Application of Combined Intelligent Algorithm in Load Forecasting

Abstract:

Load forecasting is the foundation of power system planning, accurate load forecasting results can ensure the quality of power supply requirements under the premise of maximum avoid the waste of power grid construction fund, realize the maximization of the social benefits of limited investment. This paper in smart grid environment to load forecast of load signal at the same time, increasing the reliability of the forecast results. The discrete wavelet transform smooth wavelet transform, stationary wavelet transform the redundancy and panning invariability of the time frequency transform, in the process,to avoid the sampling processing signal distortion. In the load forecast this step,use wavelet clustering of data load classification,then use Elman neural network algorithm forecast. The main method is to use wavelet clustering algorithms for load classification. It will greatly enhance the load forecasting results accuracy and dependability.

• Periodical: Advanced Materials Research (Volumes 945-949)
• Pages: 2895-2899
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.945-949.2895
• Online since: June 2014
• Authors: Shi Fang Wang, Hui Cheng Yang
• Keywords: Elman Algorithm, GA, Wave Cluster, Wavelet Transform (WT)

References

[1] Jaszkiewicz A., Comparison of Local Search-based Metaheuristics on the Multiple Objective Knapsack Problem, Foundations of Computing and Decision Sciences, Vol. 26, No. 1, pp.99-120, (2001).

[2] Schaffer J. D., Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp.93-100, (1985).

[3] Jason D. Lohn, William F. Kraus, and Gray L . Haith , Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization, 2002, IEEE.

DOI: 10.1109/cec.2002.1004406

[4] R.S. MOGHRAM I. Analysis and evaluation of five short-term load forecasting technique, IEEE Transactions on PowerSystems, 1989, 4(4).

[5] Srivivas N Deb K. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 1995(Vol. 2 No. 3): 221-248.

DOI: 10.1162/evco.1994.2.3.221

[6] Gross, G; Galiana F.D. Short-term load forecasting, Proceedings of the IEEE, Volume 75, Issue 12.

[7] H. Lee Willis, Spatial Electric Load Forcasting, Routledge, USA.

[8] Zuhairi Baharudin, Autoregressive Models In Short-term Load Forecast; A Comparison Of Ar and Arma.