Research Article
Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China
Table 4
Predicted value of wind power utilization efficiency in 30 provinces (2020).
| | 2019 | 2020 prediction | Trend | Mean (2013–2019) | Rank | Mean (2013–2020) | Rank | Ranking changes |
| Inner Mongolia | 1.1495 | 1.1832 | Upward | 1.1654 | 1 | 1.1677 | 1 | No change | Shandong | 1.0732 | 1.1375 | Upward | 1.1110 | 2 | 1.1143 | 2 | No change | Fujian | 1.0278 | 0.7501 | Decline | 0.9963 | 3 | 0.9656 | 3 | No change | Yunnan | 1.0832 | 0.9933 | Decline | 0.9514 | 4 | 0.9566 | 4 | No change | Jiangsu | 1.1398 | 1.0021 | Decline | 0.9412 | 5 | 0.9488 | 5 | No change | Hebei | 0.8716 | 0.9793 | Upward | 0.8722 | 6 | 0.8856 | 6 | No change | Ningxia | 0.7006 | 0.8676 | Upward | 0.7818 | 7 | 0.7925 | 8 | Decline | Shanxi | 0.8377 | 0.9086 | Upward | 0.7801 | 8 | 0.7962 | 7 | Upward | Liaoning | 0.7461 | 0.7535 | Upward | 0.7798 | 9 | 0.7765 | 9 | No change | Guangdong | 0.5724 | 0.6018 | Upward | 0.6373 | 10 | 0.6329 | 10 | No change | Xinjiang | 0.6900 | 0.5922 | Decline | 0.5833 | 11 | 0.5844 | 11 | No change | Heilongjiang | 0.5975 | 0.6306 | Upward | 0.5624 | 12 | 0.5709 | 12 | No change | Gansu | 0.6040 | 0.6087 | Upward | 0.5435 | 13 | 0.5516 | 13 | No change | Anhui | 0.5003 | 0.5134 | Upward | 0.4957 | 14 | 0.4979 | 14 | No change | Zhejiang | 0.5502 | 0.4835 | Decline | 0.4778 | 15 | 0.4785 | 17 | Upward | Hubei | 0.6394 | 0.5790 | Decline | 0.4768 | 16 | 0.4896 | 16 | No change | Guizhou | 0.4895 | 0.6202 | Upward | 0.4767 | 17 | 0.4947 | 15 | Decline | Jiling | 0.5141 | 0.5601 | Upward | 0.4659 | 18 | 0.4776 | 18 | No change | Hunan | 0.4152 | 0.5148 | Upward | 0.4395 | 19 | 0.4489 | 19 | No change | Shanghai | 0.4561 | 0.4310 | Decline | 0.4373 | 20 | 0.4365 | 20 | No change | Guangxi | 0.7190 | 0.5208 | Decline | 0.4128 | 21 | 0.4263 | 22 | Decline | Jiangxi | 0.5206 | 0.4983 | Decline | 0.4126 | 22 | 0.4233 | 23 | Decline | Sichuan | 0.7987 | 0.5679 | Decline | 0.4062 | 23 | 0.4264 | 21 | Upward | Shaanxi | 0.4410 | 0.5148 | Upward | 0.4052 | 24 | 0.4189 | 24 | No change | Henan | 0.5386 | 0.5265 | Decline | 0.3932 | 25 | 0.4099 | 25 | No change | Tianjin | 0.3726 | 0.3624 | Decline | 0.3581 | 26 | 0.3586 | 26 | No change | Hainan | 0.3186 | 0.3489 | Upward | 0.3403 | 27 | 0.3414 | 27 | No change | Beijing | 0.2771 | 0.3188 | Upward | 0.2988 | 28 | 0.3013 | 29 | Decline | Qinghai | 0.3646 | 0.4267 | Upward | 0.2916 | 29 | 0.3085 | 28 | Upward | Chongqing | 0.3524 | 0.3539 | Upward | 0.2904 | 30 | 0.2983 | 30 | No change | China | 0.6454 | 0.6383 | Decline | 0.5861 | | 0.5927 | | |
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