Accelerating Multi-layer Perceptron based short term demand forecasting using Graphics Processing Units

Transmission and Distribution Conference and Exposition: Asia and Pacific, T and D Asia 2009

Volumn , Issue , 2009, Pages

  He, Ting ^a   Dong, Zhao Yang ^b   Meng, Ke ^b   Wang, Hua ^a   Oh, Y T ^c  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^c Korea University of Technology and Education   (South Korea)

Author keywords

Gpu; Load Forecasting; Multi Layer Perceptron; Neural Network

Indexed keywords

COMPUTATIONAL COSTS; DEREGULATED ELECTRICITY MARKET; ELECTRICITY MARKET; GRAPHICS PROCESSING UNIT; LOAD FORECASTING; MULTI LAYER PERCEPTRON; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; POWER SYSTEMS; QUEENSLAND; SHORT TERM LOAD FORECASTING; SHORT-TERM DEMAND FORECASTING;

COMPUTER GRAPHICS EQUIPMENT; COST REDUCTION; DEREGULATION; NEURAL NETWORKS; PROGRAM PROCESSORS;

ELECTRIC LOAD FORECASTING;

EID: 76249087378     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/TD-ASIA.2009.5356813     Document Type: Conference Paper

References (14)

1. rd International IEEE Conference Intelligent System. September 2006
2. L. Xu, Z. Dong and A. Tay, Time Series Forecast with Elman Neural Networks and Genetic Algorithms, invited book chapter, Recent Advances in Simulated Evolution and Learning, edt. K.C. Tan, M. H. Lim, X. Yao and L.P. Wang, World Scientific series, Recent Advances in Simulated Evolution and Learning, Vol.2 Advances in Natural Computation, 2004
3. Z. Xu, Z.Y. Dong and W. Liu, Neural Network Models for Electricity Market Forecasting, invited book chapter, D.H. Wang edt. Neural Networks Applications in Information Technology and Web Engineering, 2004.
4. ZY Dong, "A Neural Network based Framework for Electricity Load and Price Forecasting", Paper 153, IEEE Int. Conf. on Computational Intelligence, Robotics & Autonomous Systems, November 28-30, 2001, Singapore, pp.88 - 93.
5. B. L. Zhang and Z. Y. Dong, "An Adaptive Neural-wavelet Model for Short Term Load Forecasting", International Journal of Electric Power Systems Research. Vol.59 pp. 121-129, 2001.
6. G.Ching Liao, A Novel Particle Swarm optimization Approach Combined with Fuzzy Neural Networks for Short-Term Load Forecasting. Power Engineering Society General Meeting, 2007. IEEE
7. Salama, H.A., El-Gawad, A.F.A., Mahmoud, H.M., Mohamed, E.A., Saker, S.M., Short-term load forecasting investigations of Egyptian. electrical network using ANNs. 42nd Int. Universities Power Engineering Conference, UPEC 2007.4-6 Sept. 2007 Page(s):550-555
8. Senjyu, T., Takara, H.,Uezato, K., Funabashi, T., "One-Hour-Ahead Load Forecasting Using Neural Networks", IEEE Transactions on Power Systems, Feb 2002
9. Lursinsap, C. Kim, J.H., "Parallel learning for back-propagation network in binary field", Proc. IEEE International Sympoisum on Circuits and Systems, Vol 3, 1477-1480, 1991.
10. H. Schabauer, E. Schikuta, T. Weishäupl, "Solving Very Large Traveling Salesman. Problems by SOM Parallelization on Cluster Architectures", Proc PDCAT 2005: 954-958.
11. I. Buck, "GPU Computing: Programming a Massively Parallel Processor", Proceedings of the International Symposium, on Code Generation and Optimization. 17, 2007.
12. K. Levenberg, "A method for the solution of certain non-linear problems in least squares", The Quarterly of Applied Mathematics, vol.2, 1944, pp. 164-168.
13. NVIDIA CUDA Compute Unified Device Architecture, Programming Guide Version 2.1 Beta, Dec. 2008.
14. (Australian) National Electricity Market Management Company Limited (NEMMCO) website, http://www.nemmco.com.au. (from 1 July 2009, AEMO website www.aemco.com.au