Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting 289

Table 4. Comparison between WTVS and TVS model in

forecasting phase.

23/5 Actual

load WTVS TVS[3] 29/6

Actual

load WTVS TVS[3]

21 1633 1620 1723 21 1615 15471519

22 1515 1625 1607 22 1520 16201627

23 1417 1497 1538 23 1475 15291525

24 1293 1419 1433 24 1370 14601475

MAPE 5.8 7.74 5.2 6.0

Table 5. Comparison under different numbers of intervals

in forecasting phase.

Number of intervals

Time

4 5 8 10 16

23/5 6.94 7.23 7.74 8.27 8.18

29/6 4.8 7.28 4.6 5.87 5.43

the forecasting phase under different numbers of inter-

vals. It is shown that the forecast accuracy is influenced

by the length of intervals.

5. Conclusions

In this paper a weighted time-variant slide fuzzy time-

series model for short-term load forecasting is proposed.

The proposed model is tested for forecasting efficacy on

the load of the National Electric Power Company in Jor-

dan. Some of the heuristic knowledge generated by the

WTVS model in the training phase is used to forecast

unknown future values. The experimental results show

that the WTVS model is more accurate than TVS model.

The advantages of the WTVS model are as follows.

1) External factors were considered in WTVS model.

In the data preprocessing phase, the impact of random

factors is weakened by smoothing the historical data. In

the trend training and load forecasting phase, the sea-

sonal factor was introduced into TVS model.

2) The most recent data from the prediction load has

the greater impact. The weighted historical data are con-

sidered in WTVS model.

6. Acknowledgements

The authors would like to thank the anonymous review-

ers and the work was supported by Shanghai Municipal

Natural Science Fund under grant 10ZR1401400 and The

Fundamental Research Funds for the Central Universities

under grant 11D10417 and 11D10402.

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