Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization

光伏系统 水准点(测量) 计算机科学 电力系统 粒子群优化 预测建模 群体智能 人工智能 机器学习 功率(物理) 数据挖掘 工程类 地理 大地测量学 物理 电气工程 量子力学
作者
Yilin Zhou,Jianzhou Wang,Zhiwu Li,Haiyan Lu
出处
期刊:Energy Conversion and Management [Elsevier]
卷期号:267: 115944-115944 被引量:45
标识
DOI:10.1016/j.enconman.2022.115944
摘要

Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models.
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
Clarence应助周小松采纳,获得20
刚刚
Lucas应助Singularity采纳,获得10
1秒前
pw完成签到 ,获得积分10
1秒前
1秒前
gao完成签到,获得积分10
3秒前
NJY发布了新的文献求助10
3秒前
朱凌娇发布了新的文献求助10
4秒前
猴子完成签到,获得积分10
5秒前
嘻yyy完成签到 ,获得积分10
5秒前
6秒前
xl发布了新的文献求助10
6秒前
LZX完成签到 ,获得积分10
6秒前
爆米花应助懂得珍惜采纳,获得10
7秒前
勤劳半青完成签到,获得积分10
7秒前
科研通AI2S应助俭朴的发带采纳,获得10
9秒前
10秒前
gzj完成签到,获得积分10
10秒前
FashionBoy应助愤怒也呵呵采纳,获得10
11秒前
tuanheqi完成签到,获得积分0
13秒前
地球观光客完成签到,获得积分10
14秒前
14秒前
14秒前
17秒前
朱w完成签到,获得积分20
18秒前
揍个大西瓜完成签到,获得积分10
19秒前
中和皇极完成签到,获得积分0
19秒前
李爱国应助哈哈哈哈采纳,获得10
19秒前
zhh发布了新的文献求助10
20秒前
朱w发布了新的文献求助10
22秒前
花海完成签到,获得积分10
23秒前
朱凌娇完成签到,获得积分10
24秒前
科目三应助zhh采纳,获得10
24秒前
669完成签到,获得积分10
25秒前
顾矜应助闲谈落月采纳,获得10
25秒前
25秒前
小白完成签到,获得积分20
25秒前
宋十一完成签到 ,获得积分10
26秒前
愤怒也呵呵完成签到,获得积分10
26秒前
26秒前
江海客完成签到,获得积分10
26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
Handbook of Qualitative Cross-Cultural Research Methods 600
Very-high-order BVD Schemes Using β-variable THINC Method 568
Chen Hansheng: China’s Last Romantic Revolutionary 500
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 3137238
求助须知:如何正确求助?哪些是违规求助? 2788358
关于积分的说明 7785777
捐赠科研通 2444399
什么是DOI,文献DOI怎么找? 1299897
科研通“疑难数据库(出版商)”最低求助积分说明 625650
版权声明 601023