Kısa Dönem Yük Tahmini için Mevsimsel ve Çok Değişkenli Gri Tahmin Modellerinin Uygulanması

Year 2017, , 329 - 338, 11.12.2017

Tuncay Özcan

https://doi.org/10.17093/alphanumeric.359942

Cited By: 2

Abstract

Kısa dönem elektrik yükü tahmini, elektrik piyasasında en önemli operasyonlardan biridir. Elektrik piyasasındaki işletmelerin operasyonlarındaki başarı, yük tahminlerinin doğruluğuna bağlıdır. Bu çalışmada, gün öncesi piyasasında kısa döneli yük tahmini problemi için mevsimsel gri model (SGM), çok değişkenli gri model (GM (1,N)) ve genetik algoritma esaslı gri model olmak üzere üç gri tahmin modeli önerilmiştir. Bu modellerin etkinliği, iki gerçek hayat veri kümesi ile gösterilmiştir. Sayısal sonuçlar, genetik algoritma esaslı gri modeli daha iyi tahmin doğruluğu sağlayarak en etkin gri tahmin modeli olduğunu göstermektedir.

Keywords

Gri Tahmin, Kısa Dönem Yük Tahmini, Genetik Algoritma, Parametre Optimizasyonu

Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting

Abstract

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy.

Keywords

Grey Prediction, Short Term Load Forecasting, Genetic Algorithm, Parameter Optimization

References

• Bahrami, S., Hooshmand, R. A., & Parastegari, M. (2014). Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy(72), 434-442.
• Bianco, V., Manca, O., Nardini, S., & Minea, A. A. (2010). Analysis and forecasting of nonresidential electricity consumption in Romania. Applied Energy, 87(11), 3584-3590.
• Chen, B. J., & Chang, M. W. (2004). Load forecasting using support vector machines: A study on EUNITE competition 2001. IEEE transactions on power systems, 19(4), 1821-1830.
• Chen, C. I., Chen, H. L., & Chen, S. P. (2008). Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM (1, 1). Communications in Nonlinear Science and Numerical Simulation, 13(6), 1194-1204.
• Feng, S. J., Ma, Y. D., Song, Z. L., & Ying, J. (2012). Forecasting the energy consumption of China by the grey prediction model. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 376-389.
• Hoffman, K. C., & Wood, D. O. (1976). Energy system modeling and forecasting. Annual review of energy, 1(1), 423-453.
• Hong, T., Wang, P., & Willis, H. L. (2011). A naïve multiple linear regression benchmark for short term load forecasting. In Power and Energy Society General Meeting (s. 1-6). IEEE.
• Hsu, C. I., & Wen, Y. H. (1998). Improved grey prediction models for the trans‐pacific air passenger market. Transportation planning and Technology, 22(2), 87-107.
• Hsu, L. C. (2009). Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models. Expert systems with applications, 36(4), 7898-7903.
• Huang, Y. F., Zheng, M. C., & Wu, C. H. (2004). Comparison of various different approaches to tourist demand forecasting. Journal of grey system, 7(1), 21-27.
• Jin, M., Zhou, X., Zhang, Z. M., & Tentzeris, M. M. (2012). Short-term power load forecasting using grey correlation contest modeling. Expert Systems with Applications, 39(1), 773-779.
• Ju-Long, D. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294.
• Lee, Y. S., & Tong, L. I. (2011). Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1), 147-152.
• Li, D. C., Chang, C. J., Chen, C. C., & Chen, W. C. (2012). Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case. Omega, 40(6), 767-773.
• Li, G. D., Yamaguchi, D., & Nagai, M. (2006). Application of improved grey prediction model to short term load forecasting. In Proceedings of International Conference on Electrical Engineering , (s. 1-6).
• Niu, D. X., Li, W., Han, Z. H., & Yuan, X. E. (2008). Power Load Forecasting based on Improved Genetic Algorithm–GM (1, 1) Model. In Natural Computation, 2008. ICNC'08 (s. 630-634). IEEE.
• Ou, S. L. (2012). Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm. Computers and electronics in agriculture, 85, 33-39.
• Pai, T. Y., Chiou, R. J., & Wen, H. H. (2008). Evaluating impact level of different factors in environmental impact assessment for incinerator plants using GM (1, N) model. Waste Management, 28(10), 1915-1922.
• Pi, D., Liu, J., & Qin, X. (2010). A grey prediction approach to forecasting energy demand in China. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32(16), 1517-1528.
• Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2001). Applied hybrid grey model to forecast seasonal time series. Technological Forecasting and Social Change, 67(2), 291-302.
• Wang, C. H., & Hsu, L. C. (2008). Using genetic algorithms grey theory to forecast high technology industrial output. Applied Mathematics and Computation, 195(1), 256-263.
• Wang, Y. F. (2002). Predicting stock price using fuzzy grey prediction system. Expert systems with applications, 22(1), 33-38.
• Xia, M., & Wong, W. K. (2014). A seasonal discrete grey forecasting model for fashion retailing. Knowledge-Based Systems, 57, 119-126.
• Yao, A. W., Chi, S. C., & Chen, J. H. (2003). An improved grey-based approach for electricity demand forecasting. Electric Power Systems Research, 67(3), 217-224.
• Zhou, J., Fang, R., Li, Y., Zhang, Y., & Peng, B. (2009). Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization. Applied Mathematics and Computation, 207(2), 292-299.