Journal
NEURAL PROCESSING LETTERS
Volume 52, Issue 2, Pages 1207-1232Publisher
SPRINGER
DOI: 10.1007/s11063-020-10300-0
Keywords
Short-term electric load forecasting; Electricity price forecasting; LSSVM; ELM; GRNN; WOA
Categories
Funding
- National Natural Science Foundation of China [61073193, 61300230]
- Key Science and Technology Foundation of Gansu Province [1102FKDA010]
- Natural Science Foundation of Gansu Province [1107RJZA188]
- Science and Technology Support Program of Gansu Province [1104GKCA037]
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Stable electric load forecasting plays a significant role in power system operation and grid management. Improving the accuracy of electric load forecasting is not only a hot topic for energy managers and researchers of the power system, but also a fair challenging and difficult task due to its complex nonlinearity characteristics. This paper proposes a new combination model, which uses the least squares support vector machine, extreme learning machine, and generalized regression neural network to predict the electric load in New South Wales, Australia. In addition, the model employs a heuristic algorithm-whale optimization algorithm to optimize the weight coefficient. To verify the usability and generalization ability of the model, this paper also applies the proposed combined model to electricity price forecasting and compares it with the benchmark method. The experimental results demonstrate that the combined model not only can get accurate results for short-term electric load forecasting, but also achieves fine accuracy for the same period of electricity price forecasting.
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