vol: 52, pp. 199-211.

Mustafa S.K., Eren O., Mesut G., Turan P. (2012). A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey, Energy Conversion and Management, Vol: 53, pp. 75-83.

Hepbasli A., Ozalp N. (2003). Development of energy efficiency and management implementation in the Turkish industrial sector, Energy Conversion and Management, Vol: 44, pp. 231–249.

A. Abdullah, T. Ramiah Pillai, C. L. Zheng, V. Abaeian, (2015). Intrusion Detection Forecasting Using Time Series for Improving Cyber Defence. International Journal of Intelligent Systems and Applications in Engineering, Vol 3(1), pp. 28-33.

Nivedha R.R., Sreevidya L., Geetha V., Deepa R. (2011). Design of Optimal Power System Stabilizer Using ETAP, International Journal of Power System Operation and Energy Management, Vol: 1(2), pp. 120–123.

Aswani, R., Sakthivel, R. (2014). Power Flow Analysis of 110/11KV Substation Using ETAP”, International Journal of Applied Research and Studies, Vol: 3(1)

Downloads

Published

12.12.2017

How to Cite

Cetinkaya, N. (2017). Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 168–173. https://doi.org/10.18201/ijisae.2017533858

Issue

Section

Research Article