Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine
- 61 Downloads
Short-term electricity price forecasting in deregulated electricity markets has been studied extensively in recent years but without significant reduction in price forecasting errors. Also demand-side management and short-term scheduling operations in smart grids do not require strictly very accurate forecast and can be executed with certain practical price thresholds. This paper, therefore, presents a multikernel extreme learning machine (MKELM) for both short-term electricity price forecasting and classification according to some prespecified price thresholds. The kernel ELM does not require the hidden layer mapping function to be known and produces robust prediction and classification in comparison with the conventional ELM using random weights between the input and hidden layers. Further in the MKELM formulation, the linear combination of the weighted kernels is optimized using vaporization precipitation-based water cycle algorithm (WCA) to produce significantly accurate electricity price prediction and classification. The combination of MKELM and WCA is named as WCA-MKELM in this work. To validate the effectiveness of the proposed approach, three electricity markets, namely PJM, Ontario and New South Wales, are considered for electricity price forecasting and classification producing fairly accurate results.
KeywordsElectricity price forecasting and classification Extreme learning machine Kernel extreme learning machine Kernel functions Price thresholds Mutated water cycle algorithm
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest for this paper.
- 8.Singh SN (2008) Electric power generation, transmission and distribution, 2nd edn. Prentice-Hall of India, Upper Saddle RiverGoogle Scholar
- 23.Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), vol 2, Budapest, Hungary, 25–29 July 2004, pp 985–990Google Scholar
- 32.Wang D, Huang G-B (2005) Protein sequence classification using extreme learning machine. In: Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN’05, vol 3. IEEE, pp 1406–1411Google Scholar
- 36.Huang G-B, Siew C-K (2005) Extreme learning machine with randomly assigned RBF kernels. Int J Inf Technol 11(1):16–24Google Scholar
- 39.Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of 2004 IEEE international joint conference on neural networks, vol 2. IEEE, pp 985–990Google Scholar
- 43.PJM Electricity Market Data. [Online]. http://www.pjm.com. Accessed 5 Apr 2017
- 44.Ontario Electricity Market Data. [Online]. http://www.ieso.ca. Accessed 5 Apr 2017
- 45.NSW Electricity Market Data. [Online]. http://www.aemo.com.au. Accessed 5 Apr 2017