少的力量(Power of Less)

Leo Babauta 通过 Twitter 提供每日建议,帮助读者专注于最重要的事情并减少杂事。这些建议包括设立年度目标、有效专注、合理安排时间、改善饮食习惯等。

Leo Babauta is offering a Tip of the Day through Twitter — powerful tips to help you focus on the essential and get to less.

The tips so far:

  1. Don’t make resolutions, create a new habit. It lasts longer. Try the New Year’s Challenge.
  2. Start your New Year with a clear desk. Clear everything off the top of the desk but the essential tools.
  3. Set one major goal for this year. Focus yourself completely into making it happen. Make it a mantra.
  4. Take action TODAY to make your One Goal happen. Even a small action. Tomorrow: repeat.
  5. Schedule time for yourself, right now. Every day if possible, even if it’s just 10 mins.
  6. Practice effective focus. Spend 30 minutes just focusing on one task. Clear distractions. It gets easier.
  7. Realize that you don’t need to respond to email right away-no one expects you to. It can wait! Checking email just twice a day is possible if you let go of the need to respond immediately.
  8. To relax during times of stress or chaos, focus on 10 breaths. Pay attention as each breath comes in, and out.
  9. Limit emails you send to reduce the flow and time spent on email. Try choosing just 5 essential msgs to send. (Or whatever number works best for you.)
  10. Try 30 min patience sessions: don’t get mad or impatient with anyone/anything. It gets easier with practice.
  11. To experience amazing levels of productivity, disconnect from the Internet for an hour.
  12. Learn to be in the moment. When you eat, just eat. You’ll enjoy life more.
  13. Make one small change in your diet at a time. Much easier than making a drastic overhaul of your diet.
  14. Positive public pressure is a huge motivator. Tell as many people as possible about a goal or habit change.
  15. If you spend a lot of time fiddling with your to-do/productivity app, try a simple notebook or text file. Then focus on doing.
【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。
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