Name
Title | ||
|---|---|---|
A switching delayed PSO optimized extreme learning machine for short-term load forecasting. |
Abstract | ||
|---|---|---|
In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of the short-term load forecasting (STLF). In particular, the input weights and biases of ELM are optimized by a new developed SDPSO algorithm, where the delayed information of locally best particle and globally best particle are exploited to update the velocity of particle. By testing the proposed SDPSO-ELM in a comprehensive manner on a tanh function, this approach obtain better generalization performance and can also avoid adding unnecessary hidden nodes and overtraining problems. Moreover, it has shown outstanding performance than other state-of-the-art ELMs. Finally, the proposed SDPSO-ELM algorithm is successfully applied to the STLF of power system. Experiment results demonstrate that the proposed learning algorithm can get better forecasting results in comparison with the radial basis function neural network (RBFNN) algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2017.01.090 | Neurocomputing |
Keywords | Field | DocType |
Short-term load forecasting,Extreme learning machine,Switching delayed particle swarm optimization (SDPSO),Neural network,Time-delay | Extreme learning machine,Radial basis function neural,Wake-sleep algorithm,Electric power system,Load forecasting,Hyperbolic function,Artificial intelligence,Artificial neural network,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
240 | 0925-2312 | 39 |
PageRank | References | Authors |
1.24 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nianyin Zeng | 1 | 384 | 12.14 |
| Hong Zhang | 2 | 276 | 26.98 |
| Weibo Liu | 3 | 520 | 16.88 |
| Jinling Liang | 4 | 1985 | 105.88 |
| Fuad E. Alsaadi | 5 | 1818 | 102.89 |