**7. Conclusions**

(1) Wind speed prediction is of grea<sup>t</sup> significance to the stable operation and operating efficiency of the power system. At the same time, it improves the ability of wind farms to participate in market competition.

(2) The wind speed prediction method based on the maximum Lyapunov exponent and fLsm iterative prediction model was effective. Based on the historical wind speed sequence, this paper calculated the maximum prediction steps, weighted the wind speed data, and established an fLsm iterative prediction model. It can be seen from the MATLAB simulation curve that the model can better predict the wind speed and reflect the change of the sequence, which has certain guiding significance. It can be seen from Section 6 that after the conversion of the power characteristic curve, its regularity was partially destroyed, and the regularity of the obtained wind energy was even weaker, which led to a larger forecast error of wind power. Therefore, the wind speed needs to be predicted first, and then the amount of electricity can be calculated.

(3) In practice, wind speed has strong randomness, and some regions may not have LRD. The wind speed sequence of short-range dependent (SRD) has ye<sup>t</sup> to be studied.

**Author Contributions:** Conceptualization, S.D. and C.C.; Data curation, Y.Y. and H.L.; Formal analysis, W.S. and C.C.; Funding acquisition, W.S.; Investigation, S.D., W.S. and C.C.; Methodology, S.D., W.S. and C.C.; Project administration, W.S. and C.C.; Resources, W.S. and C.C.; Visualization, W.S. and C.C.; Writing–Original draft, S.D.; Writing–Review & editing, W.S. and C.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project was funded by the Natural Science Foundation of Shanghai (Grant No. 14ZR1418500).

**Conflicts of Interest:** The authors declare no conflict of interest.
