Name
Abstract | ||
|---|---|---|
In this paper, we apply the Monte Carlo neural network (MCNN), a type of neural network optimized by Monte Carlo algorithm, to electricity load forecast. Meanwhile, deep MCNNs with one, two and three hidden layers are designed. Results have demonstrated that three-layer MCNN improves 70.35% accuracy for 7-week electricity load forecast, compared with traditional neural network. And five-layer MCNN improves 17.24% accuracy for 7-week forecast. This proves that MCNN has great potential in electricity load forecast. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11227-019-02828-3 | The Journal of Supercomputing |
Keywords | DocType | Volume |
Monte Carlo, Neural network, Electricity load forecast, Deep MCNNs | Journal | 76 |
Issue | ISSN | Citations |
8 | 1573-0484 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binbin Yong | 1 | 21 | 5.23 |
| Liang Huang | 2 | 0 | 0.68 |
| Fucun Li | 3 | 6 | 2.52 |
| Jun Shen | 4 | 20 | 8.82 |
| Xin Wang | 5 | 0 | 0.34 |
| Qingguo Zhou | 6 | 103 | 29.48 |