Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

风力发电 可再生能源 风电预测 计算机科学 发电 风速 电力系统 功率(物理) 气象学 计量经济学 环境科学 工程类 地理 数学 物理 量子力学 电气工程
作者
Aodi Sui,Wuyong Qian
出处
期刊:Rairo-operations Research [EDP Sciences]
卷期号:55 (5): 3049-3072 被引量:7
标识
DOI:10.1051/ro/2021136
摘要

Renewable energy represented by wind energy plays an increasingly important role in China’s national energy system. The accurate prediction of wind power generation is of great significance to China’s energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China’s wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China’s energy-saving and emission reduction targets.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
宁静致远QY完成签到,获得积分10
1秒前
搞不动科研完成签到,获得积分10
1秒前
kbj完成签到,获得积分10
2秒前
鲸鱼完成签到,获得积分10
3秒前
3秒前
虾条完成签到 ,获得积分10
3秒前
优雅毛豆完成签到,获得积分20
4秒前
甜蜜水蜜桃完成签到 ,获得积分10
4秒前
协和_子鱼完成签到,获得积分0
4秒前
4秒前
负责的寒梅完成签到 ,获得积分10
7秒前
8秒前
8秒前
Lyubb完成签到,获得积分10
9秒前
青衣北风发布了新的文献求助10
9秒前
涂惠芳发布了新的文献求助10
9秒前
隐形曼青应助liuzengzhang666采纳,获得10
10秒前
10秒前
彩色的湘完成签到,获得积分10
10秒前
yellow完成签到,获得积分10
10秒前
hsy309完成签到,获得积分10
11秒前
wdwd发布了新的文献求助10
11秒前
搜集达人应助小杨采纳,获得10
12秒前
青衣北风完成签到,获得积分10
14秒前
wss发布了新的文献求助10
14秒前
15秒前
路在脚下完成签到 ,获得积分10
15秒前
汉弗里戴维完成签到,获得积分10
16秒前
笨笨的怜南完成签到,获得积分10
17秒前
cavendipeng完成签到,获得积分10
17秒前
今后应助cmc12314采纳,获得10
17秒前
芋圆完成签到,获得积分10
17秒前
无私的颤完成签到,获得积分10
17秒前
fabian完成签到,获得积分10
18秒前
18秒前
妮妮完成签到,获得积分10
19秒前
浓浓的淡淡完成签到 ,获得积分10
19秒前
YMY完成签到,获得积分20
20秒前
奋斗人雄完成签到,获得积分10
20秒前
wdwd完成签到,获得积分10
20秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Le dégorgement réflexe des Acridiens 800
Defense against predation 800
A Dissection Guide & Atlas to the Rabbit 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3134083
求助须知:如何正确求助?哪些是违规求助? 2784918
关于积分的说明 7769341
捐赠科研通 2440444
什么是DOI,文献DOI怎么找? 1297415
科研通“疑难数据库(出版商)”最低求助积分说明 624959
版权声明 600792