Through The Rain

博客呈现了歌曲《Through The Rain》的歌词,表达了在困境中独自坚强、战胜困难的信念。即便无人相助、感到迷茫,也能凭借自身力量和坚定信念度过难关,鼓励人们不要放弃,勇往直前。

Through The Rain

Written By Mariah Carey And Lionel Cole

Verse 1

When You Get Caught In The Rain
With Nowwhere To Run
When You're Distraught
And In Pain Without Anyone
When You Keep Crying Out To Be Saved
But Nobody Comes
And You Feel So Far Away
That You Just Can't Find Your Way Home
You Can Get There Alone, It's Ok
Once You Say

Chorus

I Can Make It Through The Rain
I Can Stand Up Once Again
On My Own And I Know
That I'm Strong Enough To Mend
And Everytime I Feel Afraid
I Hold Tighter To My Faith
And I Live One More Day
And I Make It Through The Rain

Verse 2

And If You Keep Falling Down
Don't You Dare Give In
You Will Arise Safe And Sound
So Keep Pressing On Steadfastly
And You'll Find What You Need To Prevail
Once You Say

Chorus

I Can Make It Through The Rain
I Can Stand Up Once Again
On My Own And I Know
That I'm Strong Enough To Mend
And Everytime I Feel Afraid
I Hold Tighter To My Faith
And I Live One More Day
And I Make It Through The Rain

Bridge

And When The Wind Blows
And Shadows Grow Close
Don't Be Afraid
There's Nothing You Can't Face
And Should They Tell You
You'll Never Pull Through
Don't Hesitate
Stand Tall And Say

Chorus

I Can Make It Through The Rain
I Can Stand Up Once Again
On My Own And I Know
That I'm Strong Enough To Mend
And Everytime I Feel Afraid
I Hold Tighter To My Faith
And I Live One More Day
And I Make It Through The Rain

Chorus

I Can Make It Through The Rain
And Stand Up Once Again
And I'll Live One More Day, And I
I Can Make It Through The Rain

Ending

Oh Yes You Can
You're Gonna Make It Through The
Rain

【多变量输入超前多步预测】基于CNN-BiLSTM的光伏功率预测研究(Matlab代码实现)内容概要:本文介绍了基于CNN-BiLSTM模型的多变量输入超前多步光伏功率预测方法,并提供了Matlab代码实现。该研究结合卷积神经网络(CNN)强大的特征提取能力与双向长短期记忆网络(BiLSTM)对时间序列前后依赖关系的捕捉能力,构建了一个高效的深度学习预测模型。模型输入包含多个影响光伏发电的气象与环境变量,能够实现对未来多个时间步长的光伏功率进行精确预测,适用于复杂多变的实际应用场景。文中详细阐述了数据预处理、模型结构设计、训练流程及实验验证过程,展示了该方法相较于传统模型在预测精度和稳定性方面的优势。; 适合人群:具备一定机器学习和深度学习基础,熟悉Matlab编程,从事新能源预测、电力系统分析或相关领域研究的研发人员与高校研究生。; 使用场景及目标:①应用于光伏电站功率预测系统,提升电网调度的准确性与稳定性;②为可再生能源并网管理、能量存储规划及电力市场交易提供可靠的数据支持;③作为深度学习在时间序列多步预测中的典型案例,用于科研复现与教学参考。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注数据归一化、CNN特征提取层设计、BiLSTM时序建模及多步预测策略的实现细节,同时可尝试引入更多外部变量或优化网络结构以进一步提升预测性能。
评论 5
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值